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Open AccessArticle

Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective

1
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
2
Department of Architecture and Building Engineering, School of Environment and Society, Tokyo Institute of Technology 4259-G3-2 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
3
School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 462; https://doi.org/10.3390/ijgi8100462
Received: 18 September 2019 / Revised: 18 October 2019 / Accepted: 20 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
Small earthquakes following a large event in the same area are typically aftershocks, which are usually less destructive than mainshocks. These aftershocks are considered mainshocks if they are larger than the previous mainshock. In this study, records of aftershocks (M > 2.5) of the Kermanshah Earthquake (M 7.3) in Iran were collected from the first second following the event to the end of September 2018. Different machine learning (ML) algorithms, including naive Bayes, k-nearest neighbors, a support vector machine, and random forests were used in conjunction with the slip distribution, Coulomb stress change on the source fault (deduced from synthetic aperture radar imagery), and orientations of neighboring active faults to predict the aftershock patterns. Seventy percent of the aftershocks were used for training based on a binary (“yes” or “no”) logic to predict locations of all aftershocks. While untested on independent datasets, receiver operating characteristic results of the same dataset indicate ML methods outperform routine Coulomb maps regarding the spatial prediction of aftershock patterns, especially when details of neighboring active faults are available. Logistic regression results, however, do not show significant differences with ML methods, as hidden information is likely better discovered using logistic regression analysis. View Full-Text
Keywords: aftershocks; machine learning; Kermanshah earthquake; Coulomb stress; synthetic aperture radar aftershocks; machine learning; Kermanshah earthquake; Coulomb stress; synthetic aperture radar
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MDPI and ACS Style

Karimzadeh, S.; Matsuoka, M.; Kuang, J.; Ge, L. Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective. ISPRS Int. J. Geo-Inf. 2019, 8, 462.

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