Abstract
Aftershocks can cause disasters again after mainshocks, which result in threat to life and economic loss. In order to avoid secondary disasters, it is necessary to predict whether aftershocks would happen in a given region. There have been studies using different features and methods to predict aftershocks spatial distribution. However, it is still unclear which are more important for aftershock prediction, input features or models; which type of features is more predictive for the prediction task. In this paper, we predict aftershock spatial distribution by combining different types of features and applying different machine learning methods. We introduce five different types of features and combine them together for prediction: the stress change sensors, their logarithmic values, the physical quantities, the magnitude of mainshocks, and the distance between the grid cell and the epicenter of mainshocks. We train different classifiers: Naive Bayes, Support Vector Machine,Gradient Boosting Decision Tree, k-Nearest Neighbors, Logistic regression, and DMAP (a Deep Neural Network model). Based on the 62,811 aftershocks of 171 distinct mainshocks in the past about 40 years in China, we conduct comprehensive experiments and analyses. We find that features play a more important role for this prediction task. Using the same feature type, different classifiers obtain quite similar performance. With different features, the same model performs differently. Taking the combined features as input, we achieve the state-of-the-art performance, with an AUC of 0.9530, about 4% higher than that of DeVries et al., showing the superiority of the combined features. Among all the features, adding the distance to the stress change sensors contributes the most to improve the prediction performance. In addition, it is found that the model prediction performance varies in terms of the time spans after mainshocks and the aftershock magnitudes.
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Availability of data and material
All the experiments related to machine learning methods were implemented by Python. The code and the used dataset are available on GitHub(https://github.com/whyboy/aftershockprediction).
Code availability
The code and the used dataset are available on GitHub(https://github.com/whyboy/aftershockprediction).
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Funding
This document is the result of the research project funded by the National Key R&D Program of China under Grant (No. 2018YFC1504006), and National Natural Science Foundation of China (No. 61802342, 61802340).
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Sha Zhao wrote the paper and designed the experiments; Haiyan Wang did the data pre-processing and conducted the experiments related to machine learning methods; Yan Xue did the data acquisition and conducted the experiments related to physical methods; Shijian Li and Yilin Wang analyzed the data; Jie Liu carried out the acquisition; Gang Pan designed the experiments and analyzed the data. All the authors have read and revised the manuscript. Dr Xue and Dr Pan are the corresponding authors.
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Zhao, S., Wang, H., Xue, Y. et al. What are more important for aftershock spatial distribution prediction, features, or models? A case study in China. J Seismol 26, 181–196 (2022). https://doi.org/10.1007/s10950-021-10044-x
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DOI: https://doi.org/10.1007/s10950-021-10044-x