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Artificial Intelligence in Geosciences
Scopus
ISSN 2666-5441 · KeAi Communications Co. Ltd. · 170 artikel di indeks
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Artikel Jurnal
Fast sparse representation impedance inversion method based on online adaptive reservoir characterization
Seismic impedance inversion is a key technique for extracting reservoir information from seismic data. Traditional model-driven inversion methods often prove inadequate when dealing with complex reservoirs, which has led to the growing adoption of data-driven sparse representation constrained…
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A data-driven approach to earthquake early warning: Multicomponent site-spectra prediction using deep neural networks
This paper presents a hybrid deep learning framework for earthquake early warning (EEW) that leverages front-site observations to predict target-site spectral characteristics—specifically Fourier amplitude spectra (FAS) and 5% damped pseudo-velocity response spectra (pSᵥ) in real time. In its…
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Seismic facies characterization: Integrated subsurface-outcrop analysis for complex depositional systems in northeast India
Seismic facies analysis involves the interpretation of reflection patterns from seismic data to provide insights into subsurface sedimentary environments, depositional processes, and lithological variations, aiding georesources exploration. This study evaluates unsupervised machine learning (ML)…
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Spatial mapping and modelling of soil organic carbon using random forest and remote sensing variables in part of Kaduna, Northern Nigeria
Reliable and up-to-date digital soil data is crucial for achieving Sustainable Development Goal 13 (Climate Action) by enabling improved monitoring of soil carbon and land degradation, thereby supporting climate-smart agriculture and ensuring stable crop yields in sub-Saharan Africa. This study…
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Enhancing model parameterization with linearly constrained deep generative network for ensemble-based history matching
Ensemble-based data assimilation methods have been widely used for history matching in subsurface reservoir modeling, but struggle to handle the complex nonlinear and non-Gaussian behaviors prevalent in real field applications. To address these limitations, this paper introduces a deep generative…
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A hybrid unsupervised-supervised deep learning framework for sandstone thickness prediction from seismic data
Accurate sandstone thickness prediction from seismic data is vital for reservoir characterization and well placement optimization. However, conventional deep learning methods are often hindered by inefficient sequential processing or excessive computational costs when handling long seismic traces…
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DeepSeg-based noise reduction algorithm trained on a hybrid synthetic dataset for signals from acoustic logging-while-drilling
Acoustic logging-while-drilling (ALWD) enables real-time acoustic measurements during drilling operations. However, challenging downhole conditions introduce considerable noise into ALWD signals. This study applied a numerical method to simulate clean ALWD array waveforms. A synthetic noisy dataset…
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Machine learning-driven permeability prediction in carbonates and sandstones using NMR relaxation data
Nuclear Magnetic Resonance (NMR) has proven to be a powerful tool for in-situ permeability quantification however, it typically requires laboratory calibration, and its accuracy is strongly influenced by rock type and pore system heterogeneity. Existing NMR-based permeability studies are often…
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Application of YOLOv11 deep learning model for classification and counting ice-rafted debris (IRD) in core sediments in the Arctic Ocean
The classification and quantification of ice-rafted debris (IRD) in marine sediments are key to reconstructing glacial-interglacial dynamics and sediment provenance. However, traditional IRD analysis, based on manual grain identification under binocular microscopes, is time-consuming and dependent…
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Remote sensing estimation of rice chlorophyll content based on UAV image feature selection and PSO-optimized ensemble learning
Chlorophyll content is one crucial indicator of evaluating crop growth and physiological status. Rapid, accurate, and large-scale monitoring of chlorophyll content is vital for the precise diagnosis of crop nutritional status and developmental dynamics. In this study, rice chlorophyll content was…
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Explainable flood damage assessment using multi-atrous self-attention and vision-language integration
Flood disasters triggered by excessive rainfall cause severe damage to infrastructure and pose significant risks to human life. Within the context of disaster management, accurately identifying affected structures and providing interpretable analytical results are of critical importance. This study…
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Application of machine learning for permeability prediction in heterogeneous carbonate reservoirs
Accurate prediction of reservoir permeability based on geostatistical modeling and history matching is often limited by spatial resolution and computational efficiency. To address this limitation, we developed a novel supervised machine learning (ML) approach employing feedforward neural networks…
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Artikel Jurnal
The Fossil Frontier: An answer to the 3-billion fossil question
Microfossil analysis is important in subsurface mapping, for example to match strata between wells. This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera. The…
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An FCM-based microseismic phase arrival picking method and application
Artificial intelligence-based methods for picking microseismic phase arrivals have been widely adopted. However, these methods are frequently challenged by complex and dynamic monitoring scenarios, where various types of environmental noise mask low-energy microseismic signals. Moreover, the…
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Artikel Jurnal
DTPP:An efficient depthwise separable TCN for seismic phase picking
With the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years. However, existing models face challenges in balancing picking accuracy with computational efficiency for real-time applications. To address…
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Artikel Jurnal
An adaptable hybrid method for lossless airborne lidar data compression
Light Detection and Ranging (LIDAR) point clouds provide high precision spatial data but impose significant storage and transmission challenges, often exceeding one gigabyte per square kilometer. This paper introduces a novel hierarchical framework for lossless LiDAR data compression, designed to…
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Enhancing fault detection using CHRRA-Unet and focal loss functions for imbalanced data: A case study in Luoping county, Yunnan, China
Recent advancements in remote sensing technology have made it easier to detect surface faults. Deep learning, especially convolutional models, offers new potential for automatic fault detection from remote sensing imagery. However, these models often struggle with segmentation accuracy due to their…
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Artikel Jurnal
Hierarchical machine learning for the automatic classification of surface deformation from SAR observations
Ground deformation processes, such as landslides and subsidence, cause significant social, economic, and environmental impacts. This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets. The database integrates…
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Prediction of the soil–water retention curve of compacted clays using PSO–GA XGBoost
Soil–water retention (SWR) is fundamental for understanding the hydro-mechanical behavior of unsaturated clay soils. The soil–water retention curve is typically obtained through extensive and costly laboratory testing. To offer a more efficient alternative, an extreme gradient boosting (XGBoost)…
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Artikel Jurnal
Recent advances and challenges of cement bond evaluation based on ultrasonic measurements in cased holes
Cement bond quality evaluations are essential for assessing zonal isolation between formation strata, providing crucial information for ensuring environmental and ecological safety in oil and gas exploitation, geothermal energy injection and geological carbon dioxide sequestration. In the past…