Microseismic Location Research Articles

The Use of Intelligent Sensing Algorithm for Internet of Things and Hash Spatial Information Location Technology

This exploration is aimed at quickly obtaining the spatial position information of microseismic focal points and increasing the accuracy of microseismic rapid positioning, to take timely corresponding measures. A microseismic focal point location system completely different from the traditional microseismic location method is proposed. The search engine technology is introduced into the system, which can locate the microseismic focal point quickly and accurately. First, the propagation characteristics of microseismic signals in coal and rock layers are analyzed, and the focal position information is obtained. However, the collected microseismic signal of the coal mine contains noise, so it is denoised at first. Then, a waveform database is established for the denoised waveform data and focal point position. The structure and mathematical model of the location‐sensitive hash (LSH) based on P stable distribution are introduced and improved, and the optimized algorithm multiprobe LSH is obtained. The microseismic location model is established according to the characteristics of microseismic data. The values of three parameters, hash table number, hash function family dimension, and interval size, are determined. The experimental data of the parameters of the search engine algorithm are analyzed. The results show that when the number of hash tables is 6, the dimension k of the hash function family is 14, and the interval size W is 8000, the retrieval time reaches a relatively small value, the recall rate reaches a large value, and the proportion of retrieved candidates is large; the parameters of the search engine algorithm of the measured coal mine microseismic data are analyzed. It is obtained that when the number of hash tables is 4, the dimension k of the hash function family is 6, and the interval size W is 500, the retrieval time reaches a relatively small value, the recall rate obtains a large value, and the proportion of retrieved candidates is large. The contents studied are of great significance to the evaluation of destructive mine earthquakes and impact risk.

An improved CS-LS hybrid algorithm on microseismic source location

This paper presents a hybrid global optimization algorithm for the localization of microseismic events, which is composed of the Least-Squares (LS) algorithm and the Cuckoo search (CS) algorithm. It significantly reduces the demand for the accuracy of the inversion velocity model. The positioning process is presented as follows: firstly, construct an objective function by utilizing time difference information of P-wave; then, find the final optimal value through iterations by embedding this objective function as the fitness function to the hybrid algorithm. Through simulation experiments, the hybrid algorithm can acquire more accurate locations and a faster convergence speed than the LS algorithm.

Errors in microseismic events locations introduced by neglecting anisotropy during velocity model calibration in downhole monitoring

Microseismic monitoring is the only tool to provide locations of microseismic events induced e.g. during hydraulic fracturing of unconventional reservoirs. Obtained locations become a baseline for the evaluation of stimulation effectiveness. However, due to several factors, they can be different from the true ones and lead to misinterpretation. Most important is the inaccuracy of the velocity model used in the location procedure, which is often amplified by seismic anisotropy. Even though the anisotropy is a common feature of organic-rich mudrocks, it is often neglected in the current industry practice. The usual approach is to produce a set of isotropic models, valid for the limited subsurface area only, which compensate for anisotropy effects. Here we explore how the accuracy of microseismic events' locations is affected when neglecting the anisotropy during velocity model building using traveltimes of calibration shots. We analyse events' mislocations using the synthetic modelling that simulates an ongoing multi-stage treatment in a vertically-transverse isotropic (VTI) media, monitored with a single vertical borehole. Finally, we provide a quantitative interpretation of location errors expected to be present when VTI anisotropy is being neglected. The most important result is the demonstration of how spatially limited are single-stage isotropic models in terms of correct event locations, despite performing an accurate calibration. Also, we show the limited gain in terms of location accuracy when multi-stage isotropic velocity models are constructed. As the ultimate solution to reduce location errors, we propose to account for anisotropy by inverting VTI anisotropy model parameters even when only part of calibration data is available.

High-Accuracy Location of Microseismic Events in a Strong Inhomogeneous Mining Environment by Optimized Global Full Waveform Inversion

