CN114063144B - Method for identifying coal rock destabilization precursor characteristics by utilizing short-time zero-crossing rate - Google Patents
Method for identifying coal rock destabilization precursor characteristics by utilizing short-time zero-crossing rate Download PDFInfo
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/288—Event detection in seismic signals, e.g. microseismics
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- 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. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/20—Trace signal pre-filtering to select, remove or transform specific events or signal components, i.e. trace-in/trace-out
- G01V2210/23—Wavelet filtering
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- G01V2210/00—Details of seismic processing or analysis
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Abstract
The invention discloses a method for identifying coal rock instability precursor characteristics by utilizing short-time zero-crossing rate, which comprises the following steps: collecting full waveform signals in the destabilization and destruction process of a coal rock mass sample; windowing is carried out on the collected full waveform signals, and the full waveform signals are divided into short-time frame signals with equal lengths; performing wavelet threshold denoising on the segmented short-time frame signals; calculating the zero crossing rate of each short-time frame signal after denoising, and arranging the calculated zero crossing rate according to time sequence; determining a response rule of the zero-crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic identification criterion; and identifying the instability state of the coal rock mass to be identified according to the coal rock instability precursor characteristic identification criterion. The invention can provide a new analysis method and indexes for the acoustic emission monitoring technology in coal and rock dynamic disaster early warning, high and steep slope monitoring and structural health monitoring, and plays a positive role in improving the application effect of the acoustic emission monitoring technology.
Description
Technical Field
The invention relates to the technical field of coal rock fracture shock wave signal analysis and coal rock instability precursor characteristic analysis, in particular to a method for identifying coal rock instability precursor characteristics by utilizing a short-time zero crossing rate.
Background
The dynamic disaster of the coal and rock seriously threatens the life and property safety of related personnel in the pit, and the coal and rock instability is taken as one of precursors of the dynamic disaster, so that the effective coal and rock instability characteristics can be identified and avoided to ensure the safe production in the pit. At present, a micro-vibration or acoustic emission technology is commonly used for monitoring vibration wave signals released in the coal rock mass fracture generation process, the vibration wave signals can directly reflect the coal rock instability process, and good application effects are achieved in a plurality of mines. However, for vibration wave signals collected by acoustic emission, microseismic systems and the like, the analysis means and the analysis indexes are mostly single, and only the time domain indexes such as the number of events, ringing count, amplitude and the like are simply used, so that deep mining analysis on the original vibration waveform is lacking; in addition, research has found that the vibration waveforms of the coal and rock mass in different damage stages are obviously different, single microseismic/acoustic emission events can be distinguished through threshold values in an elastic stage, and waveforms of a plurality of events are overlapped due to the increase of the generation speed of the microseismic/acoustic emission events in a yield damage stage, so that the accurate evaluation of the stability of the coal and rock and the accurate early warning of dynamic disasters are difficult to realize by the existing parameters, and the mine safety production is influenced. Therefore, the signals collected by the monitoring systems such as microseism/acoustic emission are needed to be deeply excavated and analyzed, and parameters which can accurately represent the stability of the coal and rock mass are extracted from the original vibration wave signals, so that the purpose of improving the monitoring and early warning accuracy of the dynamic disasters of the coal and rock is achieved.
Disclosure of Invention
The invention provides a method for identifying the precursor characteristics of coal rock instability by utilizing short-time zero-crossing rate, which aims to solve the technical problems that in the prior art, an analysis means and an analysis index are single, accurate evaluation of coal rock stability and accurate early warning of dynamic disasters are difficult to realize in a yield damage stage, and mine safety production is influenced.
In order to solve the technical problems, the invention provides the following technical scheme:
a method for identifying precursor features of coal rock destabilization using short-term zero-crossing rate, comprising:
collecting full waveform signals in the destabilization and destruction process of a coal rock mass sample;
windowing is carried out on the collected full waveform signals, and the full waveform signals are divided into short-time frame signals with equal lengths;
performing wavelet threshold denoising on the segmented short-time frame signals;
calculating the zero crossing rate of each short-time frame signal after denoising, and arranging the calculated zero crossing rate according to time sequence;
determining a response rule of the zero-crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic identification criterion;
and identifying the instability state of the coal rock mass to be identified according to the coal rock instability precursor characteristic identification criterion.
