A nonlinear microseismic source location method based on Simplex method and its residual analysis

Li, Nan; Wang, Enyuan; Ge, Mao Chen; Sun, Zhenyu

doi:10.1007/s12517-013-1121-0

Cited by 41 publications

(20 citation statements)

References 21 publications

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“…In conclusion we note that the microseismic source location accuracy is influenced by many factors, such as the location method, the layout of the microseismic network, the velocity model, and the accuracy of the arrival-time measurement (Dong and Li, 2013). Among these, the key factor influencing the stability of the location algorithm and the location accuracy is precision of the velocity model (Prange et al, 2015;Li et al, 2014;Usher et al, 2013). In this paper, an adaptive particle swarm optimization algorithm is proposed for microseismic source location, which is based on average flying velocity of the particles.…”

Section: Introductionmentioning

confidence: 85%

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Sun

Zhang

Wang

et al. 2019

Nonlin. Processes Geophys.

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An intelligent method is presented for locating a microseismic source based on the particle swarm optimization (PSO) concept. It eliminates microseismic source locating errors caused by the inaccurate velocity model of the earth medium. The method uses, as the target of PSO, a global minimum of the sum of squared discrepancies between differences of modeled arrival times and differences of measured arrival times. The discrepancies are calculated for all pairs of detectors of a seismic monitoring system. Then, the adaptive PSO algorithm is applied to locate the microseismic source and obtain optimal value of the P-wave velocity. The PSO algorithm adjusts inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters to avoid the PSO algorithm trapping by local optima during the solution process. The origin time of the microseismic event is estimated by minimizing the sum of squared discrepancies between the modeled arrival times and the measured arrival times. This sum is calculated using the obtained estimates of the microseismic source coordinates and P-wave velocity. The effectiveness of the PSO algorithm was verified through inversion of a theoretical model and two analyses of actual data from mine blasts in different locations. Compared with the classic least squares method (LSM), the PSO algorithm displays faster convergence and higher accuracy of microseismic source location. Moreover, there is no need to measure the microseismic wave velocity in advance: the PSO algorithm eliminates the adverse effects caused by error in the P-wave velocity when locating a microseismic source using traditional methods.

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Section: Introductionmentioning

confidence: 85%

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Sun

Zhang

Wang

et al. 2019

Nonlin. Processes Geophys.

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“…Feng et al [154,155] introduced an efficient global-optimization algorithm and particle swarm optimization to improve localization accuracy. Li et al [156] developed a nonlinear MS source localization method using the simplex method, which can search the MS source directly in the error space through four deformations of the simplex figures. However, the accuracy of localization methods based on the arrival time difference is usually affected by the premeasured wave velocity, the iterative algorithm, and the initial value.…”

Section: Microseismic (Ms) Techniquementioning

confidence: 99%

Dynamic Stability Analysis of Rockmass: A Review

Dong

Wang

et al. 2018

Advances in Civil Engineering

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ere are a series of human or natural activities, including earthquakes, explosions, and rockbursts, which have caused a number of safety accidents in geotechnical engineering. is review paper summarized the theories and methods for dynamic stability analysis of rockmass. First, numerical simulation methods, including finite element method (FEM), discrete element method (DEM), finite difference method (FDM), boundary element method (BEM), discontinuous deformation analysis method (DDA), and numerical manifold method (NMM), are summarized. Second, the laboratory experiments, containing shaking table test, split-Hopkinson pressure bar test (SHPB), improved true-triaxial test, dynamic centrifugal model test, acoustic emission (AE) technique, and dynamic infrared monitoring, are considered. ird, the in situ tests including microseismic (MS) technique, velocity tomography, stress-strain monitoring, and electromagnetic radiation monitoring are considered. Finally, some comprehensive analysis methods based on statistical theories are also provided. It is pointed out that the study foundation for the dynamic stability of rockmass is weak to explain the mechanism. So, a set of general comprehensive theories integrating the different methods, including theoretical analysis, numerical methods, laboratory experiments, and in situ tests, should be completely established. is is the most effective way for further investigation.

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“…22,23 Meanwhile, MS has been studied depending on the theories and practices of dynamic disasters in coal mines. [24][25][26][27][28] All these studies have rapidly developed the MS theory and the related technology and provided the feasibility of MS monitoring for HF in coal mines.…”

Section: Introductionmentioning

confidence: 99%

Underground microseismic monitoring of a hydraulic fracturing operation for CBM reservoirs in a coal mine

Jiang

et al. 2019

Energy Science & Engineering

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Hydraulic fracturing can evidently improve the coalbed methane production in underground coal mines, but it is difficult to delimit the stimulated area accurately. In order to evaluate the stimulated area, microseismic (MS) monitoring technique is proposed to investigate the seismic responses of the induced fractures of hydraulic fracturing. Three coal seams were targeted to be treated in a coal mine. An array of geophones was set along the underground roadway to detect the MS signals caused by HF. In order to verify the result of MS monitoring, water content of each coal seam has been measured before and after HF treatment. The results showed that a series of MS events were detected during the entire HF process, and a sharp MS event usually occurred during the first hour of HF process. The energy of the sharp MS event had higher magnitude than others. The MS distribution exhibited complex morphological features. The directionless MS response was distributed over a radius of less than 40 m but tended to be significantly conjugated with a radius of more than 40 m. HF could stimulate both the coal seam and the rock layers nearby. The achievable stimulated area in the coal seam was determined to be 50 m × 50 m according to the MS density and water content. The stimulated area in terms of MS density was easily found to be broader than the area of water direct intrusion. The present study indicated that MS monitoring technique could potentially be used for evaluation of HF in underground coal mine.

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A nonlinear microseismic source location method based on Simplex method and its residual analysis

Cited by 41 publications

References 21 publications

The adaptive particle swarm optimization technique for solving microseismic source location parameters

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Dynamic Stability Analysis of Rockmass: A Review

Underground microseismic monitoring of a hydraulic fracturing operation for CBM reservoirs in a coal mine

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