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ISSN: 2666-5441

Artificial intelligence-based anomaly detection of the Assen iron deposit in South Africa using remote sensing data from the Landsat-8 Operational Land Imager

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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The benefits and dangers of using artificial intelligence in petrophysics

Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petrophysical...

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A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil

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

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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A new correlation for calculating wellhead oil flow rate using artificial neural network

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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Advanced geochemical exploration knowledge using machine learning: Prediction of unknown elemental concentrations and operational prioritization of Re-analysis campaigns

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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PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

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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Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

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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Synthetic shear sonic log generation utilizing hybrid machine learning techniques

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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MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

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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Deep convolutional autoencoders as generic feature extractors in seismological applications

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea...

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Flood susceptibility assessment using artificial neural networks in Indonesia

Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments,...

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Wavefield solutions from machine learned functions constrained by the Helmholtz equation

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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Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic...

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Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale

Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publicly-available...

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A study on small magnitude seismic phase identification using 1D deep residual neural network

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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The potential of self-supervised networks for random noise suppression in seismic data

Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural networks...

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Towards a fully data-driven prospectivity mapping methodology: A case study of the Southeastern Churchill Province, Québec and Labrador

Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal...

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Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and Machine learning algorithms in Kaliyaganj C.D. block, India

The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D. Block using the Analytic Hierarchy Process (AHP) and Machine learning algorithms along with...

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Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs

Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR) data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such attributes...

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Artificial Intelligence in Geosciences

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Attenuation of seismic migration smile artifacts with deep learning

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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Classification random forest with exact conditioning for spatial prediction of categorical variables

Machine learning methods are increasingly used for spatially predicting a categorical target variable when spatially exhaustive predictor variables are available within the study region. Even though...

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Random forest for spatial prediction of censored response variables

The spatial prediction of a continuous response variable when spatially exhaustive predictor variables are available within the region under study has become ubiquitous in many geoscience fields. The...

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Rapid identification of high-quality marine shale gas reservoirs based on the oversampling method and random forest algorithm

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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