IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4729-d411927.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4729/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4729/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guangliang Feng & Manqing Lin & Yang Yu & Yu Fu, 2020. "A Microseismicity-Based Method of Rockburst Intensity Warning in Deep Tunnels in the Initial Period of Microseismic Monitoring," Energies, MDPI, vol. 13(11), pages 1-15, May.
    2. Qinghe Zhang & Weiguo Li & Liang Yuan & Tianle Zheng & Zhiwei Liang & Xiaorui Wang, 2024. "A review of tunnel rockburst prediction methods based on static and dynamic indicators," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 10465-10512, September.
    3. Zhiqiang Zhang & Peng Xu & Heng Zhang & Kangjian Zhang, 2019. "Dynamic Change Characteristics of Groundwater Affected by Super-Long Tunnel Construction in the Western Mountainous Area of China," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    4. Weizhang Liang & Asli Sari & Guoyan Zhao & Stephen D. McKinnon & Hao Wu, 2020. "Short-term rockburst risk prediction using ensemble learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1923-1946, November.
    5. Zhiqiang Zhang & Chun Luo & Heng Zhang & Ruikai Gong, 2020. "Rockburst Identification Method Based on Energy Storage Limit of Surrounding Rock," Energies, MDPI, vol. 13(2), pages 1-24, January.
    6. Xuewei Liu & Quansheng Liu & Bin Liu & Yongshui Kang, 2020. "A Modified Bursting Energy Index for Evaluating Coal Burst Proneness and Its Application in Ordos Coalfield, China," Energies, MDPI, vol. 13(7), pages 1-19, April.
    7. Jiachuang Wang & Haoji Ma & Xianhang Yan, 2023. "Rockburst Intensity Classification Prediction Based on Multi-Model Ensemble Learning Algorithms," Mathematics, MDPI, vol. 11(4), pages 1-29, February.
    8. 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.
    9. Hanna Michalak & Paweł Przybysz, 2021. "The Use of 3D Numerical Modeling in Conceptual Design: A Case Study," Energies, MDPI, vol. 14(16), pages 1-21, August.
    10. Jianguo Zhang & Peitao Li & Xin Yin & Sheng Wang & Yuanguang Zhu, 2022. "Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm," Mathematics, MDPI, vol. 10(10), pages 1-16, May.
    11. Hang Zhang & Chunchi Ma & Tianbin Li, 2019. "Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel," Energies, MDPI, vol. 12(10), pages 1-17, May.
    12. Farzin Golzar & David Nilsson & Viktoria Martin, 2020. "Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    13. Chenxi Zhang & Diyuan Li & Shunchuan Wu & Long Chen & Jun Peng, 2021. "Study on Evolution Mechanism of Structure-Type Rockburst: Insights from Discrete Element Modeling," Sustainability, MDPI, vol. 13(14), pages 1-26, July.
    14. Zhiqiang Zhang & Ruikai Gong & Heng Zhang & Wanping He, 2020. "The Sustainability Performance of Reinforced Concrete Structures in Tunnel Lining Induced by Long-Term Coastal Environment," Sustainability, MDPI, vol. 12(10), pages 1-23, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4729-:d:411927. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.