Crowdsensing location method of mining-induced seismicity based on the phone mobile sensor network
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摘要: 為提高礦震監測系統定位精度，減少監測盲區，降低監測成本，基于分布式的思想，提出一種基于手機移動傳感網絡的礦震定位方法。首先以礦區附近工人及家屬等使用的智能手機建立手機移動傳感網絡，其次對模擬震源點網格化，構建基于標準差的目標函數，提出改進的螢火蟲尋優策略，并使用拐點回溯法以及手機移動傳感網絡排除離散點策略(EDPS)降低定位誤差，最后通過礦震模擬實驗進行驗證。實驗結果表明：在手機移動傳感網絡無到時誤差理想情況下，所有模擬震源點都能夠準確收斂至震源位置，定位誤差小于1 m。但手機相較于檢波器到時誤差較高，且定位誤差與到時誤差具有相關性，當手機到時誤差為?1.0～1.0 s時，傳統算法定位誤差為216 m，無法實現高精度定位。通過研究目標函數值與定位誤差間的關系，提出并使用拐點回溯法以及EDPS兩種優化方法，算法絕對定位誤差降低至73 m，當到時誤差為?0.2～0.2 s時，絕對定位誤差降低至17 m，定位精度提高76.1%。基于手機移動傳感網絡的礦震群智定位方法，為礦震監測提供了一種新方法，未來可考慮與井下微震系統聯合，在節省監測成本、提高定位精度方面具有重要意義。Abstract: To improve the positioning accuracy of a mining-induced seismicity monitoring system, reduce the monitoring blind area, and reduce the monitoring cost, based on the distributed idea, this paper proposes a positioning method of mining-induced seismicity based on the smartphone sensor network. First, smartphones used by workers and their families near the mining area were utilized to establish a mobile sensor network. Second, the simulated source points were meshed, and the objective function based on the standard deviation was constructed. An improved firefly optimization strategy was proposed. The inflection point backtracking method and smartphone sensor network exclude the discrete points strategy, namely, EDPS, to reduce the positioning error. Verification is done by the simulation experiment of the mining-induced seismicity location. Experimental results show that under the ideal condition of no arrival time error in the smartphone sensor network, all simulated source points can converge to the source position accurately with a positioning error of less than 1 m. However, compared to the detector, the arrival error of the smartphone is higher, and the positioning error is correlated with the arrival error. When the mobile phone arrival error is ?1.0–1.0 s, the traditional algorithm positioning error is 216 m, which cannot achieve high-accuracy positioning. Researching the relationship between objective function value and positioning error, this work proposes and uses two optimization methods: (1) inflection point backtracking method and (2) EDPS. The absolute positioning error of the algorithm is reduced to 73 m. When the time error is ?0.2–0.2 s, the absolute positioning error is reduced to 17 m, and the positioning accuracy is improved by 76.1%. The location method of the mining-induced seismicity based on the crowdsensing of a phone mobile sensor network provides a new method for mining-induced seismicity monitoring. It can be considered to combine with an underground microseismic system in the future, which is of great significance in saving the monitoring cost and improving the positioning accuracy.
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圖 1 礦震發生過程手機移動傳感網絡觸發示意
Figure 1. Trigger diagram of the mobile phone sensor network in the process of a mine earthquake
圖 2 手機移動傳感網絡礦震監測與群智定位流程
Figure 2. Flow chart of mining-induced seismicity monitoring and crowdsensing positioning based on the mobile phone sensor network
圖 4 采用兩種移動率時移動距離和保留距離的變化情況. (a)采用靜態移動率；(b)采用動態移動率
Figure 4. Change of the moving distance and reserved distance with two moving rates: (a) using static movement rate;(b) using dynamic movement rate
圖 5 最優模擬震源點F隨迭代次數增加的移動情況. (a) 目標函數值與定位誤差的對應關系; (b) 模擬震源點移動情況示意圖
Figure 5. Movement of the optimal simulated source point F with the increase of the number of iterations: (a) correspondence between objective function value and positioning error; (b) schematic diagram of the movement of the simulated earthquake source point
圖 9 無到時誤差模擬震源點移動情況. (a) 模擬震源點移動5次的情況; (b) 模擬震源點移動10次的情況; (c) 模擬震源點移動15次的情況; (d) 模擬震源點移動20次的情況; (e) 模擬震源點移動40次的情況; (f) 模擬震源點移動60次的情況
Figure 9. Simulation of the source point movement without time error: (a) simulation of 5 movements of the earthquake source point; (b) simulation of 10 movements of the earthquake source point; (c) simulation of 15 movements of the earthquake source point; (d) simulation of 20 movements of the earthquake source point; (e) simulation of 40 movements of the earthquake source point; (f) simulation of 60 movements of the earthquake source point
圖 10 ?1.0～1.0 s到時誤差模擬震源點移動情況. (a) 模擬震源點移動5次的情況; (b) 模擬震源點移動10次的情況; (c) 模擬震源點移動15次的情況; (d) 模擬震源點移動20次的情況; (e) 模擬震源點移動40次的情況; (f) 模擬震源點移動60次的情況
