Coal mine microseismic signal processing method and device

Abstract

The present application proposes a method and device for processing microseismic signals in coal mines, wherein the method comprises: monitoring the mining-affected area of ​​the coal mine underground through a vibration sensor, determining the vibration waveform signal corresponding to the monitored real-time vibration signal, and determining the background noise characteristic information of the real-time vibration waveform signal, and processing the real-time vibration waveform signal based on the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, and then obtaining a clock signal corresponding to the effective microseismic waveform signal, and compressing the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Thus, in the process of processing the microseismic signals in coal mines, the effective microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby improving the accuracy of identifying the effective microseismic waveform signal and realizing high-precision monitoring of microseisms in coal mines.

Description

Technical Field

[0001] The present application relates to the field of coal mine production safety monitoring, and in particular to a method and device for processing coal mine microseismic signals. Background Art

[0002] Coal is the foundation for supporting the sustained and rapid development of the national economy. For a considerable period of time, coal resources will remain the main energy source in the domestic energy consumption structure. In the past decade, whether it is the number of accidents or the number of deaths, coal mine dynamic disasters such as coal mine roof, rock burst, coal and gas outburst, and mine water inrush accounted for more than 50%. Among the related technologies, microseismic monitoring technology, as a three-dimensional spatial monitoring technology for coal rock microfractures, has been widely used in the monitoring of dynamic disasters such as coal mine rock burst, roof, coal and gas outburst, and mine water inrush. However, the related microseismic monitoring technology has low efficiency and low accuracy in identifying effective microseismic signals. Summary of the invention

[0003] The present application proposes a method and device for processing microseismic signals in coal mines.

[0004] On the one hand, an embodiment of the present application proposes a method for processing microseismic signals in a coal mine, the method comprising: obtaining a real-time vibration signal obtained by a vibration sensor monitoring an area affected by mining underground in a coal mine; obtaining a vibration waveform signal corresponding to the real-time vibration signal; determining background noise characteristic information of the real-time vibration waveform signal; processing the real-time vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal; obtaining a clock signal corresponding to the effective microseismic waveform signal; compressing the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer.

[0005] In one embodiment of the present application, determining the background noise characteristic information of the real-time vibration waveform signal includes: determining an invalid waveform signal in the real-time vibration waveform signal; and determining the background noise characteristic information of the real-time vibration waveform signal based on time domain and frequency domain characteristics of the invalid waveform signal.

[0006] In one embodiment of the present application, the real-time vibration waveform signal is processed according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, including: performing cluster analysis on the vibration waveform signal according to the background noise characteristic information of the real-time vibration waveform signal to determine the microseismic waveform signal of the real-time vibration waveform signal; determining the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal based on the long time window length, the long time window threshold and the corresponding first arrival time in the long and short time window average method, and the short time window length, the short time window threshold and the corresponding second arrival time; and performing fine segmentation of the microseismic waveform signal according to the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal to obtain the effective microseismic waveform signal.

[0007] In one embodiment of the present application, after the microseismic waveform signal is finely segmented according to the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal to obtain the effective microseismic waveform signal, it also includes: using the autoregressive Akaike information criterion method, using the effective microseismic waveform signal, optimizing the long time window length and the short time window length in the long and short time window mean method to obtain the optimized long time window length and short time window length; determining the target longitudinal wave signal and the target transverse wave signal of the microseismic waveform signal according to the optimized long time window length and the short time window length; and obtaining the effective microseismic waveform signal after the real-time vibration waveform signal is updated according to the target longitudinal wave signal and the target transverse wave signal.

[0008] In one embodiment of the present application, obtaining a real-time vibration waveform signal corresponding to the real-time vibration signal includes: inputting the real-time vibration signal into an analog filter to obtain an analog continuous waveform signal; inputting the analog continuous waveform signal into an analog-to-digital conversion circuit to obtain a digital discrete waveform signal; inputting the digital discrete waveform signal into a digital filter to obtain the real-time vibration waveform signal.

