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Support vector machine-based rapid magnitude estimation using transfer learning for the Sichuan-Yunnan region, China

Zhu Jingbao, Li Shanyou, Ma Qiang, He Bin and Song Jindong
Support vector machine-based rapid magnitude estimation using transfer learning for the Sichuan-Yunnan region, China
Bulletin of the Seismological Society of America (February 2022) 112 (2): 894-904

Abstract

The Sichuan-Yunnan region is a seismically active area. To explore the feasibility of using the support vector machine (SVM) method for magnitude estimation in the area and to improve the rapid magnitude estimation accuracy, we construct an SVM magnitude estimation model using transfer learning (TLSVM-M model) based on a single-station record in this study. We find that the magnitude estimation of a single station shows that for the test dataset, within the 3 s time window after the P-wave arrival, the average absolute error (which reflects the size of the estimated magnitude error as a whole) and standard deviation (which reflects the scatter of magnitude estimation error) of the magnitudes estimated by the TLSVM-M model are 0.31 and 0.41, respectively, which are less than those of the SVM magnitude estimation model without transfer learning (0.44 and 0.55, respectively), the tau c method (1.35 and 1.74, respectively) and the Pd method (0.44 and 0.56, respectively). In addition, in test involving five earthquake events via the TLSVM-M model, at 1 s after the first station is triggered, the magnitudes of three events (Ms 4.2, 5.2, and 6.3) are estimated within an error range of + or -0.3 magnitude units. For the other two earthquakes (Ms 6.6 and 7.0), there is an obvious magnitude underestimation problem at 1 s after the first station is triggered, with less underestimation by increasing time after the first station is triggered. Meanwhile, for these two events (Ms 6.6 and 7.0), within 13 s after the first station was triggered, the magnitude estimation errors are both within + or -0.3 magnitude units. The TLSVM-M model has the capability of rapid magnitude estimation for small-to-moderate events in the Sichuan-Yunnan region. Meanwhile, we infer that the proposed model may have potential in earthquake early warning.


ISSN: 0037-1106
EISSN: 1943-3573
Coden: BSSAAP
Serial Title: Bulletin of the Seismological Society of America
Serial Volume: 112
Serial Issue: 2
Title: Support vector machine-based rapid magnitude estimation using transfer learning for the Sichuan-Yunnan region, China
Affiliation: China Earthquake Administration, Institute of Engineering Mechanics, Laboratory of Earthquake Engineering and Engineering Vibration, Harbin, China
Pages: 894-904
Published: 20220201
Text Language: English
Publisher: Seismological Society of America, Berkeley, CA, United States
References: 54
Accession Number: 2022-011444
Categories: Seismology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. sketch map
N21°40'00" - N29°00'00", E97°30'00" - E106°10'00"
N26°00'00" - N34°10'00", E97°30'00" - E108°25'00"
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute. Abstract, Copyright, Seismological Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 202210
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