Automatic microseismic event picking via unsupervised machine learning. Issue 3 (20th June 2020)

• Record Type: Journal Article
• Authors: Chen, Yangkang
• Is Part Of: Geophysical journal international. Volume 222:Issue 3(2020)
• Page Start: 1750
• Page End: 1764
• Publication Date: 2020-06-20
• Subjects: Inverse theory -- Time-series analysis -- Earthquake source observations; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggaa186
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Effective and efficient arrival picking plays an important role in microseismic and earthquake data processing and imaging. Widely used short-term-average long-term-average ratio (STA/LTA) based arrival picking algorithms suffer from the sensitivity to moderate-to-strong random ambient noise. To make the state-of-the-art arrival picking approaches effective, microseismic data need to be first pre-processed, for example, removing sufficient amount of noise, and second analysed by arrival pickers. To conquer the noise issue in arrival picking for weak microseismic or earthquake event, I leverage the machine learning techniques to help recognizing seismic waveforms in microseismic or earthquake data. Because of the dependency of supervised machine learning algorithm on large volume of well-designed training data, I utilize an unsupervised machine learning algorithm to help cluster the time samples into two groups, that is, waveform points and non-waveform points. The fuzzy clustering algorithm has been demonstrated to be effective for such purpose. A group of synthetic, real microseismic and earthquake data sets with different levels of complexity show that the proposed method is much more robust than the state-of-the-art STA/LTA method in picking microseismic events, even in the case of moderately strong background noise.