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ISSN: 2666-5441

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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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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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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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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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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Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical...

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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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Capsule network-based approach for estimating grassland coverage using time series data from enhanced vegetation index

The degradation and desertification of grasslands pose a daunting challenge to China's arid and semiarid areas owing to the increasing demand for them in light of the rise of animal husbandry. Monitoring...

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Near-surface velocity inversion from Rayleigh wave dispersion curves based on a differential evolution simulated annealing algorithm

The utilization of urban underground space in a smart city requires an accurate understanding of the underground structure. As an effective technique, Rayleigh wave exploration can accurately obtain...

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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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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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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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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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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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Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China

A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon detection...

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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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Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China

The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded...

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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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Unilateral Alignment: An interpretable machine learning method for geophysical logs calibration

Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue, so the trained model cannot well generalize to the unseen data without calibrating...

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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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Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo

The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically...

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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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Application of neural network to speed-up equilibrium calculations in compositional reservoir simulation

Compositional reservoir simulation is an important tool to model fluid flow in oil and gas reservoirs. Important investment decisions regarding oil recovery methods are based on simulation results,...

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