WO2019019565A1 - Procédé d'identification de signal microsismique de mine fondé sur une caractéristique de distribution d'énergie - Google Patents

Procédé d'identification de signal microsismique de mine fondé sur une caractéristique de distribution d'énergie Download PDF

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Publication number
WO2019019565A1
WO2019019565A1 PCT/CN2018/072533 CN2018072533W WO2019019565A1 WO 2019019565 A1 WO2019019565 A1 WO 2019019565A1 CN 2018072533 W CN2018072533 W CN 2018072533W WO 2019019565 A1 WO2019019565 A1 WO 2019019565A1
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signal
microseismic signal
microseismic
energy distribution
identified
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Chinese (zh)
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张杏莉
卢新明
贾瑞生
彭延军
赵卫东
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Shandong University of Science and Technology
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Shandong University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616—Data from specific type of measurement
    • G01V2210/6161—Seismic or acoustic, e.g. land or sea measurements

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  • the invention belongs to the field of signal analysis and identification, and particularly relates to a mine microseismic signal identification method based on energy distribution characteristics.
  • Microseismic monitoring is an advanced and effective monitoring method for coal and rock dynamic disasters developed in recent years. It can monitor the microseismic activities of coal and rock mass in real time, continuously and online, and form microseismic monitoring data. Due to the complex environment of the mine, there are a lot of interference signals such as background noise and blasting vibration, which makes the microseismic monitoring system unable to accurately identify and record the effective microseismic events. Later, it is necessary to manually identify the effective microseismic events by technicians, which seriously affects the identification of the microseismic monitoring system. effectiveness. Because coal mine blasting operations often occur, and the micro-seismic and blasting vibration waveforms of coal and rock mass are very similar, the manual identification method often causes mishandling and is difficult to identify.
  • common time-frequency analysis methods for waveform identification of mine microseismic signals include Fourier transform, wavelet transform, wavelet packet transform, frequency slice wavelet transform and EMD.
  • Traditional Fourier transform is mainly used to analyze periodic stationary signals, including spikes and The random and non-stationary microseismic signal analysis of the mutation is not effective; the wavelet analysis can simultaneously perform time-frequency analysis, but the appropriate wavelet base needs to be selected to achieve better decomposition effect; EMD can process random non-stationary signals well.
  • the EMD method has boundary effects and modal aliasing, which leads to instability and non-uniqueness of EMD. These methods have a certain degree of disadvantages in signal analysis, which increases the difficulty of signal identification and high false positive rate.
  • the present invention proposes a mine microseismic signal identification method based on energy distribution characteristics, and uses time division mode decomposition (VMD) to perform time-frequency analysis on signals.
  • VMD is a new signal decomposition method. Compared with other modal decomposition techniques, it has a solid theoretical foundation, eliminates the modal aliasing problem, overcomes the shortcomings of the prior art, and has a good frequency domain adaptive decomposition. effect.
  • Step 2 Perform VMD decomposition on the identified microseismic signal x(t) to obtain K variable-variant modal components ⁇ u 1 ,...u k ,..., u K ⁇ arranged in descending order of frequency:
  • the differential seismic signal x(t) is decomposed into K variable modal components by VMD.
  • the constraint is to minimize the sum of the estimated bandwidths of the modalities, and the sum of the modalities is equal to the microseismic signal x(t) to be identified.
  • the constrained variational model is described as equations (1) and (2):
  • x(t) represents the microseismic signal to be identified
  • ⁇ (t) is a Dirac function
  • * represents a convolution
  • j 2 -1; in equation (2), To sum all the variational modes;
  • ⁇ is a quadratic penalty factor and ⁇ (t) is a Lagrangian multiplication operator
  • Step 2.1 defining the value of the number K of the variational modal component and the value of the penalty factor ⁇ ;
  • Step 2.2 Initialize
  • Step 2.4 Execute the first loop of the inner layer, and update u k according to formula (4);
  • Step 2.6 Execute the second loop of the inner layer, and update ⁇ k according to equation (5);
  • Step 2.8 Perform an outer loop to update ⁇ according to equation (6);
  • ⁇ is an update step size parameter of the Lagrangian multiplication operator ⁇ (t);
  • Step 2.9 Repeat steps 2.3 to 2.8 until the iterative stop condition is satisfied, as shown in equation (7), ending the entire loop, and outputting the result to obtain K variable modal components;
  • Step 3 Calculate the energy distribution vector P of the microseismic signal x(t) to be identified
  • the energy percentage value of the modal component u k can be obtained.
  • Step 4 Calculate the center of gravity coefficient cx of the energy distribution of the X-axis of the energy distribution of the microseismic signal x(t) to be identified;
  • Step 5 Identify the microseismic signal x(t) to be identified according to the identification threshold T. If cx>T is the microseismic signal of the mine rock mass rupture, cx ⁇ T is the blasting vibration signal;
  • Step 6 adaptively update the value of the identification threshold T
  • the identification threshold T is updated according to the system of equations (10):
  • W 1 is a set of cx values of the microseismic signal of the coal rock mass in the training concentrated
  • W 2 is a set of cx values of the vibration signal of the training concentrated blasting.
  • the present invention utilizes the characteristic difference of the energy distribution of the two microseismic signals, firstly reads the microseismic signal to be identified and performs the VMD decomposition, and obtains K according to the frequency from the high.
