TerraMosaic Daily Digest: August 5, 2026
Daily Summary
Fire reorganized sediment supply before the first post-fire debris flows developed in the Hengduan Mountains. Following the March 2024 Yajiang Fire, 506 debris-flow events occurred during the same year; in the Baima catchment, wind and gravity transferred 93.8% of measured ash into channels within 11 days, and loose ash-rich sediment supplied 86.6% of the solid material in the earliest flows. Repeat debris flows in the Upper Colorado River Basin likewise interrupted channel recovery by reloading a multi-year sediment wave. At longer timescales, Holocene exposure ages from the Rwenzori Mountains tie episodic rockfall to distinct temperature regimes, while reconstruction of the 2024 Platteikogel failure shows that ice-apron loss promoted instability through interacting permafrost warming, water-pressure buildup and unloading rather than through ice weakening alone.
Slope warning is moving from static terrain classes toward time-dependent estimates of failure state and consequence. Across more than 91,000 km² of Austria and South Tyrol, an interpretable spatial framework combines meteorological, geo-environmental and exposure variables to estimate daily impacts from slides, flows and falls; slide and flow models were promising, whereas weather-driven warning remained less suitable for falls. Weakly supervised susceptibility mapping in northern Noto reduced label uncertainty and improved recall against an independent 2024 inventory, while Sentinel-1 observations at Njintout identified sustained motion four and a half months before failure, albeit near the method's sensitivity limit and without ground validation. Centrifuge tests show that cyclic fine-particle migration can clog liquefaction-mitigation stone columns. At basin scale, stochastic ground-motion simulation and two-dimensional site-response analysis delineate severe liquefaction-potential zones around Dhaka, Sylhet, Mymensingh and Chittagong, while rainfall-seismic experiments and InSAR acceleration indices connect observed deformation to progressive mechanical change.
Probabilistic forecasting is becoming a primary hazard product rather than an uncertainty appended to a deterministic map. A conditional diffusion model reproduces the declining post-earthquake uncertainty of the 2011 Tohoku-oki tsunami while forecasting inundation depth and extent. WeatherNext Cyclones generates 15-day global ensembles of track, intensity and wind radii, with up to 1,000 members and an average lead-time advantage of at least one day over leading operational models for 2023-2025 storms. TC-SAR-Coast organizes 3,128 Sentinel-1 scenes into 277 cyclone events, and SPIN couples NeuralGCM tracks to a dynamical intensity model, creating complementary observation and simulation resources for coastal and compound cyclone risk. At building scale, a 604-storm ensemble shows that compound wind, inland-flood and surge records are rare but six to nine times more severe than single-hazard records, with more than half of worst-case losses falling on uninsured homes. Event-partitioned Bayesian classification of infrasound adds calibrated epistemic and aleatoric uncertainty when separating volcanic, seismic, explosive and atmospheric sources.
Key Trends
Five methodological shifts connect rapidly changing slope state, consequence-based warning, calibrated ensembles, process-aware models and event-structured evidence.
- Rapid Material Transfer Is Becoming an Explicit Hazard State: Post-fire ash storage, repeated debris-flow sediment loading, permafrost degradation and weathering are represented as evolving controls on slope response rather than fixed susceptibility factors.
- Warning Models Are Extending from Hazard to Impact: Daily mass-movement potential is combined with exposure and runout context, while building-scale cyclone loss, infrastructure restoration and disaster-chain models propagate physical disruption into consequences that can support operational decisions.
- Calibration and Ensembles Are Entering the Forecast Target: Conditional diffusion, large cyclone ensembles, pixel-wise flood-map uncertainty, repeated model comparison and calibrated Bayesian infrasound classification estimate distributions, confidence or performance stability instead of reporting a single unqualified prediction.
- Mechanics and Learned Models Are Being Coupled at Failure-Relevant Scales: Rainfall-seismic tests, strain-dependent constitutive analysis, time-continuous block contact, InSAR change-point detection and graph-based or foundation-model methods preserve process or geometry that purely empirical classifiers omit.
- Event-Structured Data Are Replacing Isolated Products: Cyclone SAR scenes, pre-failure displacement histories and source-path-endpoint runout records retain provenance and temporal context, enabling hazard models to be tested across complete events rather than selected snapshots.
Selected Papers
The leading studies resolve how fire rapidly preloads channels with mobile sediment, how daily weather and exposure can be translated into mass-movement impacts, and how probabilistic ensembles can replace false precision in tsunami and cyclone forecasts. Thermal reconstruction of an alpine rock-slope failure adds a complementary mechanistic result: permafrost warming mattered, but water pressure and unloading were needed to explain detachment.
1. Early post‐fire debris‐flow initiation following the March 2024 Yajiang Fire, Sichuan, China
Core Problem: The sediment source and triggering sequence of the first debris flows after wildfire remain poorly constrained, limiting emergency recognition of newly hazardous burned catchments.
Key Innovation: Field surveys, UAV mapping and sediment analyses show that wind and gravity stored 93.8% of measured ash in channels within 11 days; rainfall then mobilized this dry-ravel reservoir, which supplied 86.6% of debris-flow solids in the Baima catchment.
2. Dynamic spatial modelling of mass movement impacts for large areas: a data-driven framework for impact-based early warning
Core Problem: Rainfall thresholds alone do not represent where mass movements will intersect exposed assets or how daily impact potential changes across a large mountain region.
Key Innovation: An interpretable GAMM framework integrates triggering, preparatory, predisposing, runout and exposure variables over more than 91,000 km², producing daily impact estimates for slides, flows and falls while revealing weaker short-term predictability for falls.
3. Real-time probabilistic tsunami forecasting via generative AI
Core Problem: Deterministic inundation boundaries can imply false safety during near-field tsunamis because source uncertainty is large immediately after rupture.
Key Innovation: A conditional diffusion model generates calibrated inundation ensembles and, for the 2011 Tohoku-oki event, tracks the decline in post-earthquake uncertainty while predicting inundation depth and extent.
4. Operational Tropical Cyclone Forecasting with AI
Core Problem: Operational tropical-cyclone guidance must resolve track, intensity, size and rare outcomes far enough ahead to support warnings, but conventional ensembles are costly and limited in size.
Key Innovation: WeatherNext Cyclones produces 15-day global forecasts with up to 1,000 ensemble members; evaluation on 2023-2025 storms reports an average lead-time advantage of at least one day over leading operational models for track, intensity and wind radii.
5. How ice apron loss and permafrost degradation promoted the Platteikogel rock slope failure: a thermo-mechanical reconstruction
Core Problem: The coupled roles of ice-apron retreat, permafrost warming, groundwater pressure and unloading in high-alpine rock-slope failure are difficult to separate.
Key Innovation: A multidecadal thermal reconstruction and mechanical stability analysis of the 50,000 m³ Platteikogel failure show that warming-driven ice-strength loss alone is insufficient and identify water-pressure buildup and rockfall unloading as superimposed destabilizing processes.
6. Temperature fluctuations controlled episodic rockfall activity in the Rwenzori Mountains (Uganda) during the Holocene
Core Problem: Controls on long-term rockfall activity in warm, humid tropical mountains remain uncertain because dated records spanning multiple climatic regimes are rare.
Key Innovation: Beryllium-10 exposure ages from seven Rwenzori deposits resolve three Holocene rockfall episodes and link early failures to deglaciation and freeze-thaw weathering, with later episodes associated with warmer conditions and enhanced chemical and biological weathering.
7. An Interpretable Uncertainty-Aware Framework for Landslide Susceptibility Mapping Based on Weak Supervision and Probabilistic Inference
Core Problem: Landslide inventories provide positives but not verified stable negatives, so susceptibility maps inherit uncertainty from incomplete labels, scale and model choice.
Key Innovation: Weakly supervised negative construction, joint SHAP-PFI interpretation and ensemble probabilistic inference produce susceptibility together with predictive and model uncertainty; independent-event recall improved while both uncertainty measures declined in northern Noto.
8. Landslide Susceptibility Evaluation Based on Deep Learning and Imbalanced Sampling at Multi-Scale
Core Problem: Susceptibility performance depends jointly on map resolution, landslide-to-non-landslide sampling and architecture, yet these choices are often optimized separately.
Key Innovation: Fifty-four model configurations across three resolutions, three sampling ratios and six algorithms show that a 12.5 m ResNet with a 1:3 ratio gives the preferred balance of AUC, Kappa agreement and spatial zoning efficiency.
9. Beyond the initial impact: Channel evolution following wildfire and debris flows in a mountain stream of the Upper Colorado River Basin
Core Problem: Channel recovery after wildfire is poorly constrained when later storms reactivate hillslopes and deliver new debris-flow pulses.
Key Innovation: Repeated field mapping, cross sections, instrumentation and two-dimensional hydro-morphodynamic modelling show a multi-year sediment-wave recovery punctuated by renewed loading three years after fire and two years after the initial debris flows.
10. Investigation on deformation and failure characteristics of inclined-vertical pile reinforced soft-hard interbedded slope
Core Problem: Conventional vertical anti-slide piles can concentrate deformation boundaries and bending demand in soft-hard interbedded slopes.
Key Innovation: Nine centrifuge tests show that appropriately spaced inclined-vertical piles reduce fitted maximum bending moment by about 30-50% and maximum soil displacement by about 20-30%, while promoting a more uniform deformation field and continuous arching between piles.
11. Accounting for Climate Uncertainty in Landslide Hazard Assessment: A Quantitative Weighting Framework for Meizhou City, South China
Core Problem: Landslide assessments rarely quantify uncertainty in the relative importance of multiple climate-related triggers for different failure types and slope components.
