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ISSN: 2666-5441

Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource...

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Deriving big geochemical data from high-resolution remote sensing data via machine learning: Application to a tailing storage facility in the Witwatersrand goldfields

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues...

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Models of plate tectonics with the Lattice Boltzmann Method

Modern geodynamics is based on the study of a large set of models, with the variation of many parameters, whose analysis in the future will require Machine Learning to be analyzed. We introduce here...

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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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Blockly earthquake transformer: A deep learning platform for custom phase picking

Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance...

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Toward earthquake early warning: A convolutional neural network for repaid earthquake magnitude estimation

Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake magnitude,...

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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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Machine learning in petrophysics: Advantages and limitations

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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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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Optimization of shale gas fracturing parameters based on artificial intelligence algorithm

Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale...

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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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2D magnetotelluric inversion based on ResNet

In this study, a deep learning algorithm was applied to two-dimensional magnetotelluric (MT) data inversion. Compared with the traditional linear iterative inversion methods, the MT inversion method...

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Developing soft-computing regression model for predicting bearing capacity of eccentrically loaded footings on anisotropic clay

In this investigation, the bearing capacity solution of a strip footing in anisotropic clay under inclined and eccentric load is analyzed using the numerical simulation model. The lower and upper bound...

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Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage...

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Machine learning elucidates the anatomy of buried carbonate reef from seismic reflection data

A carbonate build-up or reef is a thick carbonate deposit consisting of mainly skeletal remains of organisms that can be large enough to develop a favourable topography. Delineation of such geologic...

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Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach,...

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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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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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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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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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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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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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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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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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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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