Further, the collecting of the full waveform signal in the destabilization and destruction process of the coal rock mass sample comprises the following steps:
and collecting continuous vibration wave full waveform signals or a series of independent vibration wave signals generated in the destabilization and destruction process of the coal rock mass sample by utilizing a microseismic or acoustic emission sensor.
Further, when the collected full waveform signal is subjected to windowing processing and is divided into short-time frame signals with equal length, the length value standard of the adopted framing window is as follows: a maximum time window over which each independent frame signal can pass the stationarity check; the method for testing the stability comprises a graph testing method and a unit root testing method.
Further, the performing wavelet threshold denoising on the segmented short-time frame signal includes:
performing wavelet decomposition on each frame of signal by selecting a wavelet basis function to obtain wavelet coefficients of the signal in different layers;
for wavelet coefficients of each scale, comparing the wavelet coefficients with a threshold lambda, and when the kth coefficient d of the jth layer jk When the value is smaller than the threshold lambda, the coefficient d is considered jk Mainly caused by noise, d will be jk Setting to zero; when d jk When not less than the threshold lambda, consider the coefficient d jk Mainly caused by the signal, and the signal is transmitted to the receiver,at this time reserve d jk ；
The method for calculating the threshold lambda comprises the following steps:
wherein n is the total number of wavelet coefficients, and sigma is the standard deviation of the signal;
and reconstructing the processed wavelet coefficient by utilizing wavelet inverse transformation to obtain a denoised frame signal.
Further, the calculating the zero crossing rate of each denoised short-time frame signal, and arranging the calculated zero crossing rates according to time sequence includes:
calculating zero crossing rate Z of each short-time frame signal after denoising according to the following method n ：
Where x [ N ] is the short-time frame signal, N is the short-time frame signal length, sgn|·| is the sign function, namely:
and arranging the calculated zero crossing rate of each short-time frame signal according to the sequence of signals generated in the destabilization and destruction process of the coal rock mass sample.
Further, the coal rock stress state is the ratio of coal rock mass load to peak load; the value range of the stress state of the coal rock is 0-1, and the closer the value is to 1, the greater the destabilization damage degree of the coal rock body is.
Further, determining a response rule of the zero-crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic identification criterion, including:
dividing the coal rock mass state into four grades of stable, medium stable, weak stable and unstable destruction;
determining the stress states of the coal and rock corresponding to the states of the coal and rock bodies of different grades;
and determining the value range of zero crossing rates of the coal rock mass samples under different stress states to obtain corresponding quantization grading thresholds of the zero crossing rates under four grades of coal rock mass stability, medium stability, weak stability and instability damage.
Further, according to the coal rock destabilization precursor feature recognition criterion, recognizing a destabilization state of the coal rock mass to be recognized, including:
and according to the response rule of the zero crossing rate and the stress state of the coal rock, taking the magnitude of the zero crossing rate corresponding to the coal rock mass as the precursor information characteristic of the instability state of the coal rock, and finally judging the stability grade of the coal rock mass to be identified.
The technical scheme provided by the invention has the beneficial effects that at least:
according to the method for identifying the precursor characteristics of coal rock instability by utilizing the short-time zero-crossing rate, full waveform signals acquired in the coal rock instability and destruction process are used as raw data, and wavelet threshold denoising and zero-crossing rate calculation are utilized to excavate the acquired full waveform signals so as to obtain the precursor information characteristics of the coal rock instability. The method can quantitatively describe precursor information of the coal rock in different stages of destabilization and damage, and is beneficial to improving the application effect of the microseismic/acoustic emission monitoring technology in aspects of coal rock dynamic disaster early warning, high-steep slope monitoring, structural health monitoring and the like.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an execution flow of a method for identifying precursor features of coal rock instability by using a short-time zero-crossing rate according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a wavelet threshold denoising zero crossing rate calculation flow provided by an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an experimental apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a time series variation of stress and acoustic emission signals during rock sample destruction according to an embodiment of the present invention; wherein, (a) is a time sequence change curve diagram of a sample with the number of ZX8-1, (b) is a time sequence change curve diagram of a sample with the number of DX8-1, (c) is a time sequence change curve diagram of a sample with the number of DC8-1, and (d) is a time sequence change curve diagram of a sample with the number of DC 8-2;
fig. 5 is a time sequence variation curve of the zero crossing rate of the rock sample with the stress state according to the embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
The method can extract precursor characteristics accurately reflecting information of the coal rock destabilization process according to vibration wave signals, can provide a new analysis method and indexes for the microseismic/acoustic emission technology in coal rock dynamic disaster early warning, high-steep slope monitoring and structural health monitoring, and plays a positive role in improving the application effect of the acoustic emission monitoring technology.
