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

• Published in: ISPRS international journal of geo-information 2019-10, Vol.8 (10), p.462
• Main Authors: Karimzadeh, Sadra, Matsuoka, Masashi, Kuang, Jianming, Ge, Linlin
• Format: Article
• Language: English
• Subjects: Aftershocks; Algorithms; Bayesian analysis; coulomb stress; Datasets; Earthquake prediction; Earthquakes; Fault lines; Friction; kermanshah earthquake; Learning algorithms; Machine learning; Methods; Neural networks; Radar imagery; Radar imaging; Regression analysis; SAR (radar); Satellites; Seismic activity; Shear stress; Stress concentration; Support vector machines; Synthetic aperture radar; Training; Variables
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi8100462

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.