Microseismic Monitoring Events Classification Based on Waveform Clustering Analysis and Application

doi:10.13209/j.0479-8023.2020.018

Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2020, Vol. 56 ›› Issue (3): 406-416.DOI: 10.13209/j.0479-8023.2020.018

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Microseismic Monitoring Events Classification Based on Waveform Clustering Analysis and Application

ZHAI Shang¹, YU Zhichao¹, TAN Yuyang², HUANG Fangfei³, LIU Ling³, HU Tianyue^1,†, HE Chuan^1,†

1. Institute of Oil & Gas, School of Earth and Space Sciences, Peking University, Beijing 100871 2. School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026 3. Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou 510760

Received:2019-05-08 Revised:2019-07-08 Online:2020-05-20 Published:2020-05-20
Contact: HU Tianyue, E-mail: tianyue(at)pku.edu.cn; HE Chuan, E-mail: chuanhe_pku(at)163.com

基于波形聚类分析的微地震监测事件类型判别及应用

翟尚¹, 喻志超¹, 谭玉阳², 黄芳飞³, 刘玲³, 胡天跃^1,†, 何川^1,†

1. 北京大学地球与空间科学学院, 北京大学石油与天然气研究中心, 北京 100871 2. 中国科学技术大学地球和空间科学学院, 合肥 230026 3. 中国地质调查局广州海洋地质调查局, 广州 510760

通讯作者: 胡天跃, E-mail: tianyue(at)pku.edu.cn; 何川, E-mail: chuanhe_pku(at)163.com
基金资助:
中国地质调查局天然气水合物专项(DD20190232-6)和国家重点研发计划(2017YFC0307605, 2017YFC0307702)资助

Abstract

Abstract:

Based on the difference of waveform similarity between different types of microseismic monitoring events and combined with their characteristics in occurrence location, traveling time and polarization direction etc., a method for classifying microseismic monitoring events based on waveform clustering analysis is proposed. Firstly unclassified events can be identified rapidly using conventional microseismic event detection methods, then similar events are grouped based on waveform clustering analysis, finally the types of microseismic events or noise events are determined combining the attribute characteristics. Classified microseismic events can be further used for template matching technique to finely detect similar events with low signal-to-noise ratio. Meanwhile the global optimization approach which aims to improve the accuracy of arrival time picking can be also performed by taking similar microseismic events as a whole.

Key words: feature extraction, waveform cross correlation, microseismic event, hierarchal clustering, feature extraction

摘要：

以不同类型微地震监测事件在波形相似性上的差异为基础, 结合发生位置、走时规律和偏振方向等方面的特征, 提出一种基于波形聚类分析的微地震监测事件类型判别方法。首先使用常规的微地震事件识别算法, 快速地得到待分类的疑似事件; 然后进行波形聚类分析, 结合事件的属性特征, 实现对不同类型微地震事件及噪声事件的分类和判别。分类结果可用于波形模板匹配, 识别同类的低信噪比微地震事件; 还可将所有同类事件作为一个整体, 采用全局优化手段提高初至拾取的精度。

关键词: 层次聚类, 属性提取, 波形互相关, 微地震事件, 层次聚类, 属性提取

ZHAI Shang, YU Zhichao, TAN Yuyang, HUANG Fangfei, LIU Ling, HU Tianyue, HE Chuan. Microseismic Monitoring Events Classification Based on Waveform Clustering Analysis and Application[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(3): 406-416.

翟尚, 喻志超, 谭玉阳, 黄芳飞, 刘玲, 胡天跃, 何川. 基于波形聚类分析的微地震监测事件类型判别及应用[J]. 北京大学学报自然科学版, 2020, 56(3): 406-416.

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Microseismic Monitoring Events Classification Based on Waveform Clustering Analysis and Application

基于波形聚类分析的微地震监测事件类型判别及应用

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