Specifically, the execution flow of the method is shown in fig. 1, and the method comprises the following steps:
s1, collecting full waveform signals in a coal rock mass sample destabilization and destruction process;
in this embodiment, the implementation manner of S1 is as follows:
and collecting continuous vibration wave full waveform signals or a series of independent vibration wave signals generated in the destabilization and destruction process of the coal rock mass sample by utilizing a microseismic or acoustic emission sensor.
S2, windowing is carried out on the collected full waveform signals, and the full waveform signals are divided into short-time frame signals with equal lengths;
in this embodiment, when the collected full waveform signal is windowed and divided into short-time frame signals with equal lengths, the length of the framing window used is as follows: the maximum time window for each independent frame signal to pass through the stability test, namely each independent frame signal after framing belongs to a stable signal; among them, the general smoothness test methods include a graph test method and a unit root test method. Among them, the commonly used unit root test method includes ADF test, DFGLS test, PP test, etc.
S3, performing wavelet threshold denoising on the segmented short-time frame signals;
in addition, the wavelet threshold denoising refers to performing nonlinear processing such as cutting and amplitude reduction on wavelet coefficients in a wavelet transform domain to achieve the purpose of eliminating noise, and is mainly divided into 3 steps of wavelet decomposition, wavelet coefficient processing and wavelet reconstruction, specifically, in this embodiment, the step S3 includes:
a, wavelet decomposition: carrying out wavelet decomposition on each frame of signal by selecting a proper wavelet basis function to obtain wavelet coefficients of the signal in different layers;
b, wavelet coefficient processing: processing wavelet coefficients using hard threshold functions, i.e. for each scale wavelet coefficient, comparing it to a threshold lambda, when the kth coefficient d of the jth layer jk When the value is smaller than the threshold lambda, the coefficient d is considered jk Mainly caused by noise, d can be jk Setting to zero; when d jk When the value is not smaller than the threshold lambda, the coefficient is considered to be mainly caused by the signal, d is reserved jk . The hard threshold function is:
the method for calculating the threshold lambda comprises the following steps:
where n is the total number of wavelet coefficients and σ is the standard deviation of the signal.
c, wavelet reconstruction: and reconstructing the processed wavelet coefficient by utilizing wavelet inverse transformation to obtain a denoised frame signal.
S4, calculating zero crossing rate of each short-time frame signal after denoising, and arranging the calculated zero crossing rate according to time sequence;
in this embodiment, the implementation procedure of S4 is as follows:
calculating zero crossing rate Z of each short-time frame signal after denoising according to the following method n ：
Where x [ N ] is the short-time frame signal, N is the short-time frame signal length, sgn|·| is the sign function, namely:
the calculation flow of the wavelet threshold denoising zero-crossing rate at the time T is shown in figure 2.
And arranging the calculated zero crossing rate of each short-time frame signal according to the sequence of signals generated in the destabilization and destruction process of the coal rock mass sample.
S5, determining a response rule of the zero crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic judgment criterion;
the coal rock stress state refers to the ratio of the coal rock mass load to the peak load; the value range of the stress state of the coal rock is 0-1, and the closer the value is to 1, the greater the destabilization and damage degree of the coal rock mass is.
The implementation process of the S5 is as follows:
dividing the coal rock mass state into four grades of stable, medium stable, weak stable and unstable destruction;
determining the stress states of the coal and rock corresponding to the states of the coal and rock bodies of different grades;
and determining the value change range of the zero crossing rate of the coal rock mass sample under different stress states to obtain the corresponding quantitative classification threshold value of the zero crossing rate under four levels of coal rock mass stability, medium stability, weak stability and instability damage.
And S6, identifying the instability state of the coal rock mass to be identified according to the coal rock instability precursor characteristic identification criterion.
It should be noted that, in this embodiment, identifying the destabilization state of the coal rock mass to be identified according to the coal rock destabilization precursor feature identification criterion refers to: and according to the response rule of the zero crossing rate and the stress state of the coal rock, taking the magnitude of the zero crossing rate as the precursor information characteristic of the destabilization state of the coal rock, and finally judging the stability grade of the coal rock mass.