  • To the low-ordered variational modal components calculate the band energy of each modal component, extract the energy percentage value of each modal component from the original signal to form the energy distribution vector P; calculate the energy distribution based on the energy distribution vector P
  • the X-axis center-of-gravity coefficient cx identifies the mine microseismic signal according to the identification threshold T.
  • the detected microseismic signal is the mine rock mass rupture microseismic signal. If cx ⁇ T, the microseismic signal is detected as the blasting vibration signal.
  • the method can effectively identify the microseismic signals and blasting vibration signals of coal rock mass rupture.
  • the present invention adopts the above technical solutions, and has the following advantages compared with the prior art:
  • the invention automatically divides the micro-seismic signal of the mine, and according to the significant difference of the energy distribution of the micro-seismic signal and the blasting vibration signal of the coal-rock mass in different frequency bands, the X-axis center-of-gravity coefficient of the energy distribution of the microseismic signal is calculated.
  • the effective identification of the microseismic signals of the two types of mines is realized.
  • the method has the characteristics of simple algorithm, adaptability and real-time performance, and has good technical value and application prospect.
  • 1 is a flow chart of a mine microseismic signal identification method based on energy distribution characteristics.
  • FIG. 2 is a schematic diagram of a microseismic signal x(t) to be identified and a time-frequency diagram thereof.
  • FIG. 3 is a schematic diagram of six variational modal components obtained by decomposing the microseismic signal x(t) by VMD and its time-frequency diagram.
  • Figure 4 is a histogram of the energy distribution of the microseismic signal x(t) to be identified.
  • Figure 5 is a graph showing the energy vector, center of gravity coefficient and identification results of the 15 sets of coal rock mass fracture microseismic test signals.
  • Figure 6 is a graph showing the energy vector, center of gravity coefficient and identification results of 15 groups of blasting vibration test signals.
  • Figure 7 shows the classification and recognition results of the microseismic signals in the test group.
  • a mine microseismic signal identification method based on energy distribution characteristics the flow of which is shown in Figure 1, which specifically includes the following steps:
  • Step 2 Perform VMD decomposition on the identified microseismic signal x(t) to obtain K variable-variant modal components ⁇ u 1 ,...u k ,..., u K ⁇ arranged in descending order of frequency:
  • the differential seismic signal x(t) is decomposed into K variable modal components by VMD.
  • the constraint is to minimize the sum of the estimated bandwidths of the modalities, and the sum of the modalities is equal to the microseismic signal x(t) to be identified.
  • the constrained variational model is described as equations (1) and (2):
  • x(t) represents the microseismic signal to be identified
  • ⁇ (t) is a Dirac function
  • * represents a convolution
  • j 2 -1; in equation (2), To sum all the variational modes;
  • ⁇ is a quadratic penalty factor and ⁇ (t) is a Lagrangian multiplication operator
  • Step 2.1 defining the value of the number K of the variational modal component and the value of the penalty factor ⁇ ;
  • Step 2.2 Initialize
  • Step 2.4 Execute the first loop of the inner layer, and update u k according to formula (4);
  • Step 2.6 Execute the second loop of the inner layer, and update ⁇ k according to equation (5);
  • Step 2.8 Perform an outer loop to update ⁇ according to equation (6);
  • ⁇ is an update step size parameter of the Lagrangian multiplication operator ⁇ (t);
  • Step 2.9 Repeat steps 2.3 to 2.8 until the iterative stop condition is satisfied, as shown in equation (7), ending the entire loop, and outputting the result to obtain K variable modal components;
  • Step 3 Calculate the energy distribution vector P of the microseismic signal x(t) to be identified
  • the energy percentage value of the modal component u k can be obtained.
  • Step 4 Calculate the center of gravity coefficient cx of the energy distribution of the X-axis of the energy distribution of the microseismic signal x(t) to be identified;
  • Step 5 Identify the microseismic signal x(t) to be identified according to the identification threshold T. If cx>T is the microseismic signal of the mine rock mass rupture, cx ⁇ T is the blasting vibration signal;
  • Step 6 adaptively update the value of the identification threshold T
  • the identification threshold T is updated according to the system of equations (10):
  • W 1 is a set of cx values of the microseismic signal of the coal rock mass in the training concentrated
  • W 2 is a set of cx values of the vibration signal of the training concentrated blasting.
  • FIG. 4 is a histogram of the energy distribution of the microseismic signal, and the black solid in the figure The circle is the position of the center of gravity of the energy distribution plane of the microseismic signal.
  • test group 15 sets of coal rock mass rupture microseismic signals and 15 sets of blasting vibration microseismic signals are given respectively.
  • the energy vector, center of gravity coefficient and identification result of the coal rock rupture microseismic test signal are shown in Fig. 5; blasting vibration microseismic test
  • the energy vector, center of gravity coefficient and its identification result of the signal are shown in Fig. 6.
  • the test group has a total of 30 sets of microseismic signals, of which 29 groups are correctly identified, 1 group is identified incorrectly, and the correct rate of identification is 96.67%.
  • the classification and recognition results of the microseismic signals of the test group are shown in Fig. 7.
  • the microseismic signal is a non-stationary random signal, and its frequency distribution is relatively scattered.
  • the energy distribution of different types of microseismic signals is significantly different in different frequency bands. Therefore, according to this feature, the energy distribution vector of the microseismic signal can be extracted, and the center of gravity coefficient of the energy distribution can be calculated and identified. By comparing the threshold values, the classification and identification of the microseismic signals to be measured can be realized.

