Journal
INFORMATION SCIENCES
Volume 544, Issue -, Pages 325-342Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.07.045
Keywords
Source depth; Machine learning; Convolutional neural networks; Feature selection; Principal component analysis
Categories
Funding
- National Key R&D Program of China [2018YFC1503502]
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The discrimination of source depth in micro-earthquake monitoring is crucial, and this study explores the use of machine learning techniques, showing that deep learning outperforms traditional classification methods in distinguishing source depth.
Discrimination of mirco-earthquake on source depth plays an important role in the field of micro-seismic monitoring. Conventional machine learning methods for data classification rely on carefully hand-engineered features that are vulnerable to low signal-to-noise ratio. Convolutional neural networks (CNNs) demonstrate some merits in dealing with structured data modelling where a set of meaningful features can be automatically extracted from sample learning. This paper explores the use of machine learning techniques for discrimination between deep and shallow mirco-seismic events. A benchmarked dataset including 444 micro-earthquakes from an underground cavern collapse in South Louisiana is employed for performance evaluation in this study, where several feature-based classifiers are compared against the CNN classifier. Empirical results show that the deep learning method outperforms the conventional classification techniques in discriminating the source depth. (C) 2020 Elsevier Inc. All rights reserved.
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