结合数据扩增与残差收缩网络的地震短临预测

doi:10.12196/j.issn.1000-3274.2022.02.006

地震 ›› 2022, Vol. 42 ›› Issue (2): 74-88.doi: 10.12196/j.issn.1000-3274.2022.02.006

结合数据扩增与残差收缩网络的地震短临预测

张翔¹, 孙宪坤¹, 胡峻², 尹京苑², 熊玉洁¹

1.上海工程技术大学电子电气工程学院, 上海 201620;
2.上海市地震局, 上海 200062

收稿日期:2021-10-25 修回日期:2021-12-01 出版日期:2022-04-30 发布日期:2023-02-17
通讯作者: 孙宪坤, 副教授。 E-mail: xksun@sues.edu.cn
作者简介:张翔(1996-), 男, 江苏扬州人, 硕士研究生, 主要从事模式识别与智能系统研究。
基金资助:
国家重点研发计划项目(2019YFC1509202); 国家自然科学基金项目(62006150); 上海青年科技英才扬帆计划项目(19YF1418400)

Short-term and Imminent Earthquake Prediction Combined with Data Augmentation and Residual Shrinkage Network

ZHANG Xiang¹, SUN Xian-kun¹, HU Jun², YIN Jing-yuan², XIONG Yu-jie¹

1. School of Electronic and Electrical Engineering, Shanghai University of Engineering and Science, Shanghai 201620, China;
2. Earthquake Administration of Shanghai Municipality, Shanghai 200062, China

Received:2021-10-25 Revised:2021-12-01 Online:2022-04-30 Published:2023-02-17

摘要/Abstract

摘要： 中强地震发生前存在地表异常增温现象, 热红外信息可以成为地震短临预测的途径之一。然而, 地震预测研究常存在可供分析的震例样本不足的问题。本文基于MODIS地表温度数据, 先通过深度卷积生成对抗网络(DCGAN)对地震前的地温数据做扩增处理, 再将扩增后的地温数据输入深度残差收缩网络进行特征提取并预测未来短期内是否存在发生5级及以上地震事件的可能性。实验针对中国地震较多的中西部地区, 将地表温度数据依照地震实际发生情况标记为发生地震数据和未发生地震数据, 样本比为3∶1, 分不同的预测时间段进行比较。结果显示, 5日预测的准确率最高为73.86%, 正确预测发生占实际发生的比例为68.09%。多次实验准确率曲线趋向稳定, 证明该预测方法有很好的实用性。基于MODIS数据结合数据扩增与残差收缩网络的预测方法为短临中强地震预测研究提供了一种新思路。

关键词: 地震预测, MODIS, 数据扩增, 深度卷积生成对抗网络, 深度残差收缩网络

Abstract: Anomalies of temperature increase will occur before moderate and strong earthquakes, and thermal infrared information can be one of the ways to predict the short-term and imminent earthquakes. However, earthquake prediction research often has the problem of insufficient earthquake samples for analysis. Based on the MODIS surface temperature data, the deep convolutional generative adversarial network (DCGAN) is used to amplify the pre-earthquake ground temperature data, and then the amplified ground temperature data is input into the deep residual shrinkage network for feature extraction, and predict whether there is the possibility of M≥5 earthquake in the short-term future. The experiment is aimed at the central and western regions of China where earthquakes often occur, and the surface temperature data is marked according to the actual occurrence of the earthquake as the data of the occurrence of earthquakes and the data of no earthquakes. The sample ratio is 3∶1, and the comparison is divided into different prediction time periods. The results show that the highest accuracy rate of 5-day prediction is 73.86%, and the proportion of correct predictions to actual occurrences is 68.09%. The accuracy rate curve of multiple experiments tends to be stable, which proves that the prediction method has good practicability. Based on MODIS data, the prediction method combining data augmentation and residual shrinkage network provides a new idea for short-term, moderate-strong earthquake prediction research.

Key words: Earthquake prediction, MODIS, Data augmentation, Deep convolution generation adversarial network, Deep residual shrinkage network

中图分类号:

P315.75

张翔, 孙宪坤, 胡峻, 尹京苑, 熊玉洁. 结合数据扩增与残差收缩网络的地震短临预测[J]. 地震, 2022, 42(2): 74-88.

ZHANG Xiang, SUN Xian-kun, HU Jun, YIN Jing-yuan, XIONG Yu-jie. Short-term and Imminent Earthquake Prediction Combined with Data Augmentation and Residual Shrinkage Network[J]. EARTHQUAKE, 2022, 42(2): 74-88.

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结合数据扩增与残差收缩网络的地震短临预测

Short-term and Imminent Earthquake Prediction Combined with Data Augmentation and Residual Shrinkage Network

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