High-accuracy determination of a microseismic (MS) location is the core task in MS monitoring. In this study, a 3D multi-scale grid Green’s function database, depending on recording wavefield frequency band for the target mining area, is pre-generated based on the reciprocity theorem and 3D spectral element method (SEM). Then, a multi-scale global grid search strategy is performed based on this pre-stored Green’s function database, which can be effectively and hierarchically processed by searching for the spatial location. Numerical wavefield modeling by SEM effectively overcomes difficulties in traditional and simplified ray tracing modeling, such as difficult wavefield amplitude and multi-path modeling in 3D focusing and defusing velocity regions. In addition, as a key step for broadband waveform simulation, the source-time function estimated from a new data-driven singular value decomposition averaged fractional derivative based wavelet function (DD-SVD-FD wavelet) was proposed to generate high-precision synthetic waveforms for better fitting observed broadband waveform than those by simple and traditional source-time function. Combining these sophisticated processing procedures, a new robust grid search and waveform inversion-based location (GSWI location) approach is integrated. In the synthetic test, we discuss and demonstrate the importance of 3D velocity model accuracy to waveform inversion-based location results for a practical MS monitoring configuration. Furthermore, the average location error of the 3D GSWI location for eight real blasting events is only 15.0 m, which is smaller than error from 3D ray tracing-based location (26.2 m) under the same velocity model. These synthetic and field application investigations prove the crucial role of 3D velocity model, finite-frequency travel-time sensitivity kernel characteristics and accurate numerical 3D broadband wavefield modeling for successful MS location in a strong heterogeneous velocity model that are induced by the presence of ore body, host rocks, complex tunnels, and large excavations.

Microseismic source location in tunnels: Effect of arrival-time picking

The microseismic (MS) monitoring technique is widely used in deep tunnels for rock mass stability monitoring and disaster warning. MS source location is the basis of the MS monitoring technique. This study investigates the effect of arrival-time picking on MS source location in a tunnel. A total of 1000 MS sources, 12,000 arrival-time pickings, and particle swarm optimization are used. Accordingly, 3796 source-locating operations are conducted for each MS source. The results show that the number of arrival-time pickings and their mode of combination significantly affect the accuracy and the stability of MS source location in a tunnel. The optimal location cannot be obtained when using the arrival-time pickings with the smallest errors. The optimal location accuracy decreases with the increase in the number of arrival-time pickings; however, the average location accuracy increases therewith. The source location accuracy significantly changes when the number of arrival-time pickings is small. The location result becomes more stable with the increasing number of arrival-time pickings. Accordingly, a suggestion involving the usage of all arrival-time pickings for MS source location in a tunnel is provided. The results will contribute to rock mass stability monitoring and disaster warning in tunnels using the MS monitoring technique.

An effective polarity correction method for microseismic migration-based location

Microseismic methods are important tools for monitoring the status and consequences of hydraulic fracturing. Because microseismic data recorded at the surface have a low signal-to-noise ratio, migration-based algorithms are widely used to determine the locations of microseismic events. However, there may be polarity changes in waveforms at different receivers due to the source mechanisms, which will cause the stacking images to not reach a maximum at the event location. One way for polarity correction is to perform the source mechanism and the source location inversions simultaneously, which, however, is computationally expensive and not good for real-time monitoring. We have developed an effective polarity correction method in the data domain for migration-based location methods called the polarity correction migration-based (PCM) method. This method uses an amplitude trend least-squares fitting procedure to determine the polarities along the receiver line with low additional computational cost. Then, the fitted waveform polarities are used to convert the signs of the amplitude values to stack them consistently. Due to curve fitting, this method is more suitable for microseismic data acquired with regular arrays than with scattered arrays. Numerical experiments of synthetic and real data sets demonstrate that the proposed PCM method can improve accuracy in the detection and location of microseismic events.

Antinoise performance of time-reverse imaging conditions for microseismic location

A suitable imaging condition is critical for the success of seismic imaging or source location. To understand what imaging condition to select for handling noisy data, the antinoise performance of the maximum amplitude imaging condition (MAIC), the autocorrelation imaging condition (ACIC), and the geometric mean imaging condition (GMIC) were comparatively studied. Synthetic microseismic data based on the Marmousi2 model, with different levels of synthetic Gaussian noise and field noise separately added, were used for tests. For Gaussian noise data, five signal-to-noise (S/N) ratio levels were considered, ranging from an absolutely clean level of [Formula: see text] to an extremely noisy level of [Formula: see text], in an increment of five times of the lower level of S/N. It was found that the antinoise ability of MAIC outperforms ACIC, and ACIC outperforms GMIC. This conclusion was confirmed to be valid for field noise in the further experiments performed, using 16 groups of industrial noise recordings from different areas. The statistical analysis shows these performance differences are statistically consistently significant. In terms of spatial resolution, it is the other way around; that is, GMIC outperforms ACIC, and ACIC outperforms MAIC. These suggest that in choosing a suitable imaging condition for time-reverse imaging location, one needs to consider the balance between the resolution demand and data quality requirement. If the data quality is very high, GMIC may be used to achieve a high-resolution location result. Conversely, if the data quality is poor, MAIC is a good choice for obtaining a robust location result. In between, ACIC or grouped GMIC is a proper approach to work out a balanced result for resolution demand and the noisy level provision.

