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

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 E...

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Bibliographic Details
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
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi8100462