[0009] In one embodiment of the present application, after compressing the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer, it also includes: obtaining a compressed signal after compressing the effective microseismic waveform signal and the clock signal; comparing the compressed signal with a real-time vibration signal obtained by the vibration sensor for monitoring the mining-affected area underground in a coal mine to determine a comparison result; and adjusting the vibration sensor according to the comparison result.

[0010] The present application proposes a method for processing microseismic signals in coal mines, which monitors the mining-affected area of ​​the coal mine underground through a vibration sensor, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal, and processes the real-time vibration waveform signal based on the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, and then obtains a clock signal corresponding to the effective microseismic waveform signal, and compresses the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Therefore, in the process of processing the microseismic signals in coal mines, the effective microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby improving the accuracy of identifying the effective microseismic waveform signal and realizing high-precision monitoring of microseisms in coal mines.

[0011] On the other hand, an embodiment of the present application proposes a device for processing microseismic signals in coal mines, the device comprising: a first acquisition module, used to acquire a real-time vibration signal obtained by a vibration sensor monitoring an area affected by mining underground in a coal mine; a data acquisition module, used to acquire a vibration waveform signal corresponding to the real-time vibration signal; a data preprocessing module, used to determine background noise characteristic information of the real-time vibration waveform signal; an effective waveform recognition module, used to process the real-time vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal; a network timing module, used to acquire a clock signal corresponding to the effective microseismic waveform signal; a microseismic event storage and upload module, used to compress the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer.

[0012] In one embodiment of the present application, the data preprocessing module is specifically used to: determine the invalid waveform signal in the real-time vibration waveform signal; and determine the background noise characteristic information of the real-time vibration waveform signal based on the time domain and frequency domain characteristics of the invalid waveform signal.

[0013] In one embodiment of the present application, the effective waveform identification module is specifically used to: perform cluster analysis on the vibration waveform signal according to the background noise characteristic information of the real-time vibration waveform signal to determine the microseismic waveform signal of the real-time vibration waveform signal; determine the longitudinal wave signal and transverse wave signal of the microseismic waveform signal based on the long time window length, long time window threshold and the corresponding first arrival time in the long and short time window average method, and the short time window length, short time window threshold and the corresponding second arrival time; and perform fine segmentation of the microseismic waveform signal according to the longitudinal wave signal and transverse wave signal of the microseismic waveform signal to obtain the effective microseismic waveform signal.

[0014] In one embodiment of the present application, the device also includes: an optimization module, which adopts the autoregressive Akaike information criterion method and uses the effective microseismic waveform signal to optimize the long-time window length and the short-time window length in the long-short-time window average method to obtain the optimized long-time window length and the short-time window length; a first determination module, which is used to determine the target longitudinal wave signal and the target shear wave signal of the microseismic waveform signal according to the optimized long-time window length and the short-time window length; an update module, which is used to obtain the effective microseismic waveform signal after the real-time vibration waveform signal is updated according to the target longitudinal wave signal and the target shear wave signal.

[0015] In one embodiment of the present application, the data acquisition module is specifically used to: input the real-time vibration signal into an analog filter to obtain an analog continuous waveform signal; input the analog continuous waveform signal into an analog-to-digital conversion circuit to obtain a digital discrete waveform signal; input the digital discrete waveform signal into a digital filter to obtain the real-time vibration waveform signal.

[0016] In one embodiment of the present application, a second acquisition module is used to obtain a compressed signal after compressing the effective microseismic waveform signal and the clock signal; a second determination module is used to compare the compressed signal with the real-time vibration signal obtained by the vibration sensor for monitoring the mining-affected area underground in a coal mine, and determine a comparison result; an adjustment module is used to adjust the vibration sensor according to the comparison result.

[0017] The present application proposes a processing device for coal mine microseismic signals, which monitors the mining-affected area of ​​the coal mine underground through a vibration sensor, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal, and processes the real-time vibration waveform signal based on the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, and then obtains a clock signal corresponding to the effective microseismic waveform signal, and compresses the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Therefore, in the process of processing the coal mine microseismic signals, the effective microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby improving the accuracy of identifying the effective microseismic waveform signal and realizing high-precision monitoring of coal mine microseisms.