Evaluation of flow paths during stimulation in an EGS reservoir using microseismic information

Evaluation of fluid flow behavior is important for understanding various phenomena in a geothermal reservoir. Microseismic monitoring and location can be used to track pore pressure migration to some extent; however, microseismic information cannot directly provide the detail of flow behavior, such as how fluid flow occurs in the reservoir. In this study, we propose a new method to evaluate the entire flow-path system in a fractured reservoir. We first estimate the pore-pressure increase for each microseismic event based on microseismic and in-situ stress information. We extend the discrete pore pressure data to a continuous distribution based on the idea of a main flow pathway and sub flow pathways. We also introduce a honeycomb-shaped flow pathway model, which consists of flow pathways and nodes. Hydraulic parameters are different between different flow pathways. To match observed pore pressures at the nodes, an optimization process is used to adjust the hydraulic parameters for each flow pathway. We get a proxy of the hydraulic conductivity distribution based on the flow pathway model, that can evaluate flow pathways and flow behavior. We apply this method to hydraulic stimulation and microseismic data from the Basel enhanced geothermal system (EGS) project. Our method successfully delineated the main permeable zone and caught the tendency of fluid flow behavior that is also indicated by other microseismic analysis. Our method can catch the qualitative behavior of fluid flow in the geothermal reservoir and can contribute to designing geothermal energy extraction systems and forecasting.

Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

Microseismic source location is a fundamental research interest in real-time microseismic monitoring and hazard risk assessment, and it provides the basis for determining the fracture zones and calculating seismic source parameters of the microseismicity (e.g., the event magnitude, the focal mechanism). In the present work, we carefully relocate some microseismic earthquakes that occurred in the Yongshaba mine (China) using the shooting 3-D ray-tracing (3D-RT) method based on a high-resolution 3-D velocity model, which is determined by a large amount of seismic data. Furthermore, a semiautomatic waveform cut method based on the cross-correlation (CC) technique (WCC) is developed for a quick, robust and precise determination of the direct P-phase relative delay times. Compared with the full waveform cross-correlation (FWCC)-based method, the WCC-based method is free from the influence of complex later-phase waveform spectra. To verify the proposed method, we artificially generate the locations of 892 synthetic microseismic events, the P-phase travel times of which are calculated based on the given 3-D velocity model and the 3-D ray-tracing technique. The relocated hypocentres of these synthetic events obtained by the developed method are improved compared with those obtained by a homogeneous velocity model and the straight line-ray tracing based L1 norm time difference (TD) method (HV-SL-TD1), as well as those obtained by the 3-D velocity model and the straight line-ray tracing based L1 norm and L2 norm TD methods (3D-SL-TD1 and 3D-SL-TD2). Finally, we apply the proposed method to eight experimental blasts with pre-measured locations. The synthetic and application tests show that, despite some multi-path phenomena existing in the ray-tracing methods, the WCC-based method performs as well as the experienced manual picking method, and the results obtained by the 3D-RT location have smaller location errors (~30 m) than those of the homogeneous velocity model-based methods (>40 m). The TD-based method is slightly better than the trigger time (TT)-based method when using the same norm, and the location precision of the L1 norm-based methods have a better resilience to larger picking errors than that of the L2 norm-based methods. Further improvements can be obtained by improving the resolution of the 3-D velocity model and including spatial voids in the model (e.g., tunnels and cavities) for the inversion.

Minimum semblance weighted stacking with polarity correction for surface microseismic data processing

Diffraction-stack-based algorithms are the most popular microseismic location methods for surface microseismic data. They can accommodate microseismic data with low signal-to-noise ratio by stacking a large number of traces. However, changes in waveform polarity across the receiver line due to source mechanisms may prevent stacking methods from locating the true source. Imaging functions based on simple stacks have low resolution, producing large uncertainty in the final location result. To solve these issues, we introduce a minimum semblance weighted stacking method with polarity correction, which uses an amplitude trend least-squares fitting algorithm to correct the polarity across the receiver line. We adapt the semblance weighted stacking for better coherency measure to improve the imaging resolution. Moreover, the minimum semblance is used to further improve the resolution of location results. Application to both synthetic and real data sets demonstrates good performance of our proposed location method.

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

Abstract. 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.

