Convolutional sparse coding network for sparse seismic time-frequency representation
June 2025
Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently,...
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Loosening rocks detection at Draa Sfar deep underground mine in Morocco using infrared thermal imaging and image segmentation models
June 2025
Rockfalls are among the frequent hazards in underground mines worldwide, requiring effective methods for detecting unstable rock blocks to ensure miners' and equipment's safety. This study proposes...
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Optimizing zero-shot text-based segmentation of remote sensing imagery using SAM and Grounding DINO
June 2025
The use of AI technologies in remote sensing (RS) tasks has been the focus of many individuals in both the professional and academic domains. Having more accessible interfaces and tools that allow people...
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Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases
June 2025
The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently. Recently, deep learning algorithms exhibit a powerful capability of detecting...
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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process
June 2025
Geotechnical parameters derived from an intrusive cone penetration test (CPT) are used to asses mechanical properties to inform the design phase of infrastructure projects. However, local, in situ 1D...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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....
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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),...
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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,...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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On the application of machine learning algorithms in predicting the permeability of oil reservoirs
Available online 3 June 2025
Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used...
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Thank you reviewers!
Available online 3 April 2025
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A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India
December 2024
In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved...
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