A Reliable Strategy for Improving Automatic First‐Arrival Picking of High‐Noise Three‐Component Microseismic Data

Research Article| April 03, 2019 A Reliable Strategy for Improving Automatic First‐Arrival Picking of High‐Noise Three‐Component Microseismic Data Huailiang Li; Huailiang Li aState Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, No. 1 East Third Road, Chengdu 610059, China, li-huai-liang@163.comtuoxg@swust.edu.cndAlso at Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China. Search for other works by this author on: GSW Google Scholar Xianguo Tuo; Xianguo Tuo aState Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, No. 1 East Third Road, Chengdu 610059, China, li-huai-liang@163.comtuoxg@swust.edu.cndAlso at Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China. Search for other works by this author on: GSW Google Scholar Ruili Wang; Ruili Wang bInstitute of Natural and Mathematical Sciences, Massey University, P.O. Box 102904, Auckland 0745, New Zealand, ruili.wang@massey.ac.nz Search for other works by this author on: GSW Google Scholar Jérémie Courtois Jérémie Courtois cFundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China Search for other works by this author on: GSW Google Scholar Author and Article Information Huailiang Li aState Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, No. 1 East Third Road, Chengdu 610059, China, li-huai-liang@163.comtuoxg@swust.edu.cndAlso at Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China. Xianguo Tuo aState Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, No. 1 East Third Road, Chengdu 610059, China, li-huai-liang@163.comtuoxg@swust.edu.cndAlso at Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China. Ruili Wang bInstitute of Natural and Mathematical Sciences, Massey University, P.O. Box 102904, Auckland 0745, New Zealand, ruili.wang@massey.ac.nz Jérémie Courtois cFundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, No. 59 Qinglong Road, Mianyang 621010, China Publisher: Seismological Society of America First Online: 03 Apr 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (3): 1336–1345. https://doi.org/10.1785/0220190038 Article history First Online: 03 Apr 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Huailiang Li, Xianguo Tuo, Ruili Wang, Jérémie Courtois; A Reliable Strategy for Improving Automatic First‐Arrival Picking of High‐Noise Three‐Component Microseismic Data. Seismological Research Letters 2019;; 90 (3): 1336–1345. doi: https://doi.org/10.1785/0220190038 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT We present a reliable first‐arrival picking method using wavelet multilevel analysis (WMA) and polarization analysis for high‐noise three‐component (3C) microseismic data. The proposed method is based on the autoregressive (AR) model using the Akaike information criterion (AIC) algorithm, named PWT‐AIC (in which PWT is polarization wavelet transform). This study aims to address the problem that the AR‐AIC picker selects the global least point as the first arrival; it will cause a questionable arrival identification while being applied to the 3C microseismic recordings with a low signal‐to‐noise ratio. Initially, we employ the WMA to extract the dominant signal for high‐noise 3C microseismic data; we then use the reconstructed approximation data to perform the AR‐AIC calculation to identify the first arrival. Furthermore, we adapt the polarization information of 3C microseismic data to determine the proper calculation section for the AR‐AIC algorithm. We conduct a polarization analysis by adopting the eigenanalysis of the sliding covariance matrix. Compared with the popular short‐term average/long‐term average ratio and existing AR‐AIC pickers, the presented algorithm can significantly reduce the picking error. A test using synthetic 3C seismic data with high‐noise indicates that the onset time can be accurately identified, and the improved method has an error of between ±1 and ±2 sample intervals. Results using field microseismic recordings also confirmed that the proposed strategy can improve the accuracy of arrival‐time estimation for noisy 3C microseismic datasets. In addition, we perform a time consumption comparison of the different approaches. Although the proposed picker requires more computation time than the other pickers, the results are acceptable, given the capabilities of modern computers. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.