Artificial Intelligence in Geosciences
Latest Publications

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15

(FIVE YEARS 15)

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Published By Elsevier BV

2666-5441

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.11.003 ◽

2021 ◽

Author(s):

Francky Fouedjio

Keyword(s):

Random Forest ◽

Spatial Prediction ◽

Categorical Variables

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Deep convolutional autoencoders as generic feature extractors in seismological applications

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.12.002 ◽

2021 ◽

Author(s):

Qingkai Kong ◽

Andrea Chiang ◽

Ana C. Aguiar ◽

M. Giselle Fernández-Godino ◽

Stephen C. Myers ◽

...

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Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.08.003 ◽

2021 ◽

Vol 2 ◽

pp. 20-25

Author(s):

Xinze Li ◽

Bangyu Wu ◽

Guofeng Liu ◽

Xu Zhu ◽

Linfei Wang

Keyword(s):

Convolutional Network ◽

Deep Convolutional Network

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Wavefield solutions from machine learned functions constrained by the Helmholtz equation

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.08.002 ◽

2021 ◽

Vol 2 ◽

pp. 11-19

Author(s):

Tariq Alkhalifah ◽

Chao Song ◽

Umair bin Waheed ◽

Qi Hao

Keyword(s):

Helmholtz Equation

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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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.11.002 ◽

2021 ◽

Author(s):

Steven E. Zhang ◽

Glen T. Nwaila ◽

Julie E. Bourdeau ◽

Lewis D. Ashwal

Keyword(s):

Machine Learning ◽

Trace Element ◽

Large Igneous Province ◽

Element Concentrations ◽

Igneous Province ◽

Prospectivity Mapping

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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.12.001 ◽

2021 ◽

Author(s):

Linqi Zhu ◽

Xueqing Zhou ◽

Chaomo Zhang

Keyword(s):

Random Forest ◽

Shale Gas ◽

Rapid Identification ◽

Random Forest Algorithm ◽

Gas Reservoirs ◽

High Quality ◽

Marine Shale ◽

Shale Gas Reservoirs

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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.11.001 ◽

2021 ◽

Author(s):

Claire Birnie ◽

Matteo Ravasi ◽

Sixiu Liu ◽

Tariq Alkhalifah

Keyword(s):

Seismic Data ◽

Noise Suppression ◽

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

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.08.001 ◽

2021 ◽

Author(s):

Yaqi Sun ◽

Hailong Liu ◽

Zhengqiang Guo

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Series Data ◽

Enhanced Vegetation Index

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The Benefits and Dangers of using Artificial Intelligence in Petrophysics

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.04.001 ◽

2021 ◽

Author(s):

Steve Cuddy

Keyword(s):

Artificial Intelligence

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ShakeDaDO: A data collection combining earthquake building damage and ShakeMap parameters for Italy

Artificial Intelligence in Geosciences ◽

10.1016/j.aiig.2021.01.002 ◽

2020 ◽

Vol 1 ◽

pp. 36-51

Author(s):

Licia Faenza ◽

Alberto Michelini ◽

Helen Crowley ◽

Barbara Borzi ◽

Marta Faravelli

Keyword(s):

Data Collection ◽

Building Damage

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