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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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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Reconstruction of lithofacies using a supervised Self-Organizing Map: Application in pseudo-wells based on a synthetic geologic cross-section
December 2024
Recently, machine learning (ML) has been considered a powerful technological element of different society areas. To transform the computer into a decision maker, several sophisticated methods and algorithms...
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Thank you reviewers!
December 2024
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Locally varying geostatistical machine learning for spatial prediction
December 2024
Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods,...
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Reservoir evaluation using petrophysics informed machine learning: A case study
December 2024
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical information...
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Robust high frequency seismic bandwidth extension with a deep neural network trained using synthetic data
December 2024
Geophysicists interpreting seismic reflection data aim for the highest resolution possible as this facilitates the interpretation and discrimination of subtle geological features. Various deterministic...
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Prediction of seismic-induced bending moment and lateral displacement in closed and open-ended pipe piles: A genetic programming approach
December 2024
Ensuring the reliability of pipe pile designs under earthquake loading necessitates an accurate determination of lateral displacement and bending moment, typically achieved through complex numerical...
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ASTER data processing and fusion for alteration minerals and silicification detection: Implications for cupriferous mineralization exploration in the western Anti-Atlas, Morocco
December 2024
Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration....
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Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches
December 2024
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. This...
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The 3-billion fossil question: How to automate classification of microfossils
December 2024
Microfossil classification is an important discipline in subsurface exploration, for both oil & gas and Carbon Capture and Storage (CCS). The abundance and distribution of species found in sedimentary...
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Application of ChatGPT in soil science research and the perceptions of soil scientists in Indonesia
December 2024
Since its arrival in late November 2022, ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned, conducted, and published using a generative artificial intelligence...
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The role of artificial intelligence and IoT in prediction of earthquakes: Review
December 2024
Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment, lives, and properties. There has been an increasing interest in the...
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Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in
December 2024
Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, companies...
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High-resolution seismic inversion method based on joint data-driven in the time-frequency domain
December 2024
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance compared...
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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...
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Water resource forecasting with machine learning and deep learning: A scientometric analysis
December 2024
Water prediction plays a crucial role in modern-day water resource management, encompassing both hydrological patterns and demand forecasts. To gain insights into its current focus, status, and emerging...
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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...
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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...
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EQGraphNet: Advancing single-station earthquake magnitude estimation via deep graph networks with residual connections
December 2024
Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this context,...
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Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data
December 2024
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentation. Recently developed...
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Enhanced permeability prediction in porous media using particle swarm optimization with multi-source integration
December 2024
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the effectiveness...
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Transfer learning for well logging formation evaluation using similarity weights
December 2024
Machine learning has been widely applied in well logging formation evaluation studies. However, several challenges negatively impacted the generalization capabilities of machine learning models in practical...
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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...
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Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows
December 2024
Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when...
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