Unsupervised machine learning and depth clusters of Euler deconvolution of magnetic data: a new approach to imaging geological structures-Reference-Cited by-同舟云学术

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Unsupervised machine learning and depth clusters of Euler deconvolution of magnetic data: a new approach to imaging geological structures

Published:2024-01-04 Issue:3 Volume:55 Page:223-245
ISSN:0812-3985
Container-title:Exploration Geophysics
language:en
Short-container-title:Exploration Geophysics

Author:

Chukwu Chibuzo¹²³,Betts Peter¹,Moore David¹,Munukutla Radhakrishna²,Armit Robin¹,McLean Mark⁴⁵,Grose Lachlan¹

Affiliation:

1. School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia

2. Department of Earth Sciences, Indian Institute of Technology Bombay, Mumbai, India

3. IITB-Monash Research Academy, Indian Institute of Technology Bombay, Mumbai, India

4. Geological Survey of Victoria, Melbourne, Australia

5. Department of Applied Geophysics, Earth and Atmospheric Sciences, University of Melbourne, Melbourne, Australia

Funder

Australian Society of Exploration Geophysics Foundation Grant

Monash-IITB

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/08123985.2023.2299475

Reference167 articles.

1. Estimating depth to the bottom of magnetic sources at Wikki Warm Spring region, northeastern Nigeria, using fractal distribution of sources approach

2. Density-Based Clustering for 3D Object Detection in Point Clouds

3. Constrained potential field modeling of the crustal architecture of the Musgrave Province in central Australia: Evidence for lithospheric strengthening due to crust-mantle boundary uplift

4. Australia's Moho: A test of the usefulness of gravity modelling for the determination of Moho depth

5. Can Euler deconvolution outline three‐dimensional magnetic sources?

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