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Other Investigation Or Analysis Of Materials By Electrical Means (AREA)

Abstract

L'invention concerne un procédé d'identification de signal microsismique de mine fondé sur une caractéristique de distribution d'énergie, se rapportant au domaine technique de l'analyse et de l'identification de signaux. Le procédé consiste : à lire un signal microsismique x(t) à identifier; à effectuer une décomposition VMD sur x(t) afin d'obtenir un nombre K de composantes de mode variationnel rangées par ordre décroissant de fréquences; à calculer les énergies de bande des différentes composantes de mode, à extraire les valeurs de pourcentage d'énergie des différentes composantes de mode dans le signal d'origine afin de former un vecteur de distribution d'énergie P; à calculer un coefficient de centre de gravité d'axe X de distribution d'énergie cx en fonction du vecteur de distribution d'énergie P; à identifier le signal microsismique de mine en fonction d'un seuil d'identification T; si cx>T, le signal microsismique constitue un signal microsismique de rupture de masse rocheuse de charbon de mine; et si cx≤T, le signal microsismique constitue un signal de vibration de coup de mine; et enfin, à mettre à jour de façon auto-adaptative la valeur du seuil d'identification T. Le procédé permet de distinguer efficacement entre le signal microsismique de rupture de masse rocheuse de charbon et le signal de vibration de coup de mine, et présente les caractéristiques d'une forte auto-adaptabilité et d'une haute précision.
PCT/CN2018/072533 2017-07-26 2018-01-13 Procédé d'identification de signal microsismique de mine fondé sur une caractéristique de distribution d'énergie Ceased WO2019019565A1 (fr)

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CN111307277A (zh) * 2020-03-20 2020-06-19 北京工业大学 基于变分模态分解和预测性能的单模态子信号选择方法
CN111413588A (zh) * 2020-03-31 2020-07-14 陕西省地方电力（集团）有限公司咸阳供电分公司 一种配电网单相接地故障选线方法
CN111751134A (zh) * 2020-06-22 2020-10-09 西安科技大学 一种基于vmd与rls的采煤机振动信号降噪方法
CN111915865A (zh) * 2020-07-29 2020-11-10 东北大学 一种基于采动震源参数的煤矿复合地质灾害预警方法
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CN116009084A (zh) * 2022-12-15 2023-04-25 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 观测耦合模拟的金属矿深井开采诱发矿震机制分析方法
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CN111751134A (zh) * 2020-06-22 2020-10-09 西安科技大学 一种基于vmd与rls的采煤机振动信号降噪方法
CN111751134B (zh) * 2020-06-22 2021-12-14 西安科技大学 一种基于vmd与rls的采煤机振动信号降噪方法
CN111915865A (zh) * 2020-07-29 2020-11-10 东北大学 一种基于采动震源参数的煤矿复合地质灾害预警方法
CN113985481A (zh) * 2021-10-26 2022-01-28 长江大学 一种基于再约束的变分模态降噪方法及装置
CN113985481B (zh) * 2021-10-26 2023-07-18 长江大学 一种基于再约束的变分模态降噪方法及装置
CN116009084A (zh) * 2022-12-15 2023-04-25 深圳市中金岭南有色金属股份有限公司凡口铅锌矿 观测耦合模拟的金属矿深井开采诱发矿震机制分析方法
CN117092692A (zh) * 2023-07-20 2023-11-21 东北大学 适用于钻爆法隧道开挖过程的即时型岩爆时间预警方法
CN120908867A (zh) * 2025-10-10 2025-11-07 长沙飞翼智联科技有限公司 一种矿井下微震智能监测与可视化方法
CN121325274A (zh) * 2025-12-16 2026-01-13 西安奥华电子仪器股份有限公司 用于中子发生器的数据采集与时序同步处理方法及系统
CN121559607A (zh) * 2026-01-22 2026-02-24 中国海洋大学 一种随钻地震钻柱参考信号特征提取方法

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