[0018] On the other hand, an embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the method for processing coal mine microseismic signals disclosed in the embodiment of the present application.

[0019] On the other hand, an embodiment of the present application proposes a computer program product, which, when an instruction processor in the computer program product executes, implements the method for processing coal mine microseismic signals in the embodiment of the present application.

[0020] Other effects of the above optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present application.

[0022] Figure 1 It is a flow chart of a method for processing microseismic signals in coal mines according to an embodiment of the present application.

[0023] Figure 2 It is a flow chart of processing coal mine microseismic signals according to another embodiment of the present application.

[0024] Figure 3 It is a flow chart of a method for processing coal mine microseismic signals according to another embodiment of the present application.

[0025] Figure 4 It is a flow chart of a method for processing coal mine microseismic signals according to another embodiment of the present application.

[0026] Figure 5 It is a structural schematic diagram of a device for processing microseismic signals in coal mines according to an embodiment of the present application.

[0027] Figure 6 It is a structural schematic diagram of a device for processing microseismic signals in coal mines according to another embodiment of the present application. DETAILED DESCRIPTION

[0028] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the method and device for processing coal mine microseismic signals according to the embodiments of the present application with reference to the accompanying drawings.

[0030] Figure 1It is a flow chart of a method for processing microseismic signals of coal mines according to an embodiment of the present application. It should be noted that the execution subject of the method for processing microseismic signals of coal mines provided in this embodiment is a processing device for processing microseismic signals of coal mines, which can be implemented by software and / or hardware, and which can be configured in an electronic device. The electronic device in this embodiment can include but is not limited to terminal devices and servers, and this embodiment does not specifically limit the electronic device.

[0031] like Figure 1 As shown, the method for processing the coal mine microseismic signal may include:

[0032] Step 101, obtaining a real-time vibration signal obtained by a vibration sensor monitoring an area affected by mining in a coal mine.

[0033] In some embodiments, in order to monitor and predict microseismic events, vibration sensors can be installed in the area affected by underground coal mining. The vibration sensors can be used to collect real-time vibration signals from the area affected by underground coal mining, and based on the real-time vibration signals from the vibration sensors, prediction and forecasting of disasters in coal mining resource areas can be achieved.

[0034] The number of the vibration sensors may be one or more, which is not specifically limited in this embodiment. In practical applications, the number of installed vibration sensors may be determined according to actual business requirements.

[0035] Step 102: Acquire a vibration waveform signal corresponding to the real-time vibration signal.

[0036] In some embodiments, based on the acquired real-time vibration signal, the real-time vibration signal is first processed by an analog filter, and then analog-to-digital (A / D) conversion is performed to obtain a digital discrete waveform signal, and then the digital discrete waveform signal is digitally filtered to obtain a vibration waveform signal corresponding to the real-time vibration signal.

[0037] Specifically, the real-time vibration signal is input into an analog filter to obtain an analog continuous waveform signal, the analog continuous waveform signal is input into an analog-to-digital conversion circuit to obtain a digital discrete waveform signal, and finally the digital discrete waveform signal is input into a digital filter to obtain a real-time vibration waveform signal.

[0038] The analog filter may use low-pass anti-aliasing filtering, but is not limited thereto.

[0039] The digital filter may be a Butterworth, Chebyshev, Bessel, or elliptic filter, but is not limited thereto. This embodiment may use any of the above filters, and this embodiment does not specifically limit this.

[0040] Step 103: Determine background noise characteristic information of the real-time vibration waveform signal.

[0041] In some embodiments, in order to accurately determine the background noise characteristic information of the real-time vibration waveform signal, one implementation method of determining the background noise characteristic information of the real-time vibration waveform signal is to obtain the background noise characteristic information of the real-time vibration waveform signal based on the time domain and frequency domain characteristics of the invalid waveform signal in the real-time vibration waveform signal, thereby accurately determining the background noise characteristic information of the real-time vibration waveform signal.

[0042] Step 104 : Processing the real-time vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal.