Key Innovation: An adaptive fuzzy analytic hierarchy assigns expertand observation-informed weights in Meizhou, identifying rainfall as the dominant overall factor and resolving distinct trigger weights for shallow slides, rockfalls and deep rock landslides.
12. Landslide hazard zonation using heuristic and machine learning approaches for steep terrains
Core Problem: Heuristic landslide zonation may not capture nonlinear interactions among terrain, rainfall, geology and human disturbance in steep terrain.
Key Innovation: A comparison using 509 landslides and 509 non-landslide locations finds XGBoost more discriminating than weighted overlay and GBM, with SHAP attributing most influence to slope, rainfall, lithology, roads and lineament density.
13. Progressive Failure and Damage Mechanism of Bedrock-Overburden Slopes under Sequential Rainfall-Seismic Action
Core Problem: Antecedent rainfall changes how bedrock-overburden slopes accumulate and localize damage under subsequent earthquake loading.
Key Innovation: Large-scale shaking-table tests combine displacement residual ratios with strain-field mapping, resolving moisture weakening, dynamic driving and damage coalescence and identifying a marked nonlinear transition at peak ground acceleration of 0.4 g or greater.
14. Sentinel-1 SBAS-InSAR detection of pre-failure ground motion at the 2025 Njintout landslide, central Cameroon Volcanic Line.
Core Problem: Pre-failure motion in central Cameroon is largely unmonitored, leaving the detectability and timing of slow acceleration before collapse uncertain.
Key Innovation: SBAS-InSAR from 36 Sentinel-1 acquisitions identifies sustained motion beginning about four and a half months before the 2025 Njintout failure, while explicitly limiting the result to a near-sensitivity detection without ground validation.
15. Gas migration and slope instability in the Danube Fan: insights from integrated OBS-MCS seismic analysis
Core Problem: The relative contributions of gas migration, gas hydrate and weak deltaic sediment to submarine slope instability in the Danube Fan are poorly resolved.
Key Innovation: Joint OBS and multichannel seismic velocity imaging maps weak, water-saturated deposits and widespread gas pathways; sparse pore-filling hydrate lacks a shear-wave signature and is therefore unlikely to provide substantial cementation.
16. Seismic Hazard on the Main Himalayan Thrust from Physics-Based Earthquake Simulations
Core Problem: Himalayan seismic hazard is difficult to estimate from the short earthquake record and simplified assumptions about rupture across heterogeneous coupling zones.
Key Innovation: A 10,000-year RSQSim catalogue embeds geodetic fault geometry and slip rate, reproduces observed rupture characteristics and shows low-coupling zones repeatedly acting as barriers that limit simulated magnitude to Mw 8.9.
17. Maximum fluid-induced earthquake magnitude shifts from injected volume to stress control
Core Problem: Volume-based limits underestimate some injection-induced earthquakes because rupture can escape the fluid-pressurized zone under high background stress.
Key Innovation: Large-scale experiments and finite-element simulations resolve a critical transition from volume-controlled, self-arrested rupture to stress-controlled runaway rupture, linking the operative regime to maximum induced magnitude.
18. Probabilistic seismic hazard and historical scenario-based assessment of liquefaction potential in the Bengal Basin for liquefaction mitigation strategies
Core Problem: Regional liquefaction mitigation requires spatially consistent estimates of shaking, site amplification, ground-failure probability and the soil improvement needed to suppress triggering.
Key Innovation: Finite-fault stochastic simulations and two-dimensional site-response analysis map 475-year surface peak ground acceleration of 0.09-1.40 g across the Bengal Basin, classify liquefaction severity and back-calculate target SPT N-values for susceptible zones.
19. Tropical cyclone hazards and economic losses in coastal North Carolina
Core Problem: Tropical-cyclone risk models commonly separate wind, inland flooding and storm surge, leaving the severity and insurance implications of compound building-level exposure poorly quantified.
Key Innovation: A 604-storm synthetic ensemble combines 250 m hazard fields with 575,453 homes; compound records account for only 0.19% of building-storm cases but are six to nine times more severe, contribute 9.4% of ensemble losses and concentrate worst-case losses among uninsured properties.
20. Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction
Core Problem: Post-wildfire debris-flow prediction is constrained by small, imbalanced datasets, overlapping classes and limited evidence about which machine-learning models remain reliable under those conditions.
Key Innovation: Repeated cross-validation across 15 classifiers identifies TabPFN as the strongest model with a 0.637 threat score; SHAP analysis isolates rainfall as the dominant predictor, and synthetic augmentation improves every tested model except the convolutional network.
21. LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching
Core Problem: Global remote-sensing image pairs can differ sharply in time, viewpoint, resolution and land cover, creating large offsets and unmatchable regions that defeat direct dense correspondence.
Key Innovation: LoRetta separates matchable-area localization from residual registration and is trained with the LEVIR-GM benchmark of 103,000 aligned and 827,000 augmented pairs across six continents, improving accuracy while reducing inference latency relative to the strongest reported baseline.
22. Drainage Performance Degradation of Stone Columns Caused by Fine-Particle Migration during Shaking in Centrifuge Model Test
Core Problem: Stone columns mitigate liquefaction by dissipating excess pore pressure, but cyclic loading can mobilize fines and progressively obstruct their drainage paths.
Key Innovation: Dynamic centrifuge testing with transparent soil and seepage-consolidation back-analysis directly resolves depth-dependent clogging, showing that particle migration rather than densification dominates the loss of drainage performance under shaking.
23. Hydrodynamic characteristics and wave overtopping prediction of mangrove-dike hybrid coastal protection systems
Core Problem: However, existing research often treats mangroves and dikes as independent components.
Key Innovation: The study conducted a series of irregular wave laboratory experiments. Results indicate that within the tested range, the mean overtopping discharge decreases as the mangrove-dike distance decreases, which may be related to low-frequency motions.
24. TC-SAR-Coast: an event-based Sentinel-1 SAR dataset with high-resolution wind fields for coastal tropical cyclones
Core Problem: Coastal cyclone SAR wind fields are distributed as disconnected scenes, obscuring storm context, provenance, quality and nearshore exposure.
Key Innovation: TC-SAR-Coast organizes 3,128 Sentinel-1 scenes into 277 cyclone events with traceable processing layers and approximately 1 km wind products validated against aircraft, buoy and satellite observations.
25. SPIN (v1.0): A spontaneous synthetic tropical cyclone model empowered by NeuralGCM for hazard assessment
Core Problem: Statistical synthetic-cyclone models often lose intra-month variability, environmental context and the dynamical organization of clustered storms.
Key Innovation: SPIN uses NeuralGCM to generate tracks and a dynamical model for intensity, reproducing key climatological and landfall statistics while representing multiple-cyclone events as dynamically organized hazards.
26. Turbulent seismoacoustic imprints during a hurricane landfall
Core Problem: Hurricane evolution is affected by turbulence in the hurricane boundary layer (HBL), which is typically measured using aircraft flights and towers.
Key Innovation: The authors identified contributions of HBL turbulence in infrasound pressure and seismic displacement, validating our interpretation by combining large-eddy simulation, calibrated with meteorological data, with quasi-static elastic deformation modeling.
27. Observational and modeling perspectives on seismo-ionospheric electromagnetic anomalies potentially associated with the 2022 Mexico earthquake
Core Problem: The study investigates potential anomalous electromagnetic signals and their characteristics during the 2022 Mexico M7.6 earthquake by integrating the satellite data with a lithosphere-atmosphere-ionosphere (LAI) coupling propagation model for extremely low frequency (ELF) electromagnetic waves.
Key Innovation: Satellite observations and wave vector analysis demonstrate that a significant upward-propagating ELF electromagnetic radiation anomaly emerged south of the epicentral region eight days before the earthquake, with signals concentrated in the 200−500 Hz frequency range.
28. Locally Assembled, Cost-Effective Creepmeters for Monitoring Aseismic Creep Displacement Along the West Valley Fault (Philippines)
Core Problem: Commercial instruments and data-loggers can leave active-fault creep poorly observed where monitoring budgets and maintenance access are limited.
Key Innovation: Locally assembled LVDT and ultrasonic creepmeters, paired with an Arduino rain gauge, resolve short-term slip and abrupt displacement on the West Valley Fault and attribute localized accelerated creep primarily to groundwater extraction with seasonal precipitation modulation.
29. A Hybrid Multicriteria Index for Assessing Documentary-Methodological Robustness in Flood Mapping: Integrating Documentary Evidence, Entropy-Based Weighting, Remote Sensing, DEMs, Hydrological Data, and Statistical Validation
Core Problem: However, no unified evidence-based framework is currently available for systematically comparing the documentary-methodological support of these alternatives.
Key Innovation: The study proposes a hybrid multicriteria index for assessing the documentary-methodological robustness of flood-mapping methodologies integrating remote sensing, DEMs, hydrological/hydraulic information, and statistical validation methods.
30. A Persistent Scatterer Interferometry-Based Parametric Framework for Characterizing Pre-, Co-, and Post-Seismic Surface Deformation: Application to the 2025 Dingri Earthquake (Southern Tibet)
Core Problem: However, most satellite-based investigations of large earthquakes remain focused on coseismic interferograms, source inversions, and short post-seismic observation windows.
Key Innovation: In this study, we propose a PSI-based parametric approach that, given a Persistent Scatterer (PS) time series, uses a piecewise linear regression with an imposed coseismic step at the earthquake origin time to estimate the pre-event line-of-sight (LOS) velocity, the coseismic displacement step, and the post-event LOS velocity using ascending and descending satellite observations.
