Estimating relative diffusion from 3D micro-CT images using CNNs
December 2023
In recent years, convolutional neural networks (CNNs) have demonstrated their effectiveness in predicting bulk parameters, such as effective diffusion, directly from pore-space geometries. CNNs offer...
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Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks: A preliminary investigation
December 2023
The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can...
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A new correlation for calculating wellhead oil flow rate using artificial neural network
December 2022
A separator and multiphase flow meters are considered the most accurate tools used to measure the surface oil flow rates. However, these tools are expensive and time consuming. Thus, this study aims...
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A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil
December 2022
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being...
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Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region
December 2022
The aim of the current work is to compare susceptibility maps of landslides produced using machine learning techniques i.e. multilayer perception neural nets (MLP), kernel logistic regression (KLR),...
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Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach
December 2022
Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space...
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PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms
December 2022
The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming...
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Synthetic shear sonic log generation utilizing hybrid machine learning techniques
December 2022
Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability mainly...
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Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager
December 2022
Most known mineral deposits were discovered by accident using expensive, time-consuming, and knowledge-based methods such as stream sediment geochemical data, diamond drilling, reconnaissance geochemical...
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A study on small magnitude seismic phase identification using 1D deep residual neural network
December 2022
Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage,...
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Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns
December 2022
In exploration geochemistry, advances in the detection limit, breadth of elements analyze-able, accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve...
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MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
December 2022
Among the biggest challenges we face in utilizing neural networks trained on waveform (i.e., seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement for accurate...
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Attenuation of seismic migration smile artifacts with deep learning
December 2022
Attenuation of migration artifacts on Kirchhoff migrated seismic data can be challenging due to the relatively low amplitude of migration artifacts compared to reflections as well as the overlap in...
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Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence
December 2022
Machine learning (ML) to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields. Meandering, braided...
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ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature
December 2022
Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering the...
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Machine learning in petrophysics: Advantages and limitations
December 2022
Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer...
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Geostatistical semi-supervised learning for spatial prediction
December 2022
Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms. Typically, the target variable...
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Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India
December 2022
Land suitability analysis (LSA) is an evaluation method that measures the degree to which land is suitable for certain land use. The primary aims of this study are to identify potentially viable agricultural...
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High resolution pre-stack seismic inversion using few-shot learning
December 2022
We propose to use a Few-Shot Learning (FSL) method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data. Recently, artificial neural...
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Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning
December 2022
Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning...
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Near-surface velocity estimation using shear-waves and deep-learning with a U-net trained on synthetic data
December 2022
Estimation of good velocity models under complex near-surface conditions remains a topic of ongoing research. We propose to predict near-surface velocity profiles from surface-waves transformed to phase...
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Thank you reviewers!
December 2022
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A study on geological structure prediction based on random forest method
December 2022
The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt, which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt....
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Wavefield solutions from machine learned functions constrained by the Helmholtz equation
December 2021
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield solutions...
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Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm
December 2021
The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage. However, due to the serious nonlinear relationship between the logging...
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