The method of this embodiment is further described below with reference to a specific application scenario:
in this embodiment, taking a rock sample collected from a certain mine as an example, a sample preparation device is used in a laboratory to process the rock sample into a standard cylindrical sample, a uniaxial compression test is performed after an acoustic emission sensor is arranged on the sample preparation device, stress and acoustic emission waveform signals in a destabilization process of the sample are synchronously collected, the collected signals are further processed to obtain a short-time zero-crossing rate so as to identify precursor information of the destabilization process of the sample, and the specific steps are as follows:
(1) 4 specimens (serial numbers of ZX8-1, DX8-1, DC8-1 and DC8-2 respectively) without macroscopic cracks are selected from all standard specimens to carry out a uniaxial compression experiment; the structure of the experimental device is shown in fig. 3, acoustic emission waveform data and stress data in the rock breaking process are collected, and the time sequence changes of the stress and acoustic emission waveforms are shown in fig. 4;
(2) Windowing the acquired acoustic emission signals, carrying out stability test on each frame of signals by using an adfuller interface in python-3.7, taking the maximum time window length passing the stability test, and framing the original signals to finally obtain the long and short frame signals with the length of 2 ms;
(3) Wavelet decomposition of signals is realized by using a wavedec interface in a pywt-0.23 third party library, and wavelet 9-layer decomposition is performed on the split-frame acoustic emission signals by using a db4 wavelet basis function to obtain wavelet coefficients of each layer;
(4) Calculating the noise standard deviation of the signal by using the waveform data collected in the loading initial stage, and combining the following formulas to obtain a small valueWave threshold λ:
where n is the total number of wavelet coefficients and σ is the standard deviation of the signal.
Calculating a wavelet threshold lambda of 0.0059, and setting the coefficient smaller than the threshold lambda in the wavelet coefficient to zero to obtain the processed wavelet coefficient;
(5) The wavelet reconstruction of the signal is realized by using a waverec interface in a pywt-0.23 third party library, and a denoised frame signal is obtained;
(6) Calculating wavelet threshold denoising zero-crossing rate by using a libsorb-0.8.0 third party library, wherein the calculation result is shown in figure 5 along with the time sequence change of the stress state of the rock sample;
(7) As can be seen from fig. 5, the stress state is 0.2 as a division point, and the zero crossing rate of the acoustic emission signal after denoising the rock sample is divided into two parts according to the change of the stress state: when the stress state is less than 0.2, the rock sample is in a compaction stage, and the zero crossing rate of the acoustic emission signal is reduced along with the increase of the stress state; when the stress state is more than 0.2 and less than 0.8, the rock sample is in an elastic and yielding stage, cracks are generated in the rock sample and gradually expand, and the zero crossing rate of the acoustic emission signal has a monotonically increasing trend along with the increase of the stress state; when the stress state is greater than 0.8, large cracks are generated by the internal fracture connection of the rock sample until the sample is destroyed, and the zero crossing rate of the acoustic emission signal is continuously increased until the value tends to be a certain value;
(8) And establishing an evaluation criterion for identifying the unstable state of the coal rock by the short-time zero-crossing rate, wherein the values of the different zero-crossing rates correspond to the stability state of the coal rock respectively, so that the aim of identifying the precursor information characteristics of the unstable state of the coal rock is finally achieved, and the specific evaluation criterion for identifying the unstable state of the coal rock by the short-time zero-crossing rate is shown in a table 1.
