Feature-data collaborative inversion: A Siamese convolutional neural network method for shallow-subsurface EFWI
September 2026
Elastic full-waveform inversion (EFWI) constitutes a vital tool for high-resolution subsurface imaging. However, its application in shallow-subsurface exploration is hindered by strong nonlinearity,...
Stability prediction of footings on slopes with dense sand using Bolton model, FELA, XGBoost, Random Forest, and Evolutionary Polynomial Regression
September 2026
This study investigates the bearing-capacity factor (Nγ) of rigid strip footings placed on dense sand slopes by integrating finite element limit analysis (FELA) with machine learning and a symbolic...
Deep hybrid vision transformers for improved landslide mapping in geospatial remote sensing
September 2026
Landslide identification using automated techniques helps researchers improve the accuracy of state-of-the-art landslide prediction models. In recent years, convolutional neural networks (CNNs) have...
iGeochem cloud: A cyber-physical framework for intelligent and automated geochemical laboratories
September 2026
Geochemistry plays a central role in addressing planetary-scale challenges, yet its scientific practice remains constrained by fragmented, artisanal workflows that limit reproducibility, throughput,...
PSO–CS approach and decomposition methods for corn prices forecasting
September 2026
This study aims to evaluate various approaches for forecasting maize prices by applying individual and ensemble models, both with and without time series decomposition techniques. In the first phase,...
AOTEM: An efficient adaptive octree embedding solver for airborne transient electromagnetic response
September 2026
Conventional numerical approaches to forward modeling in 3D airborne transient electromagnetic (ATEM) surveys are constrained by limited computational efficiency and prohibitively high costs, which...
Hybrid machine learning framework for horizontal site amplification factors estimation using H/V of microtremors and earthquakes
September 2026
Reliable estimation for the horizontal seismic site amplification factors (HSAF) is an essential step in the deterministic seismic hazard analyses. The current study introduces newly hybrid machine...
MARL-PINN: Solving the Eikonal equation with multi-agent reinforcement learning method for physics-informed neural networks
September 2026
The initial arrival time of seismic waves serves as crucial data in seismic information, efficiently depicting underground velocity structures. The Eikonal equation, a nonlinear partial differential...
Biophysical indicator-based assessment of climate change severity in the Upper East Region, Ghana
September 2026
Climate change has continued to be a pressing concern, particularly for climate-vulnerable regions around the world, including Sub-Saharan Africa. While climate change is occurring at a rapid rate,...
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June 2026
Unlocking the potential of legacy data for future geoenergy and storage applications: Porosity and permeability prediction based on machine learning applied to petrographic data
June 2026
Machine learning techniques are increasingly applied in geological research and widely adopted in industry. However, one commonly available dataset remains underutilized: petrographic data from classical...
A zero-training framework for facies classification using transformer-based vector embeddings
June 2026
Efficient subsurface drilling operations require rapid classification of changing lithology and facies for casing point selection, adjusting drilling fluid, and optimizing surface parameters. We present...
A deep learning based workflow for multicomponent seismic data registration
June 2026
Multicomponent seismic datasets, such as PS (downgoing P-wave and upgoing S-wave), offer significant advantages over conventional PP (downgoing and upgoing P-wave) data for subsurface characterization....
Downscaling of Landsat LST with HotSat-1 data and generative adversarial networks
June 2026
Land Surface Temperature (LST) significantly affects the Earth's energy balance, making it vital for various environmental and scientific studies. Currently, the highest-resolution satellite-based LST...
A hybrid ensemble deep learning model for advanced time series rainfall forecasting using satellite data and climate variability analysis
June 2026
Accurate rainfall prediction is important for climate adaptation, managing water resources, and planning for farming in dry areas and places where data is difficult to obtain. By collecting long-term...
Optimized LightGBM-based prediction of foundation bearing capacity on spatially variable Bolton sand
June 2026
This study examines the random bearing capacity factor (Nγran) of shallow foundations on spatially variable Bolton sand using random field theory, adaptive meshing technique, and finite element limit...
Stress release coefficient prediction of sandy-gravel soil by extra tree algorithms
June 2026
One of the most significant geotechnical issues that must be addressed during the dams' first impoundment phase is the collapse settling of embankment dams. Due to the fact that it leads dams to undergo...
An open benchmark dataset of synthetic seismic data and real swell noise for evaluating deep learning denoising models
June 2026
Recent advances in deep learning (DL) have been fostered by open benchmark datasets that allow reproducible and systematic evaluation of models. Despite the increasing adoption of DL methods in geophysics,...
From linear regression to hybrid networks: A comparative evaluation to find the optimal drought forecasting model for Iran
June 2026
Accurate and timely drought forecasting is a strategic imperative for Iran's national security, given the escalating water crisis. This necessity has driven the scientific community toward leveraging...
GIS-based wildfire prediction model in Indonesia using stacking ensemble learning
June 2026
In Indonesia, wildfires have become an annual disaster that results in significant losses across various aspects of life, including ecological, social, and economic conditions. To minimize these losses,...
Deep learning with Fourier Neural Operators for sedimentary structure recognition
June 2026
Sedimentary structure classification is fundamental to facies interpretation, depositional environment reconstruction, and reservoir characterization. While Convolutional Neural Networks (CNNs) have...
Deep learning-based downscaling of ERA5-Land temperature to 250 m resolution over the Trentino–South Tyrol Alpine region
June 2026
High-resolution near-surface temperature data are essential in mountainous regions, where complex topography induces strong spatial and temporal variability. However, coarse-resolution reanalysis products...
Machine learning and ensemble learning models for groundwater potential mapping in a fractured basin: Case of the Azrou-Khenifra basin, central massif, Morocco
June 2026
Groundwater potential mapping (GWP) is essential for sustainable resource managmment, particlarly in semi-arid and data-scarce regions. This study aims to delineate groundwater potential zones in the...
Predicting undrained shear strength in marine sediments using a physics-informed neural network (PINN)
June 2026
Undrained shear strength (SU) is a key parameter for evaluating slope stability, offshore foundation design, and submarine geohazards in marine environments. Conventional methods for predicting SU often...
Scalable variational Gaussian process framework for implicit geological modelling and compositional grade interpolation
June 2026
Geological modelling and estimation of polymetallic ore grades require methods that simultaneously honour spatial heterogeneity, compositional constraints, and predictive uncertainty. We present a scalable...
