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Application of Waveform Stacking Methods for Seismic Location at Multiple Scales

Author

Listed:
  • Lei Li

    (Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410083, China
    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Yujiang Xie

    (Ocean and Earth Science, University of Southampton, Southampton SO14 3ZH, UK)

  • Jingqiang Tan

    (Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410083, China
    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

Abstract

Seismic source location specifies the spatial and temporal coordinates of seismic sources and lays the foundation for advanced seismic monitoring at all scales. In this work, we firstly introduce the principles of diffraction stacking (DS) and cross-correlation stacking (CCS) for seismic location. The DS method utilizes the travel time from the source to receivers, while the CCS method considers the differential travel time from pairwise receivers to the source. Then, applications with three field datasets ranging from small-scale microseismicity to regional-scale induced seismicity are presented to investigate the feasibility, imaging resolution, and location reliability of the two stacking operators. Both of the two methods can focus the source energy by stacking the waveforms of the selected events. Multiscale examples demonstrate that the imaging resolution is not only determined by the inherent property of the stacking operator but also highly dependent on the acquisition geometry. By comparing to location results from other methods, we show that the location bias is consistent with the scale size, as well as the frequency contents of the seismograms and grid spacing values.

Suggested Citation

  • Lei Li & Yujiang Xie & Jingqiang Tan, 2020. "Application of Waveform Stacking Methods for Seismic Location at Multiple Scales," Energies, MDPI, vol. 13(18), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4729-:d:411927
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    References listed on IDEAS

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    1. Heng Zhang & Liang Chen & Shougen Chen & Jianchun Sun & Jiasong Yang, 2018. "The Spatiotemporal Distribution Law of Microseismic Events and Rockburst Characteristics of the Deeply Buried Tunnel Group," Energies, MDPI, vol. 11(12), pages 1-21, November.
    2. Guangliang Feng & Guoqing Xia & Bingrui Chen & Yaxun Xiao & Ruichen Zhou, 2019. "A Method for Rockburst Prediction in the Deep Tunnels of Hydropower Stations Based on the Monitored Microseismicity and an Optimized Probabilistic Neural Network Model," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
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    Cited by:

    1. Qinghui Mao & Tahir Azeem & Zhixian Gui & Peng Wang & Yu Zhong, 2022. "A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events," Energies, MDPI, vol. 15(8), pages 1-15, April.
    2. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.

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