[0043] In some embodiments, in order to accurately determine the effective microseismic waveform signal of the real-time vibration waveform signal, before processing the real-time vibration waveform signal based on the background noise characteristic information, invalid signals can be removed and machine learning can be performed on the remaining vibration waveform signal. For example, based on the remaining vibration waveform signal, a k-means clustering algorithm (k-means) can be used to obtain the microseismic waveform signal of the real-time vibration waveform signal, and the microseismic waveform signal is input into the short-term averaging / long-term averaging (STA / LTA) method, and based on the long-term window length and short-term window length in the short-term averaging method, the longitudinal wave signal (primary wave, P-wave) and shear wave signal (secondary wave, S-wave) of the microseismic waveform signal are determined, and then, the microseismic waveform signal is refined according to the longitudinal wave signal and the shear wave signal to obtain the effective microseismic waveform signal.

[0044] In other embodiments, in order to accurately determine the effective microseismic waveform signal, after the microseismic waveform signal is finely segmented based on the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal to obtain the effective microseismic waveform signal, the autoregressive Reality-Akaike information criterion (AR-AIC) method can be used to optimize the effective microseismic waveform signal to obtain an optimized effective microseismic waveform signal, and then update the effective microseismic waveform signal.

[0045] Specifically, the autoregressive Akaike information criterion method is adopted to optimize the long-time window length and the short-time window length in the long-short-time window average method using the effective microseismic waveform signal to obtain the optimized long-time window length and the short-time window length, and the target longitudinal wave signal and the target shear wave signal of the microseismic waveform signal are determined according to the optimized long-time window length and the short-time window length, and then the effective microseismic waveform signal after the real-time vibration waveform signal is updated is obtained according to the target longitudinal wave signal and the target shear wave signal.

[0046] It is understood that the above-mentioned machine learning, long and short time window mean method, and autoregressive Akaike information criterion method can be stored in the edge computing gateway. The edge computing gateway can be built in the Linux system, but is not limited thereto.

[0047] Step 105: Acquire a clock signal corresponding to the effective microseismic waveform signal.

[0048] In some embodiments, the clock signal may be obtained through an oscilloscope, but is not limited thereto. Specifically, the clock signal corresponding to the effective microseismic waveform signal may be determined based on clock information corresponding to the effective microseismic waveform signal in the oscilloscope.

[0049] Step 106: compress the effective microseismic waveform signal and the clock signal to store them in the memory card and upload them to the host computer.

[0050] In some embodiments, the effective microseismic waveform signal and the accurate clock signal are compressed and merged to facilitate data storage and uploading. The compressed and merged data are uploaded in time when the communication status of the host computer is good. In addition, in the case of interruption of communication with the host computer, the compressed and merged data can be stored locally and uploaded to the host computer in time when the communication is restored.

[0051] The host computer may be a computer that directly issues control commands, but is not limited thereto.

[0052] The present application proposes a method for processing microseismic signals in coal mines, which monitors the mining-affected area of ​​the coal mine underground through a vibration sensor, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal, and processes the real-time vibration waveform signal based on the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, and then obtains a clock signal corresponding to the effective microseismic waveform signal, and compresses the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Therefore, in the process of processing the microseismic signals in coal mines, the effective microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby improving the accuracy of identifying the effective microseismic waveform signal and realizing high-precision monitoring of microseisms in coal mines.

[0053] In some embodiments, when multiple vibration sensors are working, the compressed signal after compressing the above-mentioned effective microseismic waveform signal and clock signal is compared with the real-time vibration signal obtained by monitoring other vibration sensors, and the parameters of other vibration sensors are adjusted according to the comparison result. For example, the collection interval of the vibration sensor can be increased, but it is not limited to this.

[0054] Based on the above embodiments, the present application also provides a processing flow chart of coal mine microseismic signals, such as Figure 2 As shown, a digital discrete waveform signal is obtained by performing analog filtering and analog-to-digital conversion on the vibration signal collected by the vibration sensor. Based on the digital filter, a vibration waveform signal corresponding to the real-time vibration signal is obtained, and according to the background noise characteristic information of the vibration waveform signal and the machine k-means clustering algorithm in the edge computing gateway, a microseismic waveform signal is obtained. Based on the STA / LTA algorithm, a valid microseismic waveform signal is determined. The AR-AIC algorithm can also be used to continuously improve and optimize the valid microseismic waveform signal, and update it, so that the effective microseismic signal is obtained while greatly reducing the data volume of the invalid waveform signal, reducing the interference of background noise, and improving the accuracy of identifying the effective microseismic signal.

