Deep learning enhanced crack identification on rocks
June 2026
This study focuses on enhancing crack identification in rock surfaces using deep learning techniques. The research proposes a novel convolutional architecture to achieve pixel-level classification for...
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...
SeisReconNO: Leveraging a U-Net-Enhanced Fourier neural operator for 3D seismic reconstruction
June 2026
Missing traces in 3D seismic data are a recurring challenge caused by receiver malfunctions, acquisition limitations, and geological or environmental constraints. These gaps hinder accurate interpretation...
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...
Geo-foundation models and UAV data for post flooding damage assessment in Mozambique
June 2026
Earth Observation (EO) systems combined with Artificial Intelligence (AI) techniques have significantly advanced in recent years. The emergence and success of foundational models (FMs), such as ChatGPT...
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...
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,...
Enhancing land cover semantic segmentation with convolutional block attention modules and deep supervision
June 2026
High-resolution land cover semantic segmentation is challenged by strong class imbalance, spatial fragmentation of minority classes, and the presence of fine-scale textures and sensor noise that can...
Research and insights into the impact of different sampling strategies on machine learning-based lithology identification using well logging data
June 2026
Lithology identification is a critical task in geological exploration and mineral resource development, where accurate classification plays a pivotal role in geological modeling and resource evaluation....
Efficient specialization of foundation vision models for urban land cover classification
June 2026
Rapid urban expansion poses significant challenges for land use planning, spanning infrastructure provision to environmental monitoring. Accurate and detailed classification of urban land cover (ULC)...
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...
Spatial intersection of soil texture classes and landscape features: An XGBoost-enhanced digital soil mapping approach
Available online 12 June 2026
Soil texture classes (STCs) can be digitally mapped by first estimating particle-size fractions (PSFs). This study focuses on developing an extreme gradient boosting (XGBoost) model to estimate the...
Estimation of interval P-wave velocities from Dix slowness using implicit neural representation
Available online 11 June 2026
Mapping time-migration velocities to depth-domain interval velocities in the presence of lateral variation is important in seismic exploration. The first step of this process, computing Dix velocities...
Vision-language models for automated carbonate petrography and depositional environment interpretation
Available online 5 June 2026
Carbonate petrographic analysis provides essential qualitative and semi-quantitative constraints on depositional environments and diagenetic evolution at microscale. However, conventional thin-section...
Optimizing the Potential of Iterative Bilateral Proposed U-Net for Advanced Forest Segmentation Techniques
Available online 22 May 2026
Globally, advanced forest segmentation methods are essential for optimal environmental monitoring, managing resources and ecological studies. As, these techniques uses high-resolution satellite and...