31. A Data-Driven InSAR Failure-Risk Index for Early Warning of Mining Infrastructure Instability: The Çöpler Case Study, İliç, Türkiye
Core Problem: Mining deformation is commonly described by displacement or velocity without standardized thresholds that separate consolidation from progressive instability.
Key Innovation: A normalized InSAR Failure-Risk Index combines displacement, velocity and acceleration; retrospective change-point analysis at the Copler mine identifies an acceleration regime shift around 2020, roughly four years before collapse.
32. LDM-PUNet: A Lightweight Network for Denoising and Phase Unwrapping SAR Interferograms in Mining Deformation Monitoring
Core Problem: Interferometric synthetic aperture radar (InSAR) enables large-scale, all-weather, day-and-night monitoring of surface deformation, but phase unwrapping remains challenging in mining areas with large-gradient deformation.
Key Innovation: To address this problem, we propose a lightweight dilated multi-path phase unwrapping network, LDM-PUNet, for joint interferogram denoising and phase unwrapping in low-coherence mining environments.
33. Mechanism-guided analysis of tunnel construction-induced surface subsidence based on InSAR monitoring
Core Problem: Urban InSAR time series mix construction settlement with background deformation, so raw line-of-sight motion cannot be interpreted directly as tunnel response.
Key Innovation: A mechanism-guided decomposition produces separate cross-sectional and longitudinal products, recovering injected double-trough settlement geometry and construction progress while attributing along-corridor variability to burial and overburden conditions.
34. Managing squeezing rock mass with TBM data analysis: rail link Rishikesh – Karnaprayag (India)
Core Problem: Shielded tunnel-boring machines can become trapped in squeezing rock, yet operators rarely have continuous deformation measurements linked to an explicit risk model.
Key Innovation: Continuous shield-gap measurements are combined with advance speed, deformation rate, shield geometry, tunnel seismic prediction and supervised learning in a near-real-time squeezing-risk system tested along a 14.58 km Himalayan tunnel.
35. The algebraic deduction method (ADM) for contact detection of rock blocks: a time-continuous strategy beyond time-intermittent contact detection
Core Problem: Time-intermittent contact detection can miss collisions between numerical time steps and propagate random errors through discontinuum rockfall simulations.
Key Innovation: The algebraic deduction method solves block contact continuously in time; laboratory collisions and an engineering-slope case show stable contact handling, energy evolution and predetermined outcomes at near machine precision.
36. Diverging hydrometeorological drivers of flashier floods in a warming climate
Core Problem: Whether warming makes floods flashier is unresolved because increasing peak magnitude and shortening onset time have different regional controls and warning implications.
Key Innovation: Analysis of 741 basins over four decades finds increasing flashiness in nearly one-third, driven mainly by larger peaks in North America and shorter onset times in much of Europe.
37. Strain-dependent slope stability analysis based on an anisotropic hypoplastic model
Core Problem: The study proposes a novel method allowing the use of advanced constitutive model to evaluate the slope stability.
Key Innovation: Based on the proposed method, the influence of fabric anisotropy on the slope stability is investigated.
38. Weathering-Induced Strength Degradation and Hydro-Mechanical Controls on the Stability of Multilayered Tropical Rock Slopes
Core Problem: Tropical environments accelerate rock weathering, which can significantly reduce the stability of mineralized slopes.
Key Innovation: The study investigates the influence of weathering and groundwater conditions on the stability of a strong–weak–strong multilayered rock slope hosting an iron deposit in the Philippines, where intense rainfall and highly fractured rock masses are common. Results indicate that when the middle weak layer is thin, a 50% weathering intensity still allows a stable multilayered slope at an overall slope angle (OSA) of 60°.
39. Complete Mining-Induced Subsidence Basin Reconstruction via Improved RIME-Based Probability Integral Parameter Inversion and SBAS-InSAR Residual Fusion
Core Problem: Large-gradient mining subsidence is difficult to reconstruct completely using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), because decorrelation and phase-unwrapping errors underestimate central subsidence, whereas the probability integral method (PIM) is sensitive to parameter inversion accuracy.
Key Innovation: The study proposes a basin-reconstruction framework combining PIM parameter inversion based on an improved rime optimization algorithm (RIME) with SBAS-InSAR residual fusion.
40. A lightweight automatic workflow for expanding volcano-tectonic seismic catalogs: application to Villarrica Volcano, Chile
Core Problem: However, most operational workflows depend on expert-driven manual analysis, which is labor-intensive, hard to scale, and tends to miss the smallest VT events (around M L 1-2) during high-activity periods.
Key Innovation: The authors present a light weight, fully automatic, and modular workflow for the detection, association, and location of VT seismicity, built on open-source components and designed to run on standard observatory infrastructure without manual intervention. Forwell-constrained events, automatic and manual locations agree closely.
41. Surface roughness dating for lava flows: a new remote sensing geochronometer for volcanic hazard assessment
Core Problem: Regional geologic histories are key to mitigating the impact of geologic hazards on nearby communities, but standard geochronology remains costly and labor-intensive.
Key Innovation: Here, we evaluate if changes in lava flow roughness can be reliably quantified and used as a proxy for lava flow age. Results demonstrate that surface roughness decreases systematically with age and is best described by an exponential decay function ( R-squared = 0.96), with a well-constrained surface roughness decay constant corresponding to a characteristic smoothing timescale of ~6 ka.
42. Eruption source parameters for a Canadian mafic explosive eruption: an example from Sii Aks (Tseax) volcano
Core Problem: Observations from tephra fall deposits are needed to constrain input parameters used to model tephra fall hazard, but this is challenging for mafic Canadian volcanoes, which are often in remote locations.
Key Innovation: Here, we present eruption source parameters (ESPs) for the approximately 1700 CE eruption of Sii Aks (Tseax) volcano, British Columbia, based on laboratory analysis of field samples, Indigenous knowledge and tephra dispersion modelling.
43. Physics Based Broadband Ground Motion Synthetics of Deadliest 1993 MW6.2 Latur (India) Earthquake to Elucidate Physical Phenomenon Behind Various Unresolved Issues
Core Problem: Summary The paper presents the physics-based broadband (0-10 Hz) near-fault ground motion synthetics of the 1993 (Mw6.2) Latur (INDIA) earthquake to elucidate the unresolved issues related to this deadliest stable continental region earthquake worldwide at that time.
Key Innovation: Based on the statistical analysis of simulated and reported intensities, we finalized the appropriate rupture dimension, slip-pattern, focal-position and maximum intensity for the Latur earthquake.
44. Risk Assessment of Post-Earthquake Gas Explosion Disaster Chains in High-Rise Residential Buildings: A Fuzzy Bayesian and Complex Network Approach
Core Problem: To address the challenges in risk prevention and control of post-earthquake gas explosions in high-rise buildings and the deficiencies of traditional methods in handling uncertainty, this paper conducts a risk evolution analysis from the perspectives of fuzzy Bayesian networks (FBNs) and complex network (CN) theory.
Key Innovation: First, based on comprehensive risk factor identification, an earthquake-gas explosion disaster chain evolution model was constructed. The research results indicate that gas overrun (M2) is the node with the highest comprehensive importance, while sensitivity analysis further confirms that it remains the most critical controllable node for interrupting the disaster chain.
45. A comprehensive geographic analysis of disaster risk in Costa Rica
Core Problem: Costa Rica's evidence on volcanic, seismic, landslide, flood, drought and erosion risk is dispersed across hazard-specific studies and historical records.
Key Innovation: A national disaster profile combines statistical analysis with a broad literature synthesis, showing that geophysical disasters were deadliest, hydrometeorological disasters caused the greatest damage and exposure, and population and social development predicted event frequency.
46. Comprehensive flood risk evaluation with climate change study for the Bagmati River Basin in Nepal
Core Problem: In recent times, the Bagmati River Basin has faced flooding as its most significant environmental challenge.
Key Innovation: The study aims to develop a flood risk map that incorporates climate change projections using the EC-Earth 3 model for the SSP 2-4.5 and SSP 5-8.5 scenarios. The flood hazard map is generated using HEC-RAS with projected future precipitation data while model performance indicates strong predictive skill using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R-squared ), and Percent Bias (PBIAS) for calibration and validation and model selection.
47. Source–path–endpoint geohazard-chain runout prediction data for southeastern Tibet
Core Problem: Regional geohazard-chain models require linked source, path and endpoint observations, but most inventories record only isolated event locations.
Key Innovation: The released southeastern Tibet dataset connects 591 interpreted events to 267,045 path-step records, regional source predictions and Monte Carlo and random-seed uncertainty outputs for reproducible endpoint modelling.
48. Bayesian Convolutional Neural Networks for Uncertainty-Aware Classification of Infrasound Events
Core Problem: Infrasound monitoring must distinguish hazardous natural events from explosions and other sources despite small, imbalanced datasets and uncertain predictions.
Key Innovation: Bayesian versions of three convolutional networks quantify epistemic and aleatoric uncertainty on event-partitioned records of volcanic eruptions, earthquakes, explosions, launches and lightning; the best model reaches 99.14% accuracy and improves calibration over deterministic and class-weighted baselines.
49. Ensemble Multi-Criteria Flood Susceptibility Modelling with Spatial Uncertainty Quantification: A Provincial-Scale Application in KwaZulu-Natal, South Africa
Core Problem: Province-scale flood susceptibility methods can produce divergent classifications without showing where model disagreement makes the resulting map uncertain.