[0055] Figure 3 A flow chart of a method for processing microseismic signals in a coal mine according to another embodiment of the present application is provided, wherein it should be noted that this embodiment is a further expansion or refinement of the above embodiment.

[0056] like Figure 3 As shown, it may include:

[0057] Step 301, obtaining a real-time vibration signal obtained by a vibration sensor monitoring an area affected by mining in a coal mine.

[0058] Step 302: Acquire a vibration waveform signal corresponding to the real-time vibration signal.

[0059] It should be noted that, for the explanation of the above steps 301 to 302, please refer to the relevant description of the above embodiment, which will not be repeated here.

[0060] Step 303, determining the invalid waveform signal in the real-time vibration waveform signal.

[0061] In some embodiments, the invalid waveform signal in the real-time vibration waveform signal can be determined by continuously observing the real-time vibration waveform signal.

[0062] Step 304: Determine background noise characteristic information of the real-time vibration waveform signal based on the time domain and frequency domain characteristics of the invalid waveform signal.

[0063] In some embodiments, the time domain and frequency domain characteristics of the invalid waveform signal can be determined based on the time domain waveform diagram and spectrum diagram of the invalid waveform signal to determine the background noise characteristic information of the real-time vibration waveform signal.

[0064] Step 305: Process the real-time vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal.

[0065] Step 306: Acquire a clock signal corresponding to the effective microseismic waveform signal.

[0066] Step 307: compress the effective microseismic waveform signal and the clock signal to store them in the memory card and upload them to the host computer.

[0067] The present application proposes a method for processing microseismic signals in coal mines, which monitors the mining-affected area of ​​underground coal mines through vibration sensors, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal based on the time domain and frequency domain characteristics of the invalid waveform signal in the real-time vibration waveform signal, and processes the real-time vibration waveform signal based on the background noise characteristic information to obtain a valid microseismic waveform signal of the real-time vibration waveform signal, and then obtains a clock signal corresponding to the valid microseismic waveform signal, and compresses the valid microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Therefore, in the process of processing the microseismic signals in coal mines, the valid microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby obtaining the valid microseismic waveform signal while reducing the data volume of the invalid waveform signal and the interference of the background noise.

[0068] Figure 4 A flow chart of a method for processing microseismic signals in a coal mine according to another embodiment of the present application, wherein it should be noted that the method of this embodiment is further described as follows: Figure 4 As shown, the method may include:

[0069] Step 401, obtaining a real-time vibration signal obtained by a vibration sensor monitoring an area affected by mining in a coal mine.

[0070] In some embodiments, the sampling frequency of the vibration sensor may be set to a fixed value f for monitoring, and the Nyquist-Shannon sampling theorem may be used for sampling. For example, a signal with f≥2*Fmax may be used as a real-time vibration signal.

[0071] Where f is the fixed sampling frequency of the vibration sensor, Hz, and Fmax is the maximum frequency domain width of the sampling signal, Hz.

[0072] Step 402: Acquire a vibration waveform signal corresponding to the real-time vibration signal.

[0073] In some embodiments, based on the acquired real-time vibration signal, the real-time vibration signal is first subjected to analog low-pass filtering. For example, an analog low-pass filter can be used to remove interference signals with a frequency higher than f / 3 in the vibration signal to obtain an analog continuous waveform signal.

[0074] The analog continuous waveform signal is then converted into a digital discrete waveform signal. For example, the analog continuous waveform signal is input into an A / D conversion circuit to obtain a digital discrete waveform signal. Specifically, the original time-continuous signal can be replaced by a sequence of signal sample values ​​every 1 / f seconds to generate a time-discrete analog signal. The original continuously changing amplitude value is approximated based on a finite number of amplitude values, and the continuous amplitude of the analog signal is converted into a finite number of discrete values ​​with a certain interval, thereby having a certain interval discrete value pattern. The quantized discrete value is represented by a binary digit and then converted into a binary or multi-value digital discrete waveform signal. The digital discrete waveform signal is digitally filtered to obtain a vibration waveform signal corresponding to the real-time vibration signal.

