Seismic source positioning method based on Bayesian time difference measurement uncertainty estimation

Abstract

The invention provides a seismic source positioning method based on Bayesian time difference measurement uncertainty estimation, and the method comprises the steps: laying a micro-seismic monitoring system in a typical mine, carrying out a plurality of times of blasting vibration experiments with known seismic source positions, collecting and screening blasting waveform signals, and carrying out the seismic source positioning. An improved empirical mode decomposition technology and a wavelet packet noise reduction technology are adopted to process the low-signal-to-noise-ratio waveform signals obtained through screening; time difference measurement data of different seismic sources are automatically picked up based on the processed waveform signals; constructing a Bayesian estimation framework to estimate the uncertainty of the time difference measurement data, and carrying out calculation by adopting a Monte Carlo sampling technology; according to the time difference measurement uncertainty obtained through estimation, a deviation correction item is added to time difference data for unbiased correction, then positioning is carried out through a positioning method, and a high-precision micro-seismic source positioning result is obtained. According to the method, the accuracy of time difference measurement data pickup is improved, a good foundation is laid for time difference measurement uncertainty estimation, and high-precision positioning of the mine micro-seismic source is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional microseismic positioning, and in particular to a method for earthquake source positioning based on Bayesian time difference measurement uncertainty estimation. Background Art

[0002] Microseismic location technology can dynamically invert the time and spatial position of rock mass rupture and the "time and space intensity" information such as the energy released by the rupture source. Interpreting the evolution law of microseismic time and space intensity can provide an important basis for rock mass stability evaluation and accurate early warning of potential rock bursts. The time difference measurement data recorded by the network is an important input data for earthquake source location. The accuracy of its prior distribution directly affects the accuracy of earthquake source location. The accuracy of microseismic source location is seriously affected by the uncertainty of time difference measurement.

[0003] At present, most microseismic positioning methods are essentially based on the expansion and improvement of the least squares principle. The default time difference measurement data obeys the zero-mean Gaussian independent and identically distributed assumption, and no additional assumptions and estimates are made on the time difference measurement data. In the mining engineering environment, abnormal signal interference, channel crosstalk, weak waveform first arrival, non-line-of-sight propagation, multipath propagation, travel time path delay or sensor sensitivity reduction will lead to large differences in measurement uncertainty, and the bias, heteroscedasticity and correlation of measurement data are common. In addition, due to the square amplification effect of the least squares principle, the above-mentioned prior distribution error will be more prominent, resulting in a larger positioning error. Therefore, the existing traditional microseismic positioning methods cannot accurately obtain the location of the microseismic source under the conditions of complex mining environments.

[0004] However, for mine dynamic disasters, high-precision positioning of mine microseismic sources is the key to achieving accurate early warning of mine rock bursts and ensuring the safe and efficient mining of strategic mineral resources. The inability to accurately locate the microseismic source makes the prevention and control of mine dynamic disasters more difficult. Summary of the invention

[0005] In view of the technical problem that the existing microseismic positioning methods cannot accurately obtain the microseismic source position under complex mining environment conditions, making the prevention and control of mine dynamic disasters more difficult, the present invention provides a source positioning method based on Bayesian time difference measurement uncertainty estimation.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for earthquake source location based on Bayesian time difference measurement uncertainty estimation comprises the following steps:

[0008] S1. A microseismic monitoring system is deployed in a typical mine. The microseismic monitoring system includes a sensor network and a blasting source with a known source location in the monitoring system. Multiple blasting vibration experiments with known source locations are carried out to collect and screen blasting waveform signals. The screened waveform signals with low signal-to-noise ratio are processed using improved empirical mode decomposition technology and wavelet packet noise reduction technology.

[0009] S2, automatically picking up the time difference measurement data of different earthquake sources based on the processed waveform signal;

[0010] S3, construct a Bayesian estimation framework to estimate the uncertainty of the time difference measurement data, and use Monte Carlo sampling technology to calculate;

[0011] S4. According to the estimated uncertainty of the time difference measurement, the bias correction term is added to the time difference data for unbiased correction, and then the positioning method is used to obtain high-precision microseismic source positioning results.