Key Innovation: An ensemble of analytic hierarchy, fuzzy and frequency-ratio models adds pixel-wise standard deviation as an uncertainty layer; 73.85% of 65 historical flood locations fall in the high-susceptibility class and more than 90% fall in susceptible classes.
50. TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model
Core Problem: This division creates a gap between the control offered by feature engineering and the automated performance of end-to-end models.
Key Innovation: The study proposes TS2TabPFN, a framework that bridges this gap by integrating explicit feature extraction with TabPFN 2.5, a cutting-edge foundation model for tabular data, to leverage its predictive capabilities.
51. OutLangSplat: 3D Language Gaussian Splatting for UAV Outdoor Scenes
Core Problem: However, existing methods are limited to indoor or small-scale scenes, and tend to fail in Unmanned Aerial Vehicle (UAV) outdoor scenes, where severe occlusions and long distance viewpoints often lead to incorrect semantic activations and missing target responses.
Key Innovation: In this paper, we present OutLangSplat which adapts language Gaussian representations to UAV outdoor scenes by improving feature representation and aggregation reliability.
52. On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
Core Problem: This paradigm is particularly relevant in remote sensing (RS), where legal regulations, privacy concerns, and bandwidth constraints restrict data sharing.
Key Innovation: To address this issue, in this paper, we present the first comparative study of VLM adaptation strategies for FL in the context of RS image classification. However, their large parameter size may substantially increase communication overhead and local computational complexity in federated settings.
53. DefoEye: Python-Based Software for Facilitating Time-Series InSAR Analysis of Sentinel-1 Remote-Sensing Data
Core Problem: Although GMTSAR avoids some of these constraints, it still requires substantial manual intervention and C-shell commands, lacks a user-friendly graphical interface, and omits important steps such as interferogram network pruning and anchoring of unwrapped interferograms.
Key Innovation: The study introduces DefoEye (v1), an open-source Python-based software package that wraps GMTSAR and provides a unified, user-friendly TS-InSAR workflow for Sentinel-1 data. In Bologna, Italy; Gotland, Sweden; and Houston, USA, DefoEye results were compared with observations from 10 GNSS stations and showed strong agreement, with RMSE values of 4.3-11.9 mm and Pearson correlation coefficients of 0.63-0.95.
54. Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes
Core Problem: Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining.
Key Innovation: Despite this momentum, the design space and the fundamental mechanisms of how modalities interact during unified training remain underexplored. This process uncovers a vision laziness phenomenon, where delayed integration leads models to rely on language priors; (iv) Recipes: We derive efficient pretraining recipes that achieve strong generative performance using only 5% of the compute budget.
55. Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
Core Problem: Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data.
Key Innovation: The authors also use posterior sampling to assimilate real observation sources, such as those from the NOAA Integrated Global Radiosonde Archive and the Integrated Surface Database. Moreover, we generate full-state ensemble forecasts directly from sparse observations, achieving performance competitive with state-of-the-art observation-to-forecast models.
56. Uncertainty-aware Predict-Then-Optimize Framework for Equitable Post-Disaster Power Restoration
Core Problem: The increasing frequency of extreme weather events, such as hurricanes, highlights the urgent need for efficient and equitable power system restoration.
Key Innovation: To address this, we aim to propose an equity-aware power restoration strategy that balances both restoration efficiency and equity across communities. However, achieving this goal is challenging for two reasons: the difficulty of predicting repair durations under dataset heteroscedasticity, and the tendency of reinforcement learning agents to favor low-uncertainty actions, which potentially undermine equity.
57. SEAR: Simple and Efficient Adaptation of Visual Geometric Transformers for Unpaired RGB+Thermal 3D Reconstruction
Core Problem: However, their effectiveness drops when applied to mixed sensing modalities, such as RGB-thermal (RGB-T) images.
Key Innovation: To address this, we propose SEAR, a simple yet efficient fine-tuning strategy that adapts a pretrained geometry transformer to multimodal RGB-T inputs.
58. Stable Attention Response for Reliable Precipitation Nowcasting
Core Problem: Precipitation nowcasting remains challenging due to the highly localized, rapidly evolving, and heterogeneous nature of atmospheric dynamics.
Key Innovation: Based on this insight, we propose HARECast, a Head-wise Attention Response Energy-regulated framework for precipitation nowcasting. We instantiate HARECast in a standard forecasting pipeline with reconstruction branches and a diffusion-based predictor, and evaluate it on commonly used benchmarks--SEVIR and MeteoNet.
59. Simulation-Efficient model for fully Dispersive and Nonlinear waves in Arbitrary-depth (SEDNA)
Core Problem: Accurate prediction of coastal wave fields over realistic bathymetry is essential for maritime safety and engineering applications.
Key Innovation: The study develops a GPU-based Simulation-Efficient model for Dispersive and Nonlinear surface gravity waves in Arbitrary-depth (SEDNA). Using CUDA-accelerated pseudo-spectral formulations, SEDNA achieves a speedup exceeding 200 over its CPU counterpart on grids up to 4096 2.
60. Technical note: Temperature dependence of precipitation tail heaviness in the TENAX model
Core Problem: However, implementing this dependence increases the number of parameters to be estimated, affecting the model's accuracy.
Key Innovation: Here, we use hourly data from thousands of rain gauges in Germany, Japan, the UK, and the USA to assess the dependence of the Weibull shape parameter on temperature, exploring how it should be implemented in the TENAX model. However, Monte Carlo simulations show that including this dependence without careful consideration may lead to overestimation of precipitation return levels and increase the model uncertainty.
61. TRB-Net: Terrain-Residual and Boundary-Assisted Multimodal Martian Landslide Segmentation on a Local MMLSv2 Split
Core Problem: Martian landslide deposits have sparse labels, weak boundaries and terrain textures that resemble crater rims and canyon walls.
Key Innovation: TRB-Net fuses RGB, elevation, slope, thermal inertia and grayscale data with terrain-residual and boundary supervision, achieving competitive local-split segmentation while explicitly withholding claims about geographic transfer.
62. Remote Sensing-Based Identification of Sensitive Environmental Intervals Controlling Drought Propagation Time Across China
Core Problem: However, existing warning systems often focus on drought propagation probabilities and average propagation times, with insufficient attention to the identification of changes in propagation time.
Key Innovation: The study constructs a meteorological drought–soil drought–groundwater drought propagation chain and uses both Granger causality and the maximum positive correlation coefficient method to quantify stage-specific propagation time. The results show that PET, NDVI, mean annual precipitation, and temperature exhibit high importance across multiple regions and stages.
63. Resilience based design approach for seismic-resilient infrastructures: a state-of-the-art review
Core Problem: The infrastructure of the country server as an important pillar of a nation’s economy and maintaining continuity in functionality after seismic events is much required and important.
Key Innovation: The study also discusses the retrofitting techniques designed to strengthen structural resilience against seismic forces. Studies conducted previously have used steel jacketing, concrete jacketing and Fiber Reinforced Polymer (FRP) systems that can yield considerable improvements, with functional recovery after seismic hazard enhanced by roughly 40 to 62 percent.
64. Seismic fragility analysis of building-foundation systems
Core Problem: The impact of soil-structure interaction (SSI) should be considered in seismic fragility analysis of buildings on soft soils.
Key Innovation: Past SSI-related analyses focus on seismic fragility of buildings without integrating foundation damage probability into an SSI system level fragility.
65. Characterization and Modeling of the In‐Plane Multi‐directional Behavior of a Rolling Pendulum Isolation System
Core Problem: The social and economic losses caused by an earthquake can be mitigated by using a rolling pendulum (RP) isolation system to protect vital non‐structural building contents.
Key Innovation: In this paper, a physics‐based mathematical model for an RP bearing—as well as an isolation systems comprised of these bearings—is derived. Controlled‐displacement characterization tests were defined to capture the envelope performance of the isolation system subjected to a variety of multi‐directional conditions, by specifying combinations of slow and fast tests, various in‐plane (,, and ) amplitudes and frequencies, and different orbits.
66. The 2004 Italian Seismic Hazard Model: Twenty Years On
Core Problem: The Italian national seismic hazard model, MPS04, was developed more than 20 years ago to provide a rational basis for regulatory applications.
Key Innovation: Since 2009, it directly provides seismic actions for structural design and assessment in the building code.
67. Analyzing the Economic Impacts of the Expected İstanbul Earthquake: An Input‐Output Model Integrated with a System Dynamics Model
Core Problem: However, the city is prone to a major earthquake risk which may have devastating impacts on human lives as well as the regional and national economy.
Key Innovation: Thus, this study introduces a hybrid methodology to estimate the economic impacts of a potential earthquake that would affect İstanbul. The findings demonstrate that the hybrid framework can reveal the sector‐specific and dynamic economic consequences of an earthquake and help identify policies that may enhance post‐disaster recovery.
68. Artificial neural network-based approach for structural health monitoring of earthquake-affected industrial structures
Core Problem: Purpose The purpose of this study is to develop a reliable and rapid method for seismic damage detection in industrial structures using artificial neural networks (ANN).
Key Innovation: Industrial buildings often lack traditional seismic-resistant elements like shear walls, making them more vulnerable to earthquake-induced damage. The proposed approach facilitates post-earthquake safety assessment and structural integrity evaluation through rapid prediction of critical design parameters.
69. A Theory for Stress‐History Dependent Permeability Tensor of Crystalline Rocks With Isotropic Microfabric Under Pseudo‐Elastic State
Core Problem: The permeability tensor for crystalline rocks with isotropic fabric was formulated in terms of their micro‐fabric and stress tensor without introducing any curve fitting parameters.
