Optimizing the potential of iterative bilateral proposed U-Net for advanced forest segmentation techniques
September 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...
Vision-language models for automated carbonate petrography and depositional environment interpretation
September 2026
Carbonate petrographic analysis provides essential qualitative and semi-quantitative constraints on depositional environments and diagenetic evolution at microscale. However, conventional thin-section...
Estimation of interval P-wave velocities from Dix slowness using implicit neural representation
September 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...
Joint time–space–depth feature learning for multimodal velocity estimation from array acoustic logging waveforms
September 2026
Accurate velocity estimation from array acoustic logging in unconventional hydrocarbon reservoirs is frequently hindered by complex wavefield interference, limited vertical resolution in thinly interbedded...
Simultaneous estimation of hyperparameters and basement depth in gravity data inversion using the JAYA algorithm
September 2026
Basement relief depth estimation is an important component in characterizing sedimentary basins, which serve as essential reservoir for geothermal energy, groundwater, and hydrocarbons. While residual...
Source-independent transfer learning for intelligent post-stack seismic inversion
September 2026
Post-stack seismic inversion plays a crucial role in the quantitative interpretation of reservoirs. Intelligent inversion approaches employ neural networks to characterize the relationship between observed...
Explainable AI for microseismic event detection
September 2026
Deep neural networks like PhaseNet show high accuracy in detecting microseismic events, but their black-box nature is a concern in critical applications. We apply Explainable Artificial Intelligence...
HCEC: An effective hybrid CNN-ensemble classifier for hyperspectral image classification
September 2026
Hyperspectral image classification (HSI) is extensively utilized to analyze remotely sensed images for different real-world applications. Recently, convolutional neural network(CNN) have been applied...
A hybrid Prophet–LSTM–BPNN framework for seasonal drought prediction
September 2026
Drought is among the most destructive hydroclimatic hazards, particularly in arid and semi-arid regions, where water scarcity directly threatens agricultural production and socioeconomic stability....
Neural architecture search for fully connected networks: Comparing reinforcement learning algorithms with Neural Tangent Kernel-based proxy – A case study on geochemical parameter estimation from wireline logs
September 2026
This work presents a comparative study of twelve reinforcement learning (RL) algorithms for training-free neural architecture search (NAS) of fully connected neural networks (FCNNs) with skip connections....
Deep learning preconditioned methods for frequency-domain acoustic wave equation with their wavefield simulations
September 2026
To overcome the low efficiency of performing frequency-domain acoustic wavefield simulation, this paper proposes a kind of deep learning (DL) preconditioned methods based on sufficiently training deep...
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,...
Deep Hybrid Vision Transformers for Improved Landslide Mapping in Geospatial Remote Sensing
Available online 7 July 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...
Stability prediction of footings on slopes with dense sand using Bolton model, FELA, XGBoost, Random Forest, and Evolutionary Polynomial Regression
Available online 6 July 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...
Thank you reviewers!
June 2026
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...
