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...
Spatial intersection of soil texture classes and landscape features: An XGBoost-enhanced digital soil mapping approach
September 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...
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,...
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,...
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....
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...
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...
Thank you reviewers!
June 2026