Recognition of Micro‐Seismic Events and Inversion in the Case of Single Seismic Phase

Abstract

In order to identify micro‐seismic events of single seismic phase and locate the identified microseismic events as well, we have researched the method of identifying the micro‐seismic events on the basis of the arrival time difference between any two traces in the case of single seismic phase and the characteristic rule of relationship of micro‐seismic events, spatial location of detector and the seismic phase velocity. First, by analyzing the internal variation of the arrival time difference and the above changes of variables, we establish the quantitative calculation relationship between the arrival time difference and every variable. Then, with regard to the arrival time differences of the adjacent trace and the first and the last trace of detector array, we research the specific expression of the quantitative calculation relationship. Given the spatial location of the event, we can calculate the distribution range of the above two kinds of arrival time differences using the relation expression above. Comparing the distribution range of two arrival time difference of the actual micro‐seismic events which we have calculated to the given event, we can identify the single seismic phase with the range which they fall to. Integrating the advances of the search method and the genetic algorithm, we put forward a micro‐seismic joint inversion method based on the solution domain constrained iterative forward model. From the analysis of the expectation probability distribution of the iterative error solution and the variations of different directions of the solutions, we establish the definition conditions of the domain of solution constraints. In the determinate scope of the solution domain, we carry out the inversion of micro‐seismic events using the genetic algorithm. Through the analysis of application of practical data, the method of identifying the micro‐seismic events reduces the error ratio of phase recognition greatly. According to the identified information, we do the inversion of actual data using the inversion method. Comparing with the actual situation, the result is consistent with the actual crack distribution, and is fairly good in convergence.

Uncertainty of microseismic sources identification and probabilistic location in underground excavation

“…By leveraging gradient information, HMC and NUTS achieve faster convergence compared to traditional sampling methods, especially advantageous for larger models like the MS waveform model. PyMC3 stands out as an innovative, open-source probabilistic programming toolkit featuring an intuitive, readable, yet potent syntax akin to the natural language statisticians employ to articulate models (Song et al, 2013). PyMC3 was utilized to address general Bayesian statistical inference and prediction tasks.…”

“…The neighborhood algorithm was applied in the threedimensional MS source location based on P and S wave travel times in hydrocarbon reservoirs (Oye and Roth, 2003). The differential evolution was used to improve the accuracy and stability of the location based on Bayesian theory (Song et al, 2013). PSO was used to improve the computational efficiency of the location algorithm.…”