Key Innovation: Based on experimental data on a joint in dolerite, the present theory suggests that the permeability of a dolerite rock mass decreases by about two‐orders of magnitude just after three loading cycles.
70. Gray‐Zone Simulation of the Dry Convective Boundary Layer Using Fourier Neural Operator
Core Problem: Previous machine learning (ML) weather prediction models primarily focus on global mesoscale forecasting.
Key Innovation: The study develops three‐dimensional Fourier neural operator (FNO) models for simulating the dry convective boundary layer (CBL) at 800‐m grid spacing, which is a resolution in the gray zone. The FNO models outperform traditional gray‐zone simulations in predicting a variety of flow statistics and instantaneous structures, including the scale of updrafts and downdrafts, the vertical profiles of statistics, and the energy spectra.
71. Ensemble‐Based Parameter Estimation for an Unconfined Groundwater Model of a Small Island Considering Tidal Overheight
Core Problem: Tidal influences are rarely considered in parameter estimation procedures for coastal groundwater flow models.
Key Innovation: Our objective was to present a parameter estimation procedure that includes tidal overheight in coastal groundwater flow models and to analyze the appropriateness of the chosen model, process setup, and its simplifications.
72. A Robust, Low‐Cost Conductance Sensor for High Resolution Real‐Time Monitoring of Streambed Pore Water Dynamics
Core Problem: However, our understanding and ability to predict the spatial‐temporal dynamics of subsurface travel times are limited because current techniques are restricted to a few locations or constant subsurface flows.
Key Innovation: To overcome these limitations, we designed and field‐tested a small, easy‐to‐build, low‐cost pore water conductance sensor to monitor subsurface travel times in real‐time.
73. Physics-informed reduced-order modelling with equivariant spectral submanifolds
Core Problem: The computation of SSMs, however, remains computationally expensive, particularly for high-dimensional systems.
Key Innovation: The authors establish the mathematical foundations of this approach by showing that SSMs are naturally equivariant submanifolds and that the associated charts and reduced dynamics inherit the appropriate induced group actions. Building on this framework, we develop a novel equivariant SSM reduction algorithm that exploits these symmetries to achieve substantially faster computations while also improving model robustness.
74. iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data
Core Problem: To tackle this challenge, we introduce Graph-Enhanced Descriptor Sequencing (GEDS), a structured feature sequencing algorithm grounded in principles from the Column Permutation Problem (CPP).
Key Innovation: The authors incorporate GEDS within an order-aware efficient transformer framework, utilizing order-aware memory tokens that explicitly adhere to the derived feature sequencing via a dedicated loss function. Experimental results across multimodal benchmarks demonstrate that iStructTab effectively minimizes feature dispersion, improving predictive performance and robustness, and highlighting the significance of structured feature sequencing in multimodal learning.
75. EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series
Core Problem: Multimodal temporal data are inherently irregular and uneven in information density, yet most models rely on uniform discretization, leading to inefficient representations.
Key Innovation: The authors propose EvtGraph, a unified framework that aligns computation with temporal salience under explicit budget constraints. Experiments on multimodal clinical (MIMIC-IV + CXR) and cross-domain benchmarks demonstrate that EvtGraph outperforms both Transformer-based and recurrent baselines while significantly improving efficiency.
76. COSMO: Consensus-Driven Shift Modulation for Source-Free Domain Adaptation
Core Problem: Source-free domain adaptation (SFDA) adapts a source-trained model to an unlabeled target domain without source data, a practical setting under privacy or storage constraints.
Key Innovation: The authors formulate VLM-guided SFDA as a sample-wise reliability-allocation problem and propose Consensus-Driven Shift Modulation (COSMO). It first forms a sample-specific initial consensus that favors the more concentrated prediction.
77. Personalized Federated Sparse Adaptation of Time-Series Foundation Models
Core Problem: However, a single parameter-sharing strategy is unlikely to serve all pretrained TSFMs or building clients: fully shared adapters can suppress building-specific temporal behavior, while fully local adaptation discards cross-building transfer.
Key Innovation: The authors propose a personalized federated sparse adaptation framework with a heterogeneous temporal mixture-of-experts (MoE) adapter placed after the pretrained TSFM representation. Across 50 buildings and three TSFM backbones, personalization consistently outperforms Global FL-MoE and Local MoE, while the best sparse-adaptation strategy varies by backbone and metric.
78. Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Core Problem: Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated datasets.
Key Innovation: The authors conduct two empirical analyses covering 48 and 20 DL models, respectively, spanning design choices such as network architecture, fine-tuning strategy, learning strategy, and initialization. By applying fANOVA across seven MLC RSI datasets, we construct dataset meta-representations that capture design-choice sensitivity profiles.
79. Continual-Learning Physics-Informed Neural Networks for Parameterized Partial Differential Equations
Core Problem: Existing ParamPINNs, however, still face inefficient training, uneven accuracy across parameters, and overfitting to a limited set of sampled parameter tasks, which can impair generalization to unsampled parameters.
Key Innovation: To address these issues, we propose a continual-learning physics-informed neural network (CL-PINN), which treats PDE instances at different parameter values as related tasks and learns them sequentially. CL-PINN combines Bayesian-optimization-based active parameter selection, task-wise dynamic loss weighting, sparse physics-constrained replay, and an optional parameter subnetwork to improve task allocation and knowledge retention under bounded active-task capacity.
80. Above-ground Biomass Estimation with Geospatial Foundation Models
Core Problem: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.
Key Innovation: Here, we present a comprehensive benchmark of GFMs for global-scale AGB estimation using the AGBD dataset, a machine learning-ready benchmark spanning diverse biomes and geographies. We compare 11 GFMs available on PANGAEA and both embedding products against a fully supervised state-of-the-art (SOTA) model, assess their geographical and temporal generalization abilities, as well as agreement with the ESA CCI biomass product on independent reference data.
81. Towards a satellite image manipulation and deepfake localization benchmark dataset
Core Problem: The remote sensing community lacks high-quality, fine-grained manipulation datasets suitable for training and evaluating detection and image forensics algorithms.
Key Innovation: To address this gap, we describe a preliminary dataset construction process and prototype benchmark dataset for satellite image manipulation detection and localization.
82. MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
Core Problem: The authors investigate the transferability of Earth weather foundation models to planetary atmospheres by adapting the GraphCast graph neural weather forecasting model to Mars.
Key Innovation: Using the Mars Climate Database (MCD), which provides global atmospheric fields across vertical altitude levels (similar to Earth pressure levels), we evaluate zero-shot and fine-tuned GraphCast predictions of Martian temperature and wind fields.
83. An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification
Core Problem: However, the representation power of classification models is limited by inherent high-dimensional and heterogeneous spatial-spectral information, unbalanced sample distribution, and inter-class spectral similarity of airborne MPCs.
Key Innovation: The authors build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification. Another important contribution of this work is a joint loss function to improve the learning ability on unbalanced and interclass similar samples.
84. Perceptual Anchoring: Prototype-Guided Text Calibration for Training-free Open-Vocabulary Semantic Segmentation
Core Problem: However, existing methods typically focus on improving visual representations while treating text embeddings that encode only generic category concepts as fixed classification references.
Key Innovation: Inspired by the symbol-percept correspondence underlying perceptual anchoring, we propose Prototype-Guided Text Calibration (PTC) for training-free OVSS. These results validate PTC as a simple and effective approach to improving visual-text alignment.
85. How to obtain hydro-mechanical responses inside the localisation zone from triaxial tests on partially saturated soils
Core Problem: However, this assumption is invalid in the post-localisation stage, where the inelastic response of the specimen is dominated by significant interactions between grain rearrangement and liquid redistribution inside the localisation band.
Key Innovation: The advances of the proposed approach are elucidated through several results showing tight correlations between the interpretation of experimental data, parameter calibration and model validation.
86. Experimental study on seabed response and scour characteristics around monopile under flow-vibration coupling
Core Problem: With regard to the seabed response and local scouring of wind turbine monopiles under the coupled action of currents and vibrations, existing studies have predominantly relied on constant-amplitude cyclic loading, an approach that fails to adequately represent the irregular influence characteristics of the narrow-band random vibration of the tower in practical engineering.
Key Innovation: The present study was carried out in a wave-current flume, where a total of 27 test conditions were established by varying the incident flow velocity, vibration type, and vibration intensity, to monitor the pore pressure distribution in the seabed around the pile and the corresponding scour topography.
87. HOSSP: Multimodal Remote Sensing Image Matching Using Symmetrically-Weighted Steerable Phase Orientation Histograms
Core Problem: However, existing methods often suffer from insufficient correct matches, large matching errors, and limited efficiency when spatial geometric differences (SGD) and nonlinear radiometric distortions (NRD) coexist.
Key Innovation: To address these challenges, we propose a multimodal image matching method termed histogram of symmetrically-weighted steerable phase orientation (HOSSP). 1) A second-order steerable maximum/minimum moment map is constructed in a nonlinear scale space by jointly exploiting blob and corner features, and a structure-guided SSC (SG-SSC) strategy is proposed to improve the saliency and stability of keypoint selection.
88. A Geo-Foundation Model-Based Framework for Wetland Mapping Using High-Resolution Satellite Data
Core Problem: Accurate and scalable wetland classification remains a challenging task due to spectral similarity among wetland types, landscape heterogeneity, and limited labeled data.
Key Innovation: The study presents a geo-foundation-model (GFM)-based framework for multiclass wetland classification using high-resolution planet multispectral imagery and spectral indices in St. The proposed model achieved an OA of 0.92 and an F1-score of 0.90 on the full training dataset and consistently outperformed baseline models across all training scenarios, particularly under limited-data conditions.