[0012] Furthermore, the steps of the empirical mode decomposition technology in step S1 specifically include:

[0013] S11, adding a plurality of adaptive Gaussian white noise time series of different amplitudes to the original time series of the screened blasting waveform signal;

[0014] S12, averaging the upper and lower envelopes of each noisy time series, and obtaining an interpolation of the noisy signal and the corresponding average time as a new time series;

[0015] S13, continue to add Gaussian white noise to the new time series, and repeat steps S11 and S12 until the new time series meets the termination condition.

[0016] Further, the termination condition in step S13 is:

[0017] The number of extreme values ​​and zero crossings of the new time series is less than or equal to 1; and,

[0018] The average value of the upper and lower envelopes of the new time series at any time is 0.

[0019] Furthermore, the wavelet packet denoising technique in step S1 adopts a fixed threshold technique, specifically:

[0020]

[0021] Among them, T represents the threshold, σ represents the standard deviation of the noise, and N represents the length of the signal in the time domain.

[0022] Furthermore, the fixed threshold technology adopts a soft threshold function, which is specifically expressed as:

[0023]

[0024] Where S(t) represents the soft threshold function, x represents the decomposition factor, and |·| represents the absolute value.

[0025] Furthermore, the steps of estimating the uncertainty of the time difference measurement data by the Bayesian estimation framework in step S3 are specifically as follows:

[0026] Assuming that the microseismic monitoring system has been in a stable state during the research period, that is, the uncertainty of the time difference measurement of the microseismic monitoring system remains approximately unchanged, the number of sensor pairs involved in the inversion of the time difference measurement uncertainty is set to k, the number of microseismic and blasting events is m, the number of time difference measurement data is n, and 2k≤n is satisfied, then the Bayesian estimation framework is expressed as:

[0027]

[0028] Among them, P(X) is the marginal likelihood, P(θ) is the initial prior probability of the parameter to be estimated, and the default assumption of the traditional positioning method, that is, zero-mean Gaussian independent and identically distributed, is used as the prior distribution of the parameter θ; the prior probability P(θ) will be further updated by the sample information X measured by the time difference, P(θ|X) is the updated posterior probability, and P(X|θ) is the likelihood function conditioned on the model parameter θ, expressed as:

[0029]

[0030] Among them, C e Denotes T(θ) cal -T mea The covariance matrix of cal is the calculated value of the time difference data, T mea is the measured value of the time difference data, and d represents the dimension of the data.

[0031] Furthermore, the Monte Carlo sampling technique in step S3 specifically includes:

[0032] Taking 0us as the initial value of the iteration, the uncertainty of the time difference measurement of each sensor is estimated by iterating 100,000 times. In the iteration, one parameter is updated each time, and the update speed follows the Gaussian distribution sampling with a mean of 0 and a standard deviation of 0.25. After the parameter is updated, the probability density function P(X|θ') of the new parameter model θ' is calculated, and the acceptance rate α(m'|m) of the new parameter model is expressed as:

[0033]

[0034] When α(m'|m) is greater than or equal to u, the new model m' is accepted; otherwise, the new model will be rejected; each time u∈[0,1] represents a random number in the range of 0-1, which obeys a uniform distribution.

[0035] Compared with the prior art, the method for earthquake source location based on Bayesian time difference measurement uncertainty estimation provided by the present invention includes two major parts: microseismic waveform signal collection and processing and Bayesian estimation and positioning of microseismic time difference measurement uncertainty, wherein the microseismic waveform signal collection and processing includes using a signal noise reduction and purification method to perform noise reduction processing on the blasting vibration signal, and continuing to use the Akaike information method to automatically pick up the time difference measurement data of the earthquake source; then, according to the time difference measurement data, sensor coordinates, earthquake source position and wave velocity structure information, a Bayesian estimation framework for time difference measurement uncertainty is constructed to obtain an accurate time difference measurement prior distribution, and the time difference data is unbiasedly corrected by a deviation term to achieve fine positioning of the microseismic source in the mine. Therefore, the present invention improves the accuracy of time difference measurement data picking, lays a good foundation for time difference measurement uncertainty estimation, solves the problem of solving the probability distribution of non-repeatable microseismic measurement data, accurately obtains the prior distribution of time difference measurement, and achieves high-precision positioning of the microseismic source. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of a method for earthquake source location based on Bayesian time difference measurement uncertainty estimation provided by the present invention.