Table 1 evaluation criteria for identifying coal and rock instability state by short-time zero crossing rate
In summary, according to the method for identifying the precursor characteristics of coal rock instability by using the short-time zero-crossing rate in the embodiment, full waveform signals acquired in the coal rock instability destruction process are used as original data, and wavelet threshold denoising and zero-crossing rate calculation are used for excavating the full waveform signals to obtain the precursor information characteristics of coal rock instability. The method can quantitatively describe precursor information of the coal rock in different stages of destabilization and damage, and is beneficial to improving the application effect of the microseismic/acoustic emission monitoring technology in aspects of coal rock dynamic disaster early warning, high-steep slope monitoring, structural health monitoring and the like.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (2)
1. A method for identifying precursor features of coal rock destabilization using short-term zero-crossing rate, comprising:
collecting full waveform signals in the destabilization and destruction process of a coal rock mass sample;
windowing is carried out on the collected full waveform signals, and the full waveform signals are divided into short-time frame signals with equal lengths;
performing wavelet threshold denoising on the segmented short-time frame signals;
calculating the zero crossing rate of each short-time frame signal after denoising, and arranging the calculated zero crossing rate according to time sequence;
determining a response rule of the zero-crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic identification criterion;
according to the coal rock instability precursor characteristic identification criterion, identifying the instability state of the coal rock mass to be identified;
the full waveform signal in the destabilization and destruction process of the acquired coal rock mass sample comprises the following components:
collecting continuous vibration wave full waveform signals or a series of independent vibration wave signals generated in the destabilization and destruction process of a coal rock mass sample by utilizing a microseismic or acoustic emission sensor;
when windowing is carried out on the collected full waveform signals and the full waveform signals are divided into short-time frame signals with equal length, the length value standard of the adopted framing window is as follows: a maximum time window over which each independent frame signal can pass the stationarity check; the method for testing the stability comprises a graph testing method and a unit root testing method;
the calculating the zero crossing rate of each short-time frame signal after denoising, and arranging the calculated zero crossing rate according to the time sequence comprises the following steps:
calculating zero crossing rate Z of each short-time frame signal after denoising according to the following method n ：
Where x [ N ] is the short-time frame signal, N is the short-time frame signal length, sgn|·| is the sign function, namely:
arranging the zero crossing rate of each short-time frame signal according to the sequence of signals generated in the destabilization and destruction process of the coal rock mass sample;
the stress state of the coal rock is the ratio of the load of the coal rock mass to the peak load; the value range of the stress state of the coal rock is 0-1, and the value is closer to 1, so that the destabilization and damage degree of the coal rock body is larger;
determining a response rule of the zero-crossing rate and the stress state of the coal rock to obtain a coal rock instability precursor characteristic identification criterion, wherein the method comprises the following steps:
dividing the coal rock mass state into four grades of stable, medium stable, weak stable and unstable destruction;
determining the stress states of the coal and rock corresponding to the states of the coal and rock bodies of different grades; wherein the stress state is stable when 0 to 0.2, 0.2 to 0.6 is medium stable, 0.6 to 0.8 is weak stable, and 0.8 to 1 is unstable damage;
determining the value range of zero crossing rate of the coal rock mass sample under different stress states to obtain corresponding quantization grading thresholds of the zero crossing rate under four grades of coal rock mass stability, medium stability, weak stability and instability damage;
when the short-time zero-crossing rate is less than 0.05, the stress state is 0-0.2, and the stability of the coal rock is stable; when the short-time zero-crossing rate is 0.05-0.15, the stress state is 0.2-0.6, and the stability of the coal rock is medium and stable; when the short-time zero-crossing rate is 0.15-0.2, the stress state is 0.6-0.8, and the stability of the coal rock is weak and stable; when the short-time zero-crossing rate is more than 0.2, the stress state is 0.8-1, and the stability of the coal rock is unstable and destroyed;
according to the coal rock instability precursor characteristic identification criterion, identifying the instability state of the coal rock mass to be identified, including:
and according to the response rule of the zero crossing rate and the stress state of the coal rock, taking the magnitude of the zero crossing rate corresponding to the coal rock mass as the precursor information characteristic of the instability state of the coal rock, and finally judging the stability grade of the coal rock mass to be identified.
2. The method for identifying precursor features of coal rock destabilization using short time zero crossing rate according to claim 1, characterized in that the wavelet threshold denoising of the segmented short time frame signal comprises:
performing wavelet decomposition on each frame of signal by selecting a wavelet basis function to obtain wavelet coefficients of the signal in different layers;
for wavelet coefficients of each scale, comparing the wavelet coefficients with a threshold lambda, and when the kth coefficient d of the jth layer jk When the value is smaller than the threshold lambda, the coefficient d is considered jk Mainly caused by noise, d will be jk Setting to zero; when d jk When not less than the threshold lambda, consider the coefficient d jk Mainly caused by signals, where d is reserved jk ；
The method for calculating the threshold lambda comprises the following steps:
wherein n is the total number of wavelet coefficients, and sigma is the standard deviation of the signal;
and reconstructing the processed wavelet coefficient by utilizing wavelet inverse transformation to obtain a denoised frame signal.
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