Figure 10. Simulation of the source point movement under ?1.0–1.0 s time error: (a) simulation of 5 movements of the earthquake source point; (b) simulation of 10 movements of the earthquake source point; (c) simulation of 15 movements of the earthquake source point; (d) simulation of 20 movements of the earthquake source point; (e) simulation of 40 movements of the earthquake source point; (f) simulation of 60 movements of the earthquake source point
圖 11 使用拐點回溯法以及EDPS多次計算情況. (a) 二次計算定位情況；(b) 三次計算定位情況；(c)四次計算定位情況
Figure 11. Positioning by the inflection point backtracking method and EDPS repeatedly: (a) second calculation of the positioning situation; (b) third calculation of the positioning situation; (c) fourth calculation of positioning situation
圖 12 智能手機到時分布. (a) 使用EDPS前智能手機到時與X、Y軸的關系 (b) 使用EDPS后智能手機到時與X、Y軸的關系
Figure 12. Distribution graph of the smart phone arrival time: (a) relationship between smartphone arrival and X, Y axes before using EDPS; (b) relationship between smartphone arrival time and X, Y axes after using EDPS
圖 13 目標函數與定位誤差的相互關系
Figure 13. Relationship between the objective function value and positioning error value
表 1 智能手機的位置與到時信息
Table 1. Location and arrival information of smartphones
Phone number Smartphone information Phone number Smartphone information X/m Y/m Z/m Arrival time/s X/m Y/m Z/m Arrival time/s 1 8555.3 1381.7 1019.3 11.713 7 3956.3 8251.9 1014.6 10.692 2 6564.1 3225.4 1006.4 11.151 8 3247.2 4611.9 1004.2 11.159 3 4140.8 57.6 1001.1 12.063 9 3441.4 5677.5 1011.8 10.951 4 988.0 3911.4 1000.4 11.731 10 4117.2 1871.1 1019.7 11.623 5 5001.9 1018.5 1008.2 11.765 11 7305.1 8095.4 1012.3 10.273 6 3763.9 75.9 1010.6 11.071 12 5571.1 2186.3 1011.4 10.298 
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表 2 不同震源位置的計算結果
Table 2. Calculation result of different source positions
Microseismic events True focal location/m Calculated focal location/m Error value/m Event 1 (3002.0,436.0,726.0) (3002.0,436.0,726.0) 0.00782 Event 2 (2002.0,336.0,126.0) (2001.9,335.9,126.0) 4.8e-05 Event 3 (8123.0,6036.0,526.0) (8122.9,6036.0,527.1) 0.13691 Event 4 (5433.0,7566.0,126.0) (5433.1,7565.9,126.3) 0.38932 Event 5 (9520.0,7500.0,826.0) (9519.9,7499.9,826.1) 0.14616
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表 3 普通螢火蟲和優化螢火蟲定位結果對比
Table 3. Comparison of location results between the common firefly and optimized firefly location algorithm
Arrival time error/s True focal location/m Common firefly location algorithm Optimized firefly location algorithm Location results /m Objective function value Error value/m Location results/m Objective function value Error value/m ?1.0–1.0 (6500.0,7630.0,520.0) — Non convergence >1000 (6657.2,7491.7,573.8) 0.3819 216 ?0.8–0.8 (6500.0,7630.0,520.0) — Non convergence >1000 (6392.5,7670.7,674.1) 0.2543 192 ?0.6–0.6 (6500.0,7630.0,520.0) — Non convergence >1000 (6548.1,7731.3,429.1) 0.1561 144 ?0.4–0.4 (6500.0,7630.0,520.0) (7065.2,8156.6,427.1) 1.2266 997 (6527.7,7655.2,416.2) 0.0771 110 ?0.2–0.2 (6500.0,7630.0,520.0) (6649.1,7796.9,694.2) 0.8654 488 (6533.9,7620.1,457.5.4) 0.0522 71
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表 4 EDPS算法執行結果
Table 4. Results of the EDPS algorithm execution
Count time True focal location/m Calculated focal location/m Objective function value Error value/m Phone number 1 (6500.0,7630.0,520.0) (6657.2,7491.6,573.8) 0.3819 216 300 2 (6500.0,7630.0,520.0) (6508.3,7491.7,537.2) 0.2220 139 250 3 (6500.0,7630.0,520.0) (6464.5,7605.8,608.1) 0.1178 98 200 4 (6500.0,7630.0,520.0) (6537.6,7615.8,580.9) 0.0569 73 150
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表 5 不同到時誤差下算法執行情況
Table 5. Algorithm execution under different time errors
Arrival time error/s True focal location/m None EDPS calculated focal location/m Objective function value Error value/m With EDPS calculated focal location/m Objective function value Error value/m ?1.0–1.0 (6500.0,7630.0,520.0) (6657.2,7491.7,573.8) 0.3819 216 (6537.6,7615.8,580.9) 0.0569 73 ?0.8–0.8 (6500.0,7630.0,520.0) (6392.5,7670.7,674.1) 0.2543 192 (6459.8,7653.6,558.0) 0.0493 60 ?0.6–0.6 (6500.0,7630.0,520.0) (6548.1,7731.3,429.1) 0.1561 144 (6486.8,7600.5,554.1) 0.0411 47 ?0.4–0.4 (6500.0,7630.0,520.0) (6527.7,7655.2,416.2) 0.0771 110 (6507.3,7622.8,501.4) 0.0174 21 ?0.2–0.2 (6500.0,7630.0,520.0) (6533.9,7620.1,457.5.4) 0.0522 71 (6497.6,7618.6,507.4) 0.0078 17
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