Waveform-based microseismic location using stochastic optimization algorithms: A parameter tuning workflow. (March 2019)

• Record Type: Journal Article
• Authors: Li, Lei; Tan, Jingqiang; Xie, Yujiang; Tan, Yuyang; Walda, Jan; Zhao, Zhengguang; Gajewski, Dirk
• Is Part Of: Computers & geosciences. Volume 124(2019)
• Page Start: 115
• Page End: 127
• Publication Date: 2019-03
• Subjects: Data processing -- Algorithms -- Geophysics -- Inverse problems; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2019.01.002
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: A fast and accurate source location estimation is the foundation for passive seismic processing and interpretation. Waveform-based location methods become more and more popular for analysis of both natural and induced seismicity. We utilize stochastic optimization algorithms to speed up microseismic location. Two waveform-based location methods (i.e. diffraction stacking and cross correlation stacking) are adopted to test the performance of three algorithms (i.e. particle swarm optimization, differential evolution, and neighbourhood algorithm). In order to enhance the algorithmic performance, we propose a parameter tuning workflow which consists of two types of repeated tests. One type is multiple independent tests for a single event and the other involves tests of multiple events. The success rate, speedup, location uncertainty and bias are investigated to assess the algorithmic performances. We apply the workflow to a field dataset of mining induced seismicity and obtain preferential algorithm(s) with optimized ranges of control parameters. Synthetic tests are also conducted to demonstrated the feasibility of the proposed parameter tuning workflow. Given the two imaging operators, differential evolution is demonstrated to be the preferential one accounting for both algorithmic robustness and efficiency. Meanwhile, the workflow also examines the characteristics of different imaging operators. Cross correlation stacking proves to be simpler and more robust than its counterpart. Though the workflow is developed for microseismic location, it can also be adapted for other seismic inversion problems (e.g., source mechanism inversion) and ensure the algorithmic robustness and efficiency. Highlights: Waveform-based location by Particle Swarm Optimization, Differential Evolution, and Neighbourhood Algorithm are implemented. A novel parameter tuning workflow of stochastic optimization algorithms for waveform-based location is proposed. Three algorithms are tested and analyzed using both synthetic and field data. The characteristics of two imaging operators are also revealed.