Abstract
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.
Highlights
In recent years, with the rapid progress of the Internet of things (IoT) technology, intelligent sensing algorithm, and computer vision, accurate and fast search of large-scale spatial data has become a major problem perplexing researchers worldwide
The number of hash tables L, dimension k of hash function family, and interval size are analyzed in detail
The search engine location of the constructed database is conducted based on the locationsensitive hash (LSH) of P stable distribution
Summary
With the rapid progress of the Internet of things (IoT) technology, intelligent sensing algorithm, and computer vision, accurate and fast search of large-scale spatial data has become a major problem perplexing researchers worldwide. The advantage of using this kind of retrieval technology is that it can find large-scale data accurately without compressing it [1, 2] Combining this technology with microseismic focal point location can realize the balance between microseismic focal point calculation efficiency and positioning accuracy, providing a feasible scheme for fast microseismic focal point location. The advantage of building a search engine based on the LSH algorithm is that iterative calculation is not required when determining the focal point position, so as to reduce the damage caused by rockburst to rock mass [24]. Microseismic focal point location technology mainly determines the spatial position of the focal point through the vibration wave released by coal and rock mass.
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