[0037] Figure 2 The present invention provides a granite sample with a known earthquake source. DETAILED DESCRIPTION

[0038] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further described below with reference to specific diagrams.

[0039] Please refer to Figure 1 As shown, the present invention provides a method for earthquake source location based on Bayesian time difference measurement uncertainty estimation, comprising the following steps:

[0040] S1. A microseismic monitoring system is deployed in a typical mine. The microseismic monitoring system includes a sensor network and a blasting source with a known source location in the monitoring system. Multiple blasting vibration experiments with known source locations are carried out to collect and screen blasting waveform signals. The screened waveform signals with low signal-to-noise ratio are processed using improved empirical mode decomposition technology and wavelet packet noise reduction technology.

[0041] S2, automatically picking up the time difference measurement data of different earthquake sources based on the processed waveform signal, specifically, the existing Akaike information method can be used to automatically pick up the time difference measurement data of the earthquake sources;

[0042] S3. Construct a Bayesian estimation framework to estimate the uncertainty of the time difference measurement data, and use the Monte Carlo sampling technique to calculate it. The algorithm writing and implementation of the uncertainty estimation of the time difference measurement data and the Monte Carlo sampling technique are both carried out using Matlab software.

[0043] S4. According to the estimated uncertainty of the time difference measurement, the bias correction term is added to the time difference data for unbiased correction, and then the positioning is performed using the traditional linear positioning method to obtain high-precision microseismic source positioning results.

[0044] As a specific embodiment, the steps of the empirical mode decomposition technology in step S1 specifically include:

[0045] S11, adding a plurality of adaptive Gaussian white noise time series of different amplitudes to the original time series of the screened blasting waveform signal;

[0046] S12, averaging the upper and lower envelopes of each noisy time series, and obtaining an interpolation of the noisy signal and the corresponding average time as a new time series;

[0047] S13, continue to add Gaussian white noise to the new time series, and repeat steps S11 and S12 until the new time series meets the termination condition.

[0048] As a specific embodiment, the termination condition in step S13 is:

[0049] The number of extreme values ​​and zero crossings of the new time series is less than or equal to 1; and,

[0050] The average value of the upper and lower envelopes of the new time series at any time is 0.

[0051] As a specific embodiment, the wavelet packet denoising technology in step S1 adopts a fixed threshold technology, specifically:

[0052]

[0053] Among them, T represents the threshold, σ represents the standard deviation of the noise, and N represents the length of the signal in the time domain.

[0054] As a specific embodiment, the fixed threshold technology adopts a soft threshold function, which is specifically expressed as:

[0055]

[0056] Where S(t) represents the soft threshold function, x represents the decomposition factor, and |·| represents the absolute value.

[0057] As a specific embodiment, the step of estimating the uncertainty of the time difference measurement data by the Bayesian estimation framework in step S3 is specifically as follows:

[0058] Assuming that the microseismic monitoring system has been in a stable state during the research period, that is, the uncertainty of the time difference measurement of the microseismic monitoring system remains approximately unchanged, the number of sensor pairs involved in the inversion of the time difference measurement uncertainty is set to k, the number of microseismic and blasting events is m, the number of time difference measurement data is n, and 2k≤n is satisfied, then the Bayesian estimation framework is expressed as:

[0059]

[0060] Among them, P(X) is the marginal likelihood, P(θ) is the initial prior probability of the parameter to be estimated, and the default assumption of the traditional positioning method, that is, zero-mean Gaussian independent and identically distributed, is used as the prior distribution of the parameter θ; the prior probability P(θ) will be further updated by the sample information X measured by the time difference, P(θ|X) is the updated posterior probability, and P(X|θ) is the likelihood function conditioned on the model parameter θ, expressed as:

[0061]

[0062] Among them, C e Denotes T(θ) cal -T mea The covariance matrix of cal is the calculated value of the time difference data, T mea is the measured value of the time difference data, and d represents the dimension of the data.

