Robust low frequency seismic bandwidth extension with a U-net and synthetic training data
June 2025
This work focuses on enhancing low frequency seismic data using a convolutional neural network trained on synthetic data. Traditional seismic data often lack both high and low frequencies, which are...
Microseismic moment tensor inversion based on ResNet model
June 2025
This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it...
Enhancing understanding of 3D rectangular tunnel heading stability in c-φ soils with surcharge loading: A comprehensive FELA analysis using three stability factors and machine learning
June 2025
This study examines the stability of three-dimensional rectangular tunnel headings in drained c-ϕ soils, incorporating surcharge effects using 3D Finite Element Limit Analysis (FELA). It focuses on...
Fast 2D forward modeling of electromagnetic propagation well logs using finite element method and data-driven deep learning
June 2025
We propose a novel workflow for fast forward modeling of well logs in axially symmetric 2D models of the near-wellbore environment. The approach integrates the finite element method with deep residual...
Thank you reviewers!
June 2025
An intelligent recognition method of deep shale gas reservoir laminaset based on laminaset clustering and R-L-M algorithm
June 2025
Lamina structures, as typical sedimentary features in shale formations, determine both the quality of shale reservoirs and fracturing effects. In this study, through electric imaging logging, based...
Digital core reconstruction of tight carbonate rocks based on SliceGAN
June 2025
The pore structures of the Majiagou Formation in the Ordos Basin are complex, featuring micro- and nano-scale intra-crystalline and inter-crystalline pores that significantly impact hydrocarbon storage...
LatentPINNs: Generative physics-informed neural networks via a latent representation learning
June 2025
Physics-informed neural networks (PINNs) are promising to replace conventional mesh-based partial differential equation (PDE) solvers by offering more accurate and flexible PDE solutions. However, PINNs...
Identification of interlayer and connectivity analysis based on machine learning and production data: A case study from M oilfield
June 2025
Interlayer is an important factor affecting the distribution of remaining oil. Accurate identification of interlayer distribution is of great significance in guiding oilfield production and development....
Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters
June 2025
Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. ROP decreases drilling time and costs and increases rig productivity. This study employs random forest (RF),...
Automatic description of rock thin sections: A web application
June 2025
The identification and characterization of rock types is a core activity in geology and related fields, including mining, petroleum, environmental science, industry, and construction. Traditionally,...
Soil liquefaction assessment using machine learning
June 2025
Liquefaction is one of the prominent factors leading to damage to soil and structures. In this study, the relationship between liquefaction potential and soil parameters is determined by applying feature...
A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale
June 2025
Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental...
Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping
June 2025
The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy...
A staged deep learning approach to spatial refinement in 3D temporal atmospheric transport
June 2025
High-resolution spatiotemporal simulations effectively capture the complexities of atmospheric plume dispersion in complex terrain. However, their high computational cost makes them impractical for...
Self-supervised multi-stage deep learning network for seismic data denoising
June 2025
Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow, as our ability to mitigate noise in seismic data affects the quality of our subsequent...
Automatic classification of Carbonatic thin sections by computer vision techniques and one-vs-all models
June 2025
Convolutional neural networks have been widely used for analyzing image data in industry, especially in the oil and gas area. Brazil has an extensive hydrocarbon reserve on its coast and has also benefited...
Deep learning based identification of rock minerals from un-processed digital microscopic images of undisturbed broken-surfaces
June 2025
This study employed convolutional neural networks (CNNs) for the classification of rock minerals based on 3179 RGB-scale original microstructural images of undisturbed broken surfaces. The image dataset...
Enhancing microseismic event detection with TransUNet: A deep learning approach for simultaneous pickings of P-wave and S-wave first arrivals
June 2025
Microseismic monitoring is essential for understanding subsurface dynamics and optimizing oil and gas production. However, traditional methods for the automatic detection of microseismic events rely...
Thank you reviewers!
December 2024
Exploring emerald global geochemical provenance through fingerprinting and machine learning methods
December 2024
Emeralds – the green colored variety of beryl – occur as gem-quality specimens in over fifty deposits globally. While digital traceability methods for emerald have limitations, sample-based approaches...
When linear inversion fails: Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice
December 2024
In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation...
Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania
December 2024
This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray...
A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images
December 2024
Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and geochemical...
Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology
December 2024
Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relying...