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.

Microseismic Location Error Due to Eikonal Traveltime Calculation

The accuracy of computed traveltimes in a velocity model plays a crucial role in localization of microseismic events. The conventional approach usually utilizes robust fast sweeping or fast marching methods to solve the eikonal equation numerically with a finite-difference scheme. These methods introduce traveltime errors that strongly depend on the direction of wave propagation. Such error results in moveout changes of the computed traveltimes and introduces significant location bias. The issue can be addressed by using a finite-difference scheme to solve the factored eikonal equation. This equation yields significantly more accurate traveltimes and therefore reduces location error, though the traveltimes computed with the factored eikonal equation still contain small errors with systematic bias. Alternatively, the traveltimes can be computed using a physics-informed neural network solver, which yields more randomized traveltimes and resulting location errors.

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.

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.

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.