[0063] As a specific embodiment, the Monte Carlo sampling technique in step S3 specifically includes:

[0064] Taking 0us as the initial value of the iteration, the uncertainty of the time difference measurement of each sensor is estimated by iterating 100,000 times. In the iteration, one parameter is updated each time, and the update speed follows the Gaussian distribution sampling with a mean of 0 and a standard deviation of 0.25. After the parameter is updated, the probability density function P(X|θ') of the new parameter model θ' is calculated, and the acceptance rate α(m'|m) of the new parameter model is expressed as:

[0065]

[0066] When α(m'|m) is greater than or equal to u, the new model m' is accepted; otherwise, the new model will be rejected; each time u∈[0,1] represents a random number in the range of 0-1, which obeys a uniform distribution.

[0067] As a specific example, please refer to Figure 2As shown, the implementation method is realized through an indoor lead breaking experiment. The acquisition system of the microseismic waveform of the known source on the granite sample is used to receive the acoustic emission signal. The acquisition system includes a sensor, a preamplifier, a signal collector and a signal analyzer. The microseismic time is simulated by breaking the lead. The fracture angle between the pencil lead and the surface of the granite sample (where the red cross is the installation position of the sensor and the blue dot is the acoustic emission source) is 30°. The acoustic emission signal is collected by a piezoelectric sensor and amplified by a 40dB preamplifier. Afterwards, the acoustic emission signal is collected using a signal analyzer, and the collected acoustic emission signal is saved in a laptop for further analysis.

[0068] The low signal-to-noise ratio waveform signal obtained by screening is processed by improved empirical mode decomposition technology and wavelet packet denoising technology. The time difference measurement data is picked up according to the processed waveform signal, and the time difference measurement uncertainty is estimated using the time difference measurement data. The estimated time difference measurement uncertainty deviation correction term is added to the time difference data for unbiased correction, and then the traditional linear positioning method is used for positioning. Taking (25, 0, 50) (unit: mm) as the acoustic emission source as an example, the accuracy before the time difference measurement uncertainty estimation is 4.03mm, and the positioning accuracy after the time difference data is corrected is 3.48mm, which proves the feasibility and effectiveness of the Bayesian estimation framework.

[0069] The above embodiments can prove the applicability of the present invention in microseismic positioning, prove the feasibility of the Bayesian estimation framework, and can further improve the positioning accuracy.

[0070] Compared with the prior art, the method for earthquake source location based on Bayesian time difference measurement uncertainty estimation provided by the present invention includes two major parts: microseismic waveform signal collection and processing and Bayesian estimation and positioning of microseismic time difference measurement uncertainty, wherein the microseismic waveform signal collection and processing includes using a signal noise reduction and purification method to perform noise reduction processing on the blasting vibration signal, and continuing to use the Akaike information method to automatically pick up the time difference measurement data of the earthquake source; then, according to the time difference measurement data, sensor coordinates, earthquake source position and wave velocity structure information, a Bayesian estimation framework for time difference measurement uncertainty is constructed to obtain an accurate time difference measurement prior distribution, and the time difference data is unbiasedly corrected by a deviation term to achieve fine positioning of the microseismic source in the mine. Therefore, the present invention improves the accuracy of time difference measurement data picking, lays a good foundation for time difference measurement uncertainty estimation, solves the problem of solving the probability distribution of non-repeatable microseismic measurement data, accurately obtains the prior distribution of time difference measurement, and achieves high-precision positioning of the microseismic source.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for earthquake source location based on Bayesian time difference measurement uncertainty estimation, characterized in that: The following steps are involved: S1. A microseismic monitoring system is deployed in a typical mine. The microseismic monitoring system includes a sensor network and a blasting source with a known source location in the monitoring system. Multiple blasting vibration experiments with known source locations are carried out to collect and screen blasting waveform signals. The screened waveform signals with low signal-to-noise ratio are processed using improved empirical mode decomposition technology and wavelet packet noise reduction technology. S2, automatically picking up the time difference measurement data of different earthquake sources based on the processed waveform signal; S3, construct a Bayesian estimation framework to estimate the uncertainty of the time difference measurement data, and use Monte Carlo sampling technology to calculate; S4. According to the estimated uncertainty of the time difference measurement, the bias correction term is added to the time difference data for unbiased correction, and then the positioning method is used to obtain high-precision microseismic source positioning results.