Micro-seismic source location determined by a modified objective function

The exact localization of micro-seismic sources is of significant importance in micro-seismic monitoring technology. The methods determining arrival time and time difference of micro-seismic source locations in engineering are introduced. The factors affecting the accuracy of the micro-seismic source location are analysed. To improve the accuracy of micro-seismic source localization, an objective function is used to examine the source, and a new method for locating micro-seismic sources is proposed. To make full use of the monitoring data of each geophone, the L2-norm and variance function are combined to improve the overall accuracy of the positioning and the stationarity of the objective function of each geophone equation. Finally, a particle swarm optimization is employed to search for the source location. The effectiveness of the improved method is verified by two case studies, and the results indicate that the proposed method is better than conventional approaches. The proposed method is simple and easy to perform.

Downhole Microseismic Source Location Based on a Multi‐Dimensional DIRECT Algorithm for Unconventional Oil and Gas Reservoir Exploration

Downhole microseismic data has the significant advantages of high signal‐to‐noise ratio and well‐developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy. In this study, we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system. Synthetic tests inidcate that the objective function of locations can be defined as a multi‐dimensional matrix space by employing the global optimization DIRECT algorithm, because it can be run without the initial value and objective function derivation, and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension. This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions. Therefore, the methodology, based on a multidimensional DIRECT algorithm, can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution, which is more suitable for real‐time processing of a large amount of downhole microseismic monitoring data.

Reverse travel time imaging of microseismic location

ABSTRACTThe data sources used in microseismic locating include mainly the arrival time or waveform (wavefield value) in the microseismic record, and the algorithms used are mainly inversion or reverse time imaging. In terms of data source and algorithm, previously available methods generally fall into three categories: travel time inversion, waveform inversion, and reverse time imaging based on the wavefield. In this study, we propose a new reverse imaging method based on travel time. Starting with time invariance, we use the travel time of the back-propagated wavefield as a data source for imaging source location. We calculate the travel times from one receiver to all grid points directly rather than pick arrival times, and refer to this as the reverse travel time field (RTTF). The method of using the RTTFs of all receivers for imaging is called reverse travel time imaging (RTTI). RTTI images the whole research area and so overcomes the problem of a local minimum in travel time inversion. RTTI pre-prepares RTTF and there is no need to calculate travel time during imaging, which helps maintain the high computational efficiency as travel time inversion. The effectiveness, adaptability and resistance to some interfering factors in RTTI are tested using 2D acoustic numerical experiments.

Microseismic event estimation and velocity analysis based on a source-focusing function

Attaining information corresponding to the microseismic source location helps in understanding the reservoir fracturing process. Time-reversal-based migration methods are widely used to obtain a source image directly. Such source-locating methods share a fundamental weakness: The accuracy of the source image depends highly on the accuracy of the velocity model. Full-waveform inversion (FWI) has been used in microseismic data to include an optimization of the source image and velocity model, simultaneously. However, such inversions are vulnerable to cycle-skipping problems, more so when the source location and source origin time are unknown. The computational cost of FWI is also high. To solve these problems, we have developed a source-focusing function as an additional objective to optimize the velocity model and source image. This objective function used to measure the source-image focusing property is defined by the estimated source-location coordinate and the source image. The source image is poorly focused if the velocity is inaccurate, and the objective function reaches a minimum when the velocity is accurate. We use the geometric-mean imaging condition to get a high-resolution source image at each iteration. Then, we use this source image to calculate the estimated source-location coordinate and the gradient of the objective functional with respect to the velocity. The optimized velocity can improve the source-image quality. In the end, the final output velocity and source image will allow us to fit the objective for all these attributes of the model and source image. Using synthetic data generated from the 2D Marmousi and the 3D overthrust models, we highlight the cycle-skipping immune and high-resolution features of our method. The application to field data also indicates that our method can improve the velocity model and source-image quality.

Gravitational Search Algorithm for Microseismic Source Location in Tunneling: Performance Analysis and Engineering Case Study

Microseismic source location (MSL) provides crucial information for the interpretation of rock mass stability and early warning of rock mass hazards. The accuracy of MSL mainly depends on the formation of the sensor array, the multi-velocity model, and the locating algorithm. Especially, the choice of algorithm plays a decisive role, which requires both optimal accuracy and efficiency for searching the global optimal solution. In this paper, an advanced heuristic algorithm, Gravitational Search Algorithm (GSA), is applied for MSL in tunnel engineering. A standard framework of the GSA-based searching process is first built. Its accuracy, stability, and speed of convergence are rigorously compared and analyzed with particle swarm optimization and simplex algorithm using synthetic and real microseismic data. Four types of equivalent velocity models are combined with the searching algorithm to further discuss the applicability and performance of GSA in different situations. The studies show that for all the cases, GSA has the highest speed of convergence with the best accuracy. Locating errors are controlled within 10 m, which fulfills the requirement of engineering accuracy. A representative case study is conducted using microseismic data prior to a major rockburst in a twin-tube highway tunnel. The calculated cluster of seismic events using GSA-based algorithm well matches the actual unstable areas. This work indicates that GSA is an optimal algorithm for microseismic source location and rockburst warning in tunneling.

