Supervised Neural Network Targeting and Classification Analysis of Airborne EM, Magnetic and Gamma-ray Spectrometry Data for Mineral Exploration-Reference-Cited by-同舟云学术

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Supervised Neural Network Targeting and Classification Analysis of Airborne EM, Magnetic and Gamma-ray Spectrometry Data for Mineral Exploration

Published:2015-12 Issue:1 Volume:2015 Page:1-5
ISSN:2202-0586
Container-title:ASEG Extended Abstracts
language:en
Short-container-title:ASEG Extended Abstracts

Author:

Kwan Karl¹,Reford Stephen²,Abdoul-Wahab Djiba Maïga³,Pitcher Douglas H.⁴,Bournas Nasreddine¹,Prikhodko Alexander¹,Plastow Geoffrey¹,Legault Jean M.¹

Affiliation:

1. Geotech Ltd 245 Industrial Parkway N. Aurora, Ontario, Canada L4G 4C4

2. Paterson, Grant & Watson Ltd 155 University Ave. Toronto, Ontario, Canada M5H 3B7

3. CPG/PRDSM 634 Boulevard des Nations Rue YN -34, Porte 585 République du Niger

4. Geotech Airborne Limited BE40, Kwaning Chateau Adjiringanor, East Legon Accra, Ghana Box 16160, Kia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1071/ASEG2015ab306

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1. Deep learning to estimate the basement depth by gravity data using a feedforward neural network;GEOPHYSICS;2023-04-28

2. DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications;Earth System Science Data;2023-03-24

3. A Deep Learning Estimation of the Earth Resistivity Model for the Airborne Transient Electromagnetic Observation;Journal of Geophysical Research: Solid Earth;2022-03

4. A supervised learning method to estimate basement depth from potential fields;First International Meeting for Applied Geoscience & Energy Expanded Abstracts;2021-09-01

5. Removal of multisource noise in airborne electromagnetic data based on deep learning;GEOPHYSICS;2020-11-01

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