89. Extracting Value from Fused Aerial and Terrestrial LiDAR Scans
Core Problem: Accurate estimation of forest structural attributes over operational scales remains challenging because unmanned laser scanning (ULS) provides extensive spatial coverage but limited representation of internal stem structure, whereas terrestrial and mobile laser scanning (TLS/MLS) provide detailed stem measurements over relatively small areas.
Key Innovation: The study investigates a calibration-transfer framework in which small areas of terrestrial or fused LiDAR are used to improve diameter at breast height (DBH) estimation across much larger regions surveyed only by ULS.
90. A China-Specific Near-Real-Time GNSS Water Vapor Retrieval Model Based on LightGBM
Core Problem: However, conventional meteorology-independent models still have limitations in regional adaptability, vertical accuracy, and the representation of nonlinear atmospheric variability.
Key Innovation: To address these limitations, this study proposes a China-specific near-real-time GNSS water vapor retrieval model, termed China LightGBM-based Zenith Hydrostatic Delay and Precipitable Water Vapor Model (CLZP), by integrating LightGBM with near-real-time GNSS observations.
91. Comprehensive Use of GNSS Vertical Deformation and GRACE/GFO Data to Invert the Joint Drought Index of Three Central China Provinces
Core Problem: For missing parts of GRACE and GNSS data, different methods were effectively employed to fill the gaps.
Key Innovation: Using the TWS derived from joint inversion, a drought index (Joint-DSI) was constructed, successfully identifying and tracking seven major drought events in the study area. The results indicate that joint inversion effectively integrates the high-frequency spatial signals of GNSS with the large-scale smoothing features of GRACE.
92. FAD-Net: Frequency Alignment Dual-Branch Network for Hyperspectral Image Super-Resolution
Core Problem: Recent hybrid methods combining convolutional neural networks (CNNs) and Transformers have substantially improved spatial reconstruction performance, but spectral fidelity remains insufficiently explored.
Key Innovation: To explicitly exploit this complementarity, we propose a Frequency Alignment Dual-Branch Network (FAD-Net) for HSI SR. Experiments on Chikusei, Botswana, and Pavia Center at ×2, ×3, and ×4 scales show that FAD-Net achieves the best Spectral Angle Mapper (SAM) in all nine settings while maintaining competitive spatial performance.
93. Lightweight Bayesian SAR Image Object Detection and Recognition Method Based on Heavy-Tail Prior and Variational Inference
Core Problem: Traditional Bayesian SAR detection methods suffer poor adaptability to speckle noise, fail to handle severe class imbalance within large-scale multi-target datasets, and incur prohibitive training overheads.
Key Innovation: To address these drawbacks, this paper develops a lightweight Bayesian detection and recognition framework built upon heavy-tailed Laplacian priors and variational inference. Evaluated on the MSAR-1.0 dataset, our approach achieves an mAP@0.5 of 94.98% and a macro balanced accuracy ( B A ) of 93.34%.
94. Surface-Water Fragmentation and Heterogeneous Responses of Dish-Shaped Sub-Lakes in Poyang Lake During the 2022 Extreme Drought
Core Problem: Extreme droughts can rapidly reshape surface-water patterns in river-connected floodplain wetlands, yet the fine-scale responses of individual dish-shaped sub-lakes remain insufficiently resolved.
Key Innovation: The authors developed a high-resolution wetland monitoring framework on the Google Earth Engine platform by integrating Sentinel-2 multispectral imagery, Sentinel-1 synthetic-aperture radar, and the Dynamic World land-cover product. The framework achieved an overall accuracy of 87.44%, with a Kappa coefficient of 0.80.
95. Smart prediction of intact rock properties using infrared thermography measurements and AI-driven algorithms
Core Problem: The application of intelligent algorithms for predicting rock porosity and mechanical strength is presented herein to improve the predictive performance of the Cooling Rate Index (CRI10), an experimental index calculated by the pioneering laboratory IRTest, which monitors surface rock cooling via Infrared Thermography.
Key Innovation: Then, an augmented database containing 369 data was developed by filling in missing values, processing imbalanced data, and removing outliers. Furthermore, several models for predicting rock porosity and mechanical strength were developed via four machine learning algorithms.
96. Spatiotemporal evolution of channel bars in a confined canyon river: Insights into delayed downstream propagation of bedload pulses
Core Problem: However, owing to limited field investigations and scarce long-term hydrological records leave the mechanisms governing channel-bar responses to bedload pulses poorly understood.
Key Innovation: The study addresses an important observational gap in alpine canyon systems and provides a first-order basis for assessing channel-bar adjustment and sediment-storage dynamics in ungauged mountain rivers. The results reveal a downstream shift in controls, from sediment-supply dominance upstream to morphology-driven regulation downstream.
97. Minute-scale observations reveal hydrothermal coupling dynamics of soil freeze–thaw processes in the alpine source region of the Yellow River
Core Problem: Under a warming climate, the degradation of permafrost and seasonally frozen ground in the Yellow River source region is altering freeze-thaw processes.
Key Innovation: The study integrates minute-scale measurements of soil temperature and moisture with a COMSOL-based hydro-thermal-mechanical coupling model to systematically investigate freeze-thaw dynamics, soil water-heat interactions, and soil-layer displacement responses across different freeze-thaw stages.
98. Rainfall-runoff-soil loss dynamics under different soil management and cropping systems in the Humid Lowlands of Beles River Sub-Basin, Ethiopia
Core Problem: Understanding how soil management and cropping systems influence surface runoff and soil loss is essential for designing sustainable land management strategies in humid environments.
Key Innovation: The study investigates the effect of tillage and cropping systems on runoff dynamics, soil erosion, and rainfall-runoff relationships under natural rainfall conditions on Nitisols in the Pawe area, northwestern Ethiopia. Results revealed that tillage and crop cover significantly influence runoff, soil loss, and sediment concentration throughout the season (p < 0.05).
99. High-fidelity modelling of fragmentation and pulverisation in hard granite under percussion loading: a FDEM-based approach
Core Problem: The difficulty of fracturing hard crystalline rocks, such as granite, remains a major obstacle to the efficient development of geothermal and mineral resources.
Key Innovation: In this study, a damage model was developed within a Combined Finite-Discrete Element (FDEM) framework to simulate the fragmentation behaviour of Kuru Grey granite under percussion loading. The model successfully reproduces the characteristic failure modes of granite, which are fundamentally different from the sedimentary rock (i.e., limestone and sandstone), and accurately captures the nonlinear variations in bit rebound behaviour and rock pulverisation as the impact energy increases.
100. Mechanical behaviour and damage evolution of coal pillars in abandoned coal mine gas storage
Core Problem: However, gas injection and withdrawal impose long-term cyclic loading and creep effects on the surrounding rock mass.
Key Innovation: From the current study, an Ashby-based stress-damage equation is established, revealing the existence of a stress threshold and its dependence on confining pressure. The results show that, as expected, confining pressure substantially enhances coal's compressive strength and ductility and this is well described by the Hoek-Brown relation.
101. Advancing seasonal water supply forecasting for lakes and reservoirs using copulas
Core Problem: The future of water storage dynamics in lakes around the world is increasingly uncertain due to a changing climate and evolving anthropogenic demands.
Key Innovation: Now, more than ever, it is necessary to quantify the uncertainty in forecasts of future water supply and water levels. However, classical ensemble forecasting systems that rely on weighted repetitions of historical water flux sequences, or independently resampled then post-processed sequences, often fail to capture either the dependence between hydrological variables across space and time or the full range of plausible outcomes for these variables.
102. Groundwater flow regimes and intrinsic vulnerability in a fractured bedrock aquifer: insights from hydrochemistry, isotopes, geophysics, and groundwater age
Core Problem: Intrinsic vulnerability in fractured bedrock aquifers is commonly evaluated using index-based approaches, yet these methods often underestimate the role of flow system architecture, recharge mechanisms, and groundwater renewal in controlling contaminant susceptibility.
Key Innovation: The study integrates hydrochemistry, environmental isotopes (δ18O, δ2H, 87Sr/86Sr), tritium-based groundwater age modeling, multivariate statistical analysis, and geophysical investigations to constrain groundwater flow systems in a fractured bedrock aquifer in the Boeun region, Korea. Despite groundwater quality index values indicating excellent water quality at present, DRASTIC results show moderate to high vulnerability associated with rapid recharge and structurally connected flow paths.
103. A dynamic constitutive model considering state transition for saturated loess under monotonic and cyclic shearing
Core Problem: However, it has not been adequately captured by existing loess constitutive models.
Key Innovation: To address the issue, a dynamic constitutive model considering state transition was formulated based on the Twin shear unified strength theory and the Bounding surface plasticity theory. A state evolution equation was formulated by introducing an instability line (IL) combined with critical state line (CSL) to predict the state transition of loess.
104. Investigating the P–Delta effect on the lateral ductility and instability behavior of scoured fixed-head piles
Core Problem: Although previous studies have investigated the lateral response of scoured piles, the influence of the P-Delta effect on their ductility and instability behavior remains insufficiently understood.
Key Innovation: The study investigates the stability transition and ductility behavior of scoured fixed-head piles subjected to combined axial and lateral loading. The proposed solutions are validated through numerical simulations, followed by parametric analyses to examine the influence of scour depth, subgrade reaction coefficient, sectional overstrength ratio, curvature ductility capacity, and axial force ratio.