2. The method for earthquake source location based on Bayesian time difference measurement uncertainty estimation according to claim 1, characterized in that: The steps of the empirical mode decomposition technology in step S1 specifically include: S11, adding a plurality of adaptive Gaussian white noise time series of different amplitudes to the original time series of the screened blasting waveform signal; S12, averaging the upper and lower envelopes of each noisy time series, and obtaining an interpolation of the noisy signal and the corresponding average time as a new time series; S13, continue to add Gaussian white noise to the new time series, and repeat steps S11 and S12 until the new time series meets the termination condition.

3. The earthquake source location method based on Bayesian time difference measurement uncertainty estimation according to claim 2, characterized in that: The termination condition in step S13 is: The number of extreme values ​​and zero crossings of the new time series is less than or equal to 1; and, The average value of the upper and lower envelopes of the new time series at any time is 0.

4. The earthquake source location method based on Bayesian time difference measurement uncertainty estimation according to claim 1, characterized in that: The wavelet packet denoising technique in step S1 adopts a fixed threshold technique, specifically: Among them, T represents the threshold, σ represents the standard deviation of the noise, and N represents the length of the signal in the time domain.

5. The earthquake source location method based on Bayesian time difference measurement uncertainty estimation according to claim 4, characterized in that: The fixed threshold technology adopts a soft threshold function, which is specifically expressed as: Where S(t) represents the soft threshold function, x represents the decomposition factor, and |·| represents the absolute value.

6. The earthquake source location method based on Bayesian time difference measurement uncertainty estimation according to claim 1, characterized in that: The steps of estimating the uncertainty of the time difference measurement data by the Bayesian estimation framework in step S3 are specifically as follows: Assuming that the microseismic monitoring system has been in a stable state during the research period, that is, the uncertainty of the time difference measurement of the microseismic monitoring system remains approximately unchanged, the number of sensor pairs involved in the inversion of the time difference measurement uncertainty is set to k, the number of microseismic and blasting events is m, the number of time difference measurement data is n, and 2k≤n is satisfied, then the Bayesian estimation framework is expressed as: Among them, P(X) is the marginal likelihood, P(θ) is the initial prior probability of the parameter to be estimated, and the default assumption of the traditional positioning method, that is, zero-mean Gaussian independent and identically distributed, is used as the prior distribution of the parameter θ; the prior probability P(θ) will be further updated by the sample information X measured by the time difference, P(θ|X) is the updated posterior probability, and P(X|θ) is the likelihood function conditioned on the model parameter θ, expressed as: Among them, C e Denotes T(θ) cal -T mea The covariance matrix of cal is the calculated value of the time difference data, T mea is the measured value of the time difference data, and d represents the dimension of the data.

7. The method for earthquake source location based on Bayesian time difference measurement uncertainty estimation according to claim 6, characterized in that: The Monte Carlo sampling technique in step S3 specifically includes: Taking 0us as the initial value of the iteration, the uncertainty of the time difference measurement of each sensor is estimated by iterating 100,000 times. In the iteration, one parameter is updated each time, and the update speed follows the Gaussian distribution sampling with a mean of 0 and a standard deviation of 0.

25. After the parameter is updated, the probability density function P(X|θ′) of the new parameter model θ′ is calculated, and the acceptance rate α(m′|m) of the new parameter model is expressed as: When α(m′|m) is greater than or equal to u, the new model m′ is accepted; otherwise, the new model will be rejected; each time u∈[0,1] represents a random number in the range of 0-1, which obeys a uniform distribution.

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