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.
The shallow coal resource in China has already been exhausted in recent years; hence, the shift to deep coal mining is inevitable. The average mining depth of mines in China has already reached 700 m, and the number of deep shafts would increase year by year. However, a more complicated and dangerous stress environment would lead to more dynamic disasters in coal mines. Mine earthquake is one of these primary disasters, which usually give rise to a series of secondary disasters and result in a tremendous loss of personnel and property. Therefore, mine earthquake has become a challenge for both Chinese and foreign scholars and institutions [
Most of the current methods of microseismic location have been developed from earthquake locations. Accordingly, many scholars conducted studies on the location algorithm and how to improve the location precision. Several research directions on the decrease of the location error, such as the location algorithm [
In conclusion, substantial research results have already been achieved for the microseismic source location. However, the abovementioned research mainly aims at the algorithm of a limited number of stations and station layout, and few comprehensive studies focused on the impact of comprehensive factors in a high-density station layout on the location error. In this study, an in situ microseismic monitoring experiment is conducted within a 100 m range for the first time with the high-frequency structure activity monitor to improve the microseismic location precision. Moreover, the impact of the location target function, velocity error, seismic source position, number of stations, and number of tables on the location precision is analyzed and studied by applying the theoretical analysis and field experimentation.
Microseismic monitoring has become one of the effective methods of dynamic disaster control for all kinds of mines across the country, but location precision has always been a difficult problem to resolve. High-density station monitoring is considered an effective solution, but research on this topic is seldom performed in mines. Small-range microseismic monitoring is performed in this research. Moreover, a microseismic experiment site is built for the high-density station microseismic monitoring experiment, which can provide a theoretical basis for mine earthquake research.
A gold mine in Fuxin was selected for the small-range microseismic monitoring experiment. The geographic coordinates of the mine are as follows: east longitude, 121°43′04″–121°43′06″, and north latitude, 41°53′03″–41°53′04″. The surface soil of the coal mine is mild clay, and rocks in the mine mainly include biotite plagioclase mylonite and felsic mylonite, which are solid and of good integrity. The mines are explored through vertical and inclined shafts in a single cage with a balanced weight. Coal mining currently has four midlevels, namely, mid-223 m, mid-180 m, mid-140 m, and mid-100 m. Among which, the first (mid-223 m) has already been emptied and abandoned. The top-down horizontal sublevel mining method is adopted according to the status of mine production and the hoisting condition of the ore body. Underground mining is being adopted at the present, and vertical shaft-blind inclined shaft is employed for codevelopment based on the full utilization of the original development system. A vertical shaft is employed for transportation from the surface to 180 m below the ground, while an inclined shaft is adopted from 180 m to 140 m and 180 m to 100 m plane. The experiment section is the plane of 140 m, 180 m, and 100 m.
Experiment monitoring includes sensors, cables, a 12-channel data hub, and a 48-channel data recorder for constructing the physical deformation site (Figure
Site monitoring equipment layout. (a) Data recorder. (b) Data hub.
Sensors were mainly laid out in three planes and two inclined shafts. The vertical size of the distribution region was 80 m, while the horizontal size was between 100 m and 200 m. The array mainly consisted of 33 sensors. Table
Sensor location information.
