Improving the stability of microseismic event detection by clustering algorithm

被引：0
|
作者：
Gong Y. [1 ]
Meng Q. [1 ]
Lan J. [1 ]
Shan Z. [1 ]
He P. [2 ]
Zhai R. [2 ]
机构：
[1] Exploration and Development Research Institute, SINOPEC East China Oil & Gas Company, Jiangsu, Nanjing
[2] Huadong Branch, SINOPEC Geophysical Corporation, Jiangsu, Nanjing
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2024年 / 59卷 / 01期
关键词：
AIC（Akaike Information Criteria）algorithm; clustering analysis; energy ratio algorithm; first break picking; microseismic monitoring;
D O I：
10.13810/j.cnki.issn.1000-7210.2024.01.012
中图分类号：
学科分类号：
摘要：
A key step in microseismic monitoring is the efficient and accurate picking of the first break of the microseismic data. Currently，the commonly used method to pick the first break is the energy ratio algorithm，which is simple and efficient in application. However，the main weakness of this algorithm is the poor results on low signal to noise ratio data. In this paper，the algorithm is improved by applying the clustering algorithm. The principle of the improved method is to first pick the first break through the energy ratio algorithm，and optimize the results by clustering algorithm to divide the low error result with false pickings. Then，the false pickings are corrected according to the distribution fitted by the low error result. Finally，the Akaike information criteria（AIC）algorithm is used in a small window that creates from optimized results to pick the first break accurately. This algorithm combines the benefits of the energy ratio algorithm and the AIC algorithm. Actual data test results show that the improved algorithm has higher pick⁃up accuracy in low SNR data compared to the conventional algorithm and can effectively identify the first break of multiple seismic phases. In addition，the algorithm is efficient and can be applied to field processing. © 2024 Editorial office of Oil Geophysical Prospecting. All rights reserved.
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页码：110 / 121
页数：11
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