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Research Trend Analysis for Fault Detection Methods Using Machine Learning
머신러닝을 사용한 단층 탐지 기술 연구 동향 분석
Econ. Environ. Geol. 2020 Aug;53(4):479-89
Published online August 31, 2020;
Copyright © 2020 the Korean society of economic and environmental gelology.

Wooram Bae and Wansoo Ha*
배우람 · 하완수*

Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea
부경대학교 에너지자원공학과
Received March 20, 2020; Revised June 29, 2020; Accepted June 29, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
A fault is a geological structure that can be a migration path or a cap rock of hydrocarbon such as oil and gas, formed from source rock. The fault is one of the main targets of seismic exploration to find reservoirs in which hydrocarbon have accumulated. However, conventional fault detection methods using lateral discontinuity in seismic data such as semblance, coherence, variance, gradient magnitude and fault likelihood, have problem that professional interpreters have to invest lots of time and computational costs. Therefore, many researchers are conducting various studies to save computational costs and time for fault interpretation, and machine learning technologies attracted attention recently. Among various machine learning technologies, many researchers are conducting fault interpretation studies using the support vector machine, multi-layer perceptron, deep neural networks and convolutional neural networks algorithms. Especially, researchers use not only their own convolution networks but also proven networks in image processing to predict fault locations and fault information such as strike and dip. In this paper, by investigating and analyzing these studies, we found that the convolutional neural networks based on the U-Net from image processing is the most effective one for fault detection and interpretation. Further studies can expect better results from fault detection and interpretation using the convolutional neural networks along with transfer learning and data augmentation.
Keywords : fault detection, machine learning, support vector machine, deep neural networks, convolutional neural networks


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