105. Cumulative plastic deformation of carbonaceous shale filler under coupled wetting–drying and dynamic loading: Experimental investigation and prediction model
Core Problem: Carbonaceous shale filler is highly susceptible to physical disintegration under wetting-drying (W-D) cycles, severely complicating the evolution of its long-term dynamic characteristics under combined environmental degradation and mechanical loading effects.
Key Innovation: The confining pressure sensitivity index is introduced as a novel index to quantitatively decouple environmental degradation from pure mechanical responses, thereby revealing the evolution of stress sensitivity in the weathered filler. Notably, the dynamic stress amplitude significantly amplifies environmental damage, while weathering substantially increases the filler’s sensitivity to confining pressure variations, and 60 kPa represents a characteristic stress level.
106. Abnormal horizontal displacement response of existing tunnels induced by non-uniform dewatering and partitioned excavation: a case study
Core Problem: The study investigates the abnormal horizontal displacement response of existing tunnels adjacent to a deep excavation under non-uniform dewatering and partitioned excavation.
Key Innovation: A field case from Hangzhou, China, involving an operating metro tunnel and the Jiangnan Avenue underground tunnel, is presented. The results show that, despite the use of cut-off walls, partitioned excavation, internal dewatering, and servo-controlled struts, the maximum cumulative horizontal displacement of the downline metro tunnel reached approximately 6.2 mm, exceeding the control value of 5.0 mm, whereas track-bed settlement and horizontal convergence remained within the control limits.
107. Damage evolution and mechanical response of highly porous mudstone under wetting–drying cycles: An experimental and theoretical study
Core Problem: Low-density and highly porous mudstone is susceptible to mechanical degradation under wetting-drying (W-D) cycles, which can compromise the long-term stability of mudstone slopes.
Key Innovation: At the mesoscopic and microscopic scales, W-D cycling promoted cementation deterioration, pore-surface development, and microcrack growth within the weakly cemented matrix. Although the dominant pore structure type remained essentially unchanged, the specific surface area derived from N2 physisorption increased from 18.4 to 23.1 m²/g, suggesting enhanced pore accessibility and pore-microcrack connectivity.
108. Shear stiffness of rock joints based on direct displacement measurements
Core Problem: However, conventional laboratory measurements of relative displacements in shear boxes include deformations beyond those of the joint itself, arising from inherent deficiencies in the testing system.
Key Innovation: The study presents novel insights into shear stiffness, enabled by optical displacement measurements obtained directly from the joints in a comprehensive experimental programme that significantly extends the range of previously investigated conditions. Shear stiffness, which relates shear displacement to shear stress, is therefore a key input parameter for predicting the mechanical stability and hydraulic permeability of rock masses.
109. How many tagged stones are needed to estimate the mean travel distance of sediment from tracer experiments in gravel‐bed rivers?
Core Problem: However, these kinds of tracer experiments have typically been conducted without clear guidelines or rules on what constitutes an adequate sample size of tracers to minimise bias and uncertainty in displacement metrics.
Key Innovation: Based on our analysis, we concluded that 500–1000 tagged stones is an optimal tracer sample size for estimating mean travel distances.
110. TRNet: Topography-Guided Frequency Rectification and Structure-Aware Decoding for Multimodal Paddy Rice Segmentation
Core Problem: Mapping paddy rice from very-high-resolution imagery in mountainous and hilly regions is difficult because terrain alters optical appearance and increases confusion with visually similar vegetation.
Key Innovation: The authors present TRNet for 0.5-m GaoJing-1 red--green--blue (RGB) imagery, a 5-m TanDEM-X digital elevation model (DEM), and derived slope. TRNet achieved rice intersection-over-union (IoU) values of 85.10% and 80.68%, exceeding the original Dual-Encoder U-Net by 9.15 and 18.83 percentage points, respectively.
111. Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling
Core Problem: Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure.
Key Innovation: To address this, we propose EddyFlow, a representation learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization.
112. ColorFD: A Finite-Difference Guided Black-Box Physical Adversarial Attack for Remote Sensing Object Detection
Core Problem: Although deep neural network-based remote sensing object detectors have achieved strong performance, they remain vulnerable to adversarial perturbations.
Key Innovation: To address these challenges, this paper proposes ColorFD, a black-box physical attack based on multiple pure-color patches. A target-wise fitness and selection mechanism evaluates the attack state of each target and preserves target-specific improvements during evolution.
113. CSGen: A Multi-Domain Curvilinear Structure Generation Model via Hierarchical Multimodal Diffusion
Core Problem: However, the controllable generation of images with precise curvilinear structure objects remains an open challenge.
Key Innovation: To address this, we propose CSGen, a hierarchical multimodal diffusion model that synthesizes high-fidelity images precisely aligned with multiple control conditions. Extensive experiments demonstrate that CSGen generates images with superior structure accuracy and visual realism, significantly improving downstream segmentation performance while maintaining robustness across diverse prompts.
114. StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
Core Problem: However, on the ADE20K and Cityscapes benchmarks, our study reveals that on a GPU platform such dynamic methods exhibit a surprisingly low frame rate far below a simple static approach, while having comparable results in mIoU and FLOPs.
Key Innovation: To address this issue, we propose a static structured pruning method for attention layers, that achieves both, a lower FLOPs and a high frame rate [fps] of the SegFormer network, the latter increased by up to 34% relative on the Cityscapes dataset, while having no mIoU performance drop at all.
115. Machine learning–enhanced framework of complex nonlinear wave dynamics in oceanic atmosphere
Core Problem: In this paper, we explore novel nonlinear wave solutions of the combined Kairat-II-X equation using the artificial neural network approach.
Key Innovation: For exact soliton solutions, the Hirota bilinear form of the governing model is constructed, and the Bilinear neural network method is used.
116. SonarMamba: A hybrid state space network for object detection and instance segmentation in side-scan sonar images
Core Problem: However, severe noise interference, low target-background contrast, and ambiguous boundaries make discriminative feature representation challenging; meanwhile, large-scale underwater surveys place additional demands on computational efficiency.
Key Innovation: To address the above challenges, this work proposes a hybrid state space network for object detection and instance segmentation in side-scan sonar images, termed SonarMamba. A series of experiments conducted on three public side-scan sonar datasets demonstrates that SonarMamba improves object detection and instance segmentation performance with reasonable computational cost, validating the effectiveness of the proposed method for underwater target perception.
117. Loading-rate sensitivity of damage mechanisms in marine sedimentary coral reef limestone
Core Problem: Coral reef limestone (CRL) has undergone complex diagenetic processes driven by marine sedimentation and biochemical activity, producing multiscale pore structures that introduce considerable inherent defects and significantly influence its mechanical behavior.
Key Innovation: To address the disturbance issues within multi-strain rates during the construction of island reef underground, this study adopted X-ray computed tomography to capture two-dimensional CRL morphologies and proposed two strategies for resolving the intersection issues of complex pore boundaries.
118. Experimental investigation of breaker indices and breaker types for irregular waves propagating over constant-sloped and barred coastal profiles
Core Problem: The study presents experimental results for breaker indices as well as wave breaker types for 10 different irregular wave conditions propagating over two different constant sloping profiles as well as two barred profiles.
Key Innovation: Cross-shore evolution of both breaker type and breaker indices are presented, and it is demonstrated that both plungers and spillers are present across the entire coastal profiles and that there is a large cross-shore variation in breaker indices and types even for fixed wave conditions. Similar to previous research, it is shown that plungers have higher breaker indices than spillers and that breaker indices generally increase in shallower water.
119. A Robust Seabed Sediment Classification Method Based on Multifeature Fusion
Core Problem: Seabed sediment classification using multibeam echosounder (MBES) data, such as bathymetry, backscatter angular response (AR), and backscatter mosaic, has the advantages of being faster and less costly than the traditional seafloor grab sampling.
Key Innovation: In this article, we proposed a robust seabed sediment classification method based on multifeature fusion. After examining the classification results, it was observed that the root mean square error (RMSE) of the AR curve decreased from 2.57 to 1.64 dB.
120. SPM-DETR: Scene-Prior Guided Cross-Partial Mixing Network for Small Object Detection in UAV Remote Sensing Imagery
Core Problem: Detecting small objects in uncrewed aerial vehicle (UAV) remote sensing imagery remains challenging, as targets often occupy only a few pixels and are easily overwhelmed by cluttered backgrounds, occlusions, and repetitive textures, leading to missed detections and background-induced false alarms.
Key Innovation: To address these challenges, we propose scene-prior guided cross-partial mixing network (SPM-DETR), an end-to-end framework tailored for small object detection in UAV remote sensing imagery. Experiments on the VisDrone-DET show that SPM-DETR achieves 54.1% mAP@0.50 and 34.4% mAP@0.50:0.95 on the validation set (+6.2 and +5.0 points over the RT-DETR-R18 baseline), and 43.0% mAP@0.50 and 25.7% mAP@0.50:0.95 on the test-dev set.
121. Adaptive Semantic Enhancement for Remote Sensing Image–Text Retrieval
Core Problem: However, the performance is hindered by two key challenges: the presence of multiscale objects in remote sensing images and the subjectivity of text descriptions.
Key Innovation: To address these limitations, an adaptive semantic enhancement network (ASEnet) is proposed to enhance both image and text representations. It dynamically adjusts kernel sizes and sampling locations, enabling flexible capture of fine-grained details and improving image feature expressiveness.
122. Metrics that matter: objective functions and their impact on signature representation in conceptual hydrological models
Core Problem: Although objective functions (OFs) are widely discussed in the literature, many modelling studies still default to a few common metrics, without much consideration of their relative strengths and weaknesses.