Plane | Number | Data hub and interface serial number | Sensor number | Coordinates (m) | |||
---|---|---|---|---|---|---|---|
|
|
|
|||||
180 m plane | 6 | 1 | 3 | 180103 | 8835.730 | 6146.960 | 180.816 |
4 | 180104 | 8832.982 | 6121.473 | 180.760 | |||
5 | 180105 | 8851.786 | 6124.607 | 180.740 | |||
6 | 180106 | 8854.521 | 6108.712 | 180.675 | |||
7 | 180107 | 8879.863 | 6114.670 | 180.968 | |||
8 | 180108 | 8834.896 | 6103.363 | 180.469 | |||
|
|||||||
Short inclined shaft | 3 | 1 | 9 | 110101 | 8883.636 | 6197.365 | 150.355 |
10 | 110102 | 8878.167 | 6185.213 | 156.285 | |||
11 | 110103 | 8867.835 | 6160.976 | 169.305 | |||
|
|||||||
140 m plane | 12 | 2 | 1 | 140201 | 8860.325 | 6241.630 | 141.353 |
2 | 140202 | 8879.256 | 6235.671 | 139.650 | |||
3 | 140203 | 8902.053 | 6225.357 | 139.590 | |||
4 | 140204 | 8924.206 | 6221.107 | 139.550 | |||
5 | 140205 | 8924.527 | 6201.975 | 139.608 | |||
6 | 140206 | 8918.945 | 6182.133 | 139.723 | |||
7 | 140207 | 8908.220 | 6174.085 | 139.716 | |||
8 | 140208 | 8919.876 | 6314.837 | 139.925 | |||
9 | 140209 | 8927.746 | 6244.410 | 139.580 | |||
10 | 140210 | 8932.245 | 6261.876 | 139.619 | |||
11 | 140211 | 8932.629 | 6279.137 | 139.628 | |||
12 | 140212 | 8922.516 | 6308.026 | 139.820 | |||
|
|||||||
100 m plane | 6 | 3 | 1 | 100301 | 8851.300 | 6321.265 | 100.070 |
2 | 100302 | 8858.368 | 6321.290 | 101.796 | |||
3 | 100303 | 8868.516 | 6315.367 | 100.398 | |||
4 | 100304 | 8882.840 | 6324.850 | 101.010 | |||
5 | 100305 | 8925.043 | 6335.871 | 100.521 | |||
6 | 100306 | 8894.740 | 6323.830 | 100.150 | |||
|
|||||||
Long inclined shaft | 6 | 3 | 7 | 111301 | 8838.087 | 6170.980 | 171.156 |
8 | 111302 | 8840.036 | 6193.240 | 160.305 | |||
9 | 111303 | 8841.736 | 6212.716 | 150.793 | |||
10 | 111304 | 8845.730 | 6264.803 | 125.880 | |||
11 | 111305 | 8847.221 | 6284.368 | 115.830 | |||
12 | 111306 | 8848.507 | 6308.935 | 103.820 |
180 m plane and inclined shaft sensor layout.
140 m plane sensor layout.
100 m plane sensor layout.
Sensor and artificial source space layout.
Three microblasting experiments were performed, and the blasting location was set in three operation planes. A monitoring station was set near the artificial seismic source to precisely collect the seismic moment. The arrival time difference of this station and the other monitoring stations was calculated and became the travel time of the seismic signal spent in reaching the detection station. Table
Microblasting source.
Seismic source no. | Location | Number | Explosive amount | Coordinates (m) | Example of the signal waveform | ||
---|---|---|---|---|---|---|---|
|
|
|
|||||
1 | 180 m plane | 1 | 300 g | 8882.635 | 6127.475 | 181.028 |
|
2 | 140 m plane | 1 | 300 g | 8917.753 | 6171.168 | 139.620 | |
3 | 100 m plane | 1 | 400 g | 8870.609 | 6316.663 | 100.096 |
The monitoring result showed that only the No. 2 blasting at the 140 m plane and the No. 3 blasting at the 100 m plane were well received. The No. 1 seismic signal at the 180 m plane and the effective acceptance amount of stations were quite dissatisfactory and thus abandoned. The analysis implied that this finding may be caused by the remote seismic location, the small amount of explosive, and the dense transportation channels, which affected the signal transmission from the artificial seismic source to all the sensors. Figure
Microblasting signal. (a) No. 2 seismic source signal at the 100 m plane. (b) No. 3 seismic signal at the 140 m plane.
Suppose the microseismic monitoring region is a cubic area of which the side length is 1000 m. The sensors are distributed at the eight vertexes, whose coordinates (unit: m) are A (0, 0, 0); B (1000, 0, 0); C (1000, 1000, 0); D (0, 1000, 0); E (0, 0, 1000); F (1000, 0, 1000); G (1000, 1000, 1000); and H (0, 1000, 1000). The equivalent velocity of the wave transmission in the medium in the theoretical model is simplified as
Model of the monitoring region.