Waveform Inversion-Assisted Distributed Reverse Time Migration for Microseismic Location

We present a novel approach to locate microseismic sources in situ and in real time in distributed sensor networks (DSN). We propose a distributed reverse time migration (RTM) microseismic source location algorithm. RTM-based methods have advantages in passive source location in terms of robustness and accuracy. However, the traditional methods have a centralized data collection and ex situ postprocessing style, and were not designed for in situ and real-time seismic imaging, so communication and computation costs were not considered. Utilizing newly emerging DSN, real-time and in situ microseismic source location becomes possible. Thus, we specially design a joint imaging condition for the DSN system to reduce both computation and communication burdens. The tradeoff, however, is location resolution reduction. Sequentially, we employ a waveform inversion approach to obtain a finer resolution microseismic source location result. Finally, we validate the proposed method using both synthetic and field seismic records. Our approach shows promising performances using synthetics with single and multiple sources, as well as with field data. The proposed waveform inversion-assisted distributed RTM location algorithm obtains high-resolution source location results and significantly reduced communication and computation costs, even at a low signal-to-noise ratio.

The effect of P- and S-phase arrivals on the accuracy of microseismic source location

ABSTRACT In this paper, the conventional microseismic monitoring is classified into two types: body and line monitoring cases. And the phase arrival conditions are classified into three types: 1) Only including P-arrivals and P-picks being in error; 2) Both including P- and S-arrivals and P-picks being in error; 3) Both including P- and S-arrivals and S-picks being in error. Then the effect of P- and S-phase arrivals on the accuracy of source location is studied under six conditions. In body monitoring case, the location accuracy will be improved dramatically when S-picks are added. In line monitoring case, the convergence rate of location function is dramatically improved when S-picks are added. This kind of phenomena is called P-picks effect. Then a pair of mutually exclusive hypothesizes are put forward based on P-picks effect. And another set of numerical tests are designed to verify which one is true. The tests results show that the location accuracy and the iterative convergence rate of the objective function can be dramatically improved when S-picks are added. What's more, this effect cannot be offset by increasing P-picks. To understand this effect deeply, the location model is simplified further. The properties of location functions with or without S-picks are studied and compared. And the S-picks effect is analyzed mathematically based on the simplified location model and the properties of objective function. Two field data tests, 81,307# workface microseismic monitoring of Baode Coal Mine and 4# branch tunnel microseismic monitoring of Hanjiang-to-Weihe River Valley Water Diversion Project, are analyzed to verify the conclusion that the participation of S-picks can improve converge rate and location accuracy. In both cases, the location results with P- and S-picks are consistent with the rock mechanics theoretical results. Both of them confirm that S-picks are indispensable in source location.

Waveform-based microseismic location using stochastic optimization algorithms: A parameter tuning workflow

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.

Artificial Seismic Source Field Research on the Impact of the Number and Layout of Stations on the Microseismic Location Error of Mines

A site experiment is performed herein within a 100 m range using a high‐frequency structure activity monitor to explore the impact of different factors on the microseismic source location and analyze the range of influence of the velocity model, number of stations, and array surface on the seismic source location. Moreover, the impact of wave velocity, velocity‐free location algorithm, and position of the seismic source on the microseismic location error of mines is discussed by establishing the ideal theoretical model of the wave velocity location and with particle swarm optimization. The impact of the number of stations and tables on the location precision is also explored by using the microseismic signals produced by the artificial seismic source. The results show that, for the location model containing the velocity, the velocity error would greatly affect the location precision, and the velocity‐free algorithm receives good location results. The location result is more satisfactory when the seismic source point falls in between array envelope lines. The seismic source location precision is in direct proportion to the number of stations. According to the experiment, within a 100 m range, when the number of stations is over 12, the effect does not significantly grow with the increase of stations; the number of tables affects the location precision; and the multitable location effect is significantly superior to the single‐table effect. The research shows that the optimal station density is 0.0192%, and the appropriate sensor layout to form a multitable monitoring network may effectively enhance the microseismic source precision of mines through the selection of a velocity‐free location model. On the contrary, the number of stations can be reduced on the premise of the allowable error of the seismic source location, which may effectively reduce the monitoring cost.