Key Innovation: The study systematically investigates the impact of OF choice on the representation of various streamflow characteristics across 47 conceptual models and 10 hydro-climatically diverse catchments selected from the CARAVAN dataset. We evaluate the representation of 15 hydrological signatures that capture a relevant selection of streamflow characteristics to determine generalisable strengths and weaknesses of individual OFs across different models and hydroclimatic conditions.
123. A Universal Multifractals perspective into the link between rainfall variability and temperature
Core Problem: The link between rainfall extremes, usually defined as a given percentile or for a given return period, and temperature has been widely investigated using measurement data and/or climate model outputs and notably convection permitting model outputs.
Key Innovation: Here we investigate more generally how rainfall variability across scales change with temperature, relying on the scale invariant framework of the Universal Multifractals. It provides a framework to interpret previously commonly reported trends of a scale dependence of the rate of increase of extremes with temperature.
124. A Cross-System Remote Sensing Framework for Diagnosing Event-Scale Soil Wetting, Vertical Propagation, and Pre-Cipitation Thresholds Across China’s Croplands
Core Problem: Soil moisture (SM) remote sensing is widely used for agricultural drought monitoring, yet most applications still emphasize static moisture states rather than event-scale wetting responses.
Key Innovation: The authors developed an interpretable Earth observation (EO) framework to evaluate precipitation–SM product consistency and diagnose wetting processes across China’s croplands. These findings indicate that event-scale EO diagnostics can characterize product consistency, lagged wetting responses, and state-dependent precipitation thresholds, while same-system and deep-layer interpretations remain constrained by reanalysis coupling and model-assisted root-zone products.
125. Integrating Multi-Source Geoscientific Data via Geologically Constrained Feature Engineering for Gold Prospectivity Mapping: A Case Study of Jiaoxibei, China
Core Problem: However, the superposition of multiple mineralization events has resulted in strong spatial coupling, multi-scale variability, and substantial redundancy among structural, alteration, geophysical, and geochemical information, limiting the effective extraction of key ore-controlling features.
Key Innovation: The study developed a geologically constrained feature-engineering framework for regional mineral potential evaluation. The resulting high-potential zones captured 21 of the 22 known gold occurrences in the Sanshandao and Jiaojia areas and 17 of the 23 occurrences in the other parts of the study area, yielding an overall deposit capture rate of 84.4%.
126. Recommendations for Low-Noise Data Acquisition with UAV-Mounted Multi-Channel Magnetometer Systems
Core Problem: The authors present the results of tests conducted with a state-of-the-art drone-based multi-channel magnetometer system (SENSYS MagDrone R4) developed for efficient high-resolution near-surface surveys.
Key Innovation: Based on these findings, practical recommendations for data acquisition and processing are proposed, contributing to the development of best-practice guidelines for high-resolution archeological, explosive ordnance (EO), and other near-surface magnetic survey applications. The primary aim is to advance the application of this technology in proximal sensing and to identify acquisition strategies capable of achieving data quality comparable to that of established ground-based magnetometer surveys.
127. Analysis of Diagnostic Absorption Troughs in Clay Alteration Within the Xiangshan Uranium Deposit Based on ZY1-02E Satellite Hyperspectral Imagery
Core Problem: Addressing the current shortcoming in hyperspectral alteration mapping—which primarily focuses on qualitative mineral identification while lacking systematic quantitative characterization of diagnostic absorption trough parameters—this study utilized ZY1-02E (Chinese Resource Satellite-1-02E) satellite hyperspectral imagery as the data source.
Key Innovation: The study advances the remote sensing identification of clay alteration from qualitative mapping to the quantitative analysis stage, providing a scientific basis for further exploration in the Xiangshan mineralized area.
128. Evolution of bound water in saturated clay under variable thermal paths: Insights from an improved specific gravity test
Core Problem: Bound water in saturated clay plays a critical role in governing the macroscopic thermo-mechanical behavior of clay.
Key Innovation: To address the limitations of conventional methods under non-isothermal conditions and the lack of research on thermal cycling effects, this study develops improved specific gravity tests to quantify bound water content in saturated clays under variable thermal paths.
129. Multiscale thermal damage evolution in sandstone and shale: A ternary framework linking bond dissociation, geochemical alteration, and crystallographic microstrain
Core Problem: However, the underlying mechanisms at the crystallographic and molecular levels remain poorly understood.
Key Innovation: The study proposes a multiscale approach that combines crystallographic microstrain, bond dissociation, and elemental silicate indices to explain the thermo-mechanical degradation of sandstone and shale samples subjected to temperatures ranging from 25 °C to 800 °C.
130. Architectural and environmental controls on long-term limestone weathering of coastal fortifications: The case of Famagusta (Cyprus)
Core Problem: Stone decay affecting coastal fortifications is commonly interpreted as the combined result of material properties and environmental exposure, yet the respective roles of lithology, architectural configurations and long-term system evolution remain difficult to disentangle in multi-phase monuments.
Key Innovation: The study combines archival and archaeological analysis, multiscale Structure-from-Motion (SfM) photogrammetry, surface-based photo-interpretation, non-destructive field measurements (Leeb hardness and water absorption) and targeted laboratory analyses to quantify and interpret limestone decay across Lusignan (ca.
131. Morphology and drivers of channel incision in a wet meadow-fen system in the Sangre de Cristo Mountains of New Mexico
Core Problem: Arroyo processes are widely studied in higher-order streams but remain understudied in montane headwater systems.
Key Innovation: This change has lowered water tables and drained wetlands, processes likely to continue so long as overbrowsing inhibits the establishment of woody species.
132. Rotation of borehole breakouts due to natural fractures: a case study based on image-log observations and numerical modeling
Core Problem: The in situ stress state, constrained by density logs, hydraulic fracturing tests, and borehole image data, indicates a stress regime near strike-slip faulting.
Key Innovation: These results indicate that borehole stress indicators in fractured rocks may record locally perturbed stresses rather than the far-field stress orientation, highlighting the need to account for fracture-controlled stress perturbations in stress interpretation.
133. KURVE: A web-based tool for estimating soil water retention and hydraulic conductivity curves using a k-nearest neighbor approach
Core Problem: Direct measurement of the soil water retention curve (WRC) and hydraulic conductivity curve (HCC) is expensive and time-consuming.
Key Innovation: The study introduces KURVE, a novel web-based tool that uses a recently published HYPROP-WP4C-based German reference dataset and a k-nearest neighbor approach to estimate complete WRCs and HCCs. For WRC prediction, KURVE achieved RMSE values as low as 0.061 cm³ cm⁻3 for the international dataset and 0.039 cm³ cm⁻3 for the California dataset, which was comparable to or slightly better than Rosetta-based models.
134. Development and optimization of a multifunctional sensor for measuring soil thermal properties, water retention characteristics and electrical conductivity
Core Problem: However, the measurements of the properties are affected by the temporal and spatial variability of soil due to employment of a variety of sensors, which hinders the research and modeling of coupled water, heat and solute transport.
Key Innovation: In addition, the laborious, costly and time-consuming sensor optimization is always a challenge for traditional sensor development. Our results show that the optimal radius and length of the ceramic are 18 mm and 40 mm, respectively, and the optimal rod length extended out of the ceramic is 50 mm.
135. Separable surrogates for DEM-derived elastic moduli of Hertz–Mindlin and Cundall–Strack granular packings
Core Problem: Discrete-element-method (DEM) simulations provide controlled data for calibrating small-strain stiffness laws, but contact-law comparisons remain sensitive to inconsistent state support and to over-interpreted single-variable pressure exponents.
Key Innovation: The study develops a three-variable separable interpolation surrogate for the early-loading tangent moduli of Hertz-Mindlin (HM) and Cundall-Strack (CS) granular packings. Design-level leave-one-design-out validation shows strong transfer across ϕ 0 and σ iso, while transfer across E p is weakest.
136. Experimental study on dynamic mechanical behaviors and damage evolution of limestone under true triaxial stress states
Core Problem: Accurate prediction of the mechanical response and stability of bedded limestone under such extreme conditions remains challenging due to the paucity of studies on dynamic loading under true triaxial conditions, particularly the role of minimum principal stress (σ 3).
Key Innovation: These findings provide experimental evidence and theoretical for understanding the dynamic instability of bedded limestone and for improving support design in deep tunnels subjected to high in-situ stress and dynamic disturbances.
137. Fracture behavior of thermally treated layered sandstone with a bedding-parallel crack under different loading modes
Core Problem: Understanding the fracture behavior of thermally treated layered sandstone is crucial for deep resource exploitation and tunnel excavation design.
Key Innovation: A cracked straight-through Brazilian disc (CSTBD) test under mixed mode was carried out on thermally treated layered sandstone. The findings demonstrate that temperature markedly controls the load and fracture mode of layered sandstone.
138. Influence of geomechanical loading configuration on modeling stress-dependent flow and transport in 3D fractured rocks
Core Problem: However, their effects on flow and transport in three-dimensional fractured rock systems have not been systematically investigated, creating uncertainty in stress-dependent hydraulic response.
Key Innovation: The study systematically examines the influence of different mechanical BCs on deformation, flow and transport in fracture networks. Results show that for systems with small fractures, BCs have negligible influence on flow and transport.
139. Simulation of rock specimens with double elliptical defects under uniaxial compression using non-ordinary state-based peridynamics
Core Problem: The deterioration, propagation, and coalescence of internal defects in rock masses under loading are primary causes of engineering rock failure and catastrophic events.
Key Innovation: An improved non-ordinary state-based peridynamics (NOSB-PD) method is utilized to investigate the failure evolution of defective rock specimens, with pre-existing flaws modeled as elliptical discontinuities.