Particle swarm optimization is an emerging evolutionary algorithm, which constantly collects the direction and speed of search according to the flight course and information transfer between swamps. The search process is mainly accomplished by relying on the interactions and the mutual influence of particles featuring easy implementation, fast convergence, and high precision. The free flight of “particle swarm” in the solution space can perfectly resolve the problem of the final solution being the local optimal solution. The updated versions of the speed and the location of particle
The location was worked out through the MATLAB PSO toolbox with the following PSO parameter settings: learning factor:
This study located the two target functions of the known wave velocity and the unknown wave velocity through the ion swarm algorithm to explore the effects of different location methods on the location error. Equation (
(a) Location error distribution under the known wave velocity. (b) Location error distribution under the unknown wave velocity.
Figure
The P-wave velocity of the rock samples in the experimental area was first required to be determined by an ultrasonic velocity meter based on the known wave velocity model for the location. Differences between the given wave velocity and the actual wave velocity were observed. Therefore, the errors that are ±2%, ±5%, ±10%, and ±15% are applied to disturb the original wave velocity
Location distance error distribution of different wave velocity errors.
Figure
Figure
Location distance error distribution of different positions.
The particle swarm algorithm was applied to undertake the source location to the microexplosion performed on the site. The location parameters are as follows: location area coordinates
Different station sensors, including 180 m plane, 140 m plane, 100 m plane, inclined shaft (long and short inclined shafts), and some plane sensors, were successively applied for the location to analyze the high-density array for the location performance of microseismic monitoring. The unknown wave velocity location was adopted for a better location performance (Figure
Relationship between the location error and the sensor position. (a) 100 m plane seismic source location error. (b) 140 m plane seismic source location error.
Four sensors were selected from all the sensors in the array to verify the impact of the monitoring array density on the location performance and avoid locating in a single plane. The sensors at the other positions were successively superimposed to select different sensor quantities and perform an unknown wave velocity location for the microblasting on the 140 m plane. Three sets of data were selected to take the mean value for the location. Figure
Relationship between the location error and the number of stations.
In the above calculation, the P-wave velocities of the 140 m plane and the 100 m plane obtained from the inversion of the whole array were 5898 m/s and 5903 m/s, respectively, which were rather different from the results of the indoor experiment. In addition, compared with the model analysis results, the location error of the actual monitoring was larger and related to the complexity of a geological structure. The location, size, and direction of various discontinuity surfaces, such as fractures, joints, and faults in the rock mass, all influence the wave propagation in the medium. Inevitably, errors were found in the arrival time required for the location because the time was acquired from the waveform recorded in the noise environment through phase identification. On the one hand, the high-density array microseismic monitoring can extend the quantity of the arrival time data to reduce the significant impact of the arrival time error. On the other hand, the high-density station, especially when being three-dimensionally laid, decreases the travel time distance from the source to the various sensors and reduces the difference between space and time and the theoretical calculation of the travel time data caused by the inhomogeneity of the rock mass. In summary, benefitting from the two abovementioned points, the high-density station can effectively improve the location performance of the microseismic source of the mine.
The following conclusions are obtained from this study: Theoretical modeling was used herein to analyze the target function with the wave velocity, nonwave velocity, and source location performance. Consequently, the wave velocity error had a great influence on the location result in the target function with the wave velocity, and such influence exponentially increased. The positioning error was small when the source point was at the envelope center of the monitoring array. The error was larger when the source point was outside the envelope. The influence of the station density on the location error was analyzed by a field experiment. The experimental results indicated that the influence of the station density can be divided into two parts: station quantity and station plane quantity. The location accuracy significantly increased with the increase of the station quantity when the station plane quantity was certain. When station quantity was more than 12, such an increase was no longer obvious. When the station quantity is certain, the increase of the station plane quantity can significantly improve the location accuracy. The accuracy of the multistation plane was higher than that of the single-station plane. The best station density was 0.0192%.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to express their gratitude to all those who helped them during the writing of this thesis. First of all, the author is grateful for the site and technical support of the manager and technical staff of the Fuxin Xinmin gold mine. Second, the authors are thankful to the postgraduates of Liaoning Technical University for their help in the experiments. Lastly, the authors would like to thank Editage (