[0075] Among them, the number of stages, low-pass cutoff frequency, and high-pass cutoff frequency can be used as parameters of digital filtering for filtering, but are not limited to this.

[0076] Step 403: Determine background noise characteristic information of the real-time vibration waveform signal.

[0077] Step 404 , performing cluster analysis on the vibration waveform signal according to the background noise characteristic information of the real-time vibration waveform signal, so as to determine the microseismic waveform signal of the real-time vibration waveform signal.

[0078] In some embodiments, based on the real-time vibration waveform signal and the background noise characteristic information of the real-time vibration waveform signal, a k-means clustering analysis method is used to identify the microseismic waveform signal. For example, the i-th waveform data set Ni is obtained, and the data set length of Ni is 3 to 5 times the length of the microseismic signal stored therein. Assuming that there are m microseismic waveform signals, recording starts 1 to 2 times m bits before the start of the microseismic waveform signal, and stops recording 1 to 2 times m bits later to obtain the microseismic waveform signal in the i-th waveform data set Ni.

[0079] Step 405, based on the long time window length, long time window threshold and corresponding first first arrival time in the long and short time window average method, and the short time window length, short time window threshold and corresponding second first arrival time, determine the longitudinal wave signal and the shear wave signal of the microseismic waveform signal.

[0080] In some embodiments, for the above-mentioned i-th waveform data set Ni, the preset R(t)max (=STA / LTA), long-time window length L, long-time window threshold avgd-p and the corresponding first arrival time hit-p, as well as the short-time window length S, short-time window threshold avgd-s and the corresponding second arrival time hit-s are used to determine the P-wave and S-wave of the microseismic waveform signal in the Ni waveform data set.

[0081] Among them, the first initial arrival time hit-p can be the initial arrival time of the longitudinal wave signal of the microseismic waveform signal, and the second initial arrival time hit-s can be the initial arrival time of the transverse wave signal of the microseismic waveform signal.

[0082] It is understandable that the above-mentioned hit-p and hit-s may be obtained by continuously observing the waveform signal or by a k-means clustering analysis method, but is not limited thereto and this embodiment does not make any specific limitation thereto.

[0083] Step 406 , based on the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal, the microseismic waveform signal is finely segmented to obtain a valid microseismic waveform signal.

[0084] In some embodiments, the microseismic waveform signal P-wave, S-wave initial arrival time and waveform end E in the Ni waveform data set are determined based on the longitudinal wave signal and the shear wave signal of the microseismic waveform signal, and R(t)max, L, and S are trained using an optimization trainer, so that the microseismic waveform signal is finely cut into effective microseismic waveform signals, such as Ni-real, P-wave, S-wave, and E, where the length is equal to 0.3m+Ni-real+0.2m+4×3, and the picking hit rate and the average picking error can be used to The picking effect of the first arrival of P and S waves was evaluated and recorded as hit-p, hit-s, avgd-p and avgd-s respectively. At the same time, the autoregressive Akaike information criterion method was used to evaluate the quality of the trainer and the selection of R(t)max, L, S, hit-p, hit-s, avgd-p and avgd-s. The trainer was continuously improved and optimized until hit-p and hit-s>96% and avgd-p and avgd-s<0.02 at a certain moment. The microseismic waveform signal at that moment was obtained and used as the effective microseismic waveform signal.

[0085] Step 407: Acquire a clock signal corresponding to the effective microseismic waveform signal.

[0086] Step 408: compress the effective microseismic waveform signal and the clock signal to store them in the memory card and upload them to the host computer.

[0087] In some embodiments, each of the above Ni-real signals, P-wave, S-wave, E and the high-precision clock signal at the corresponding time is compressed and merged, and the compressed and merged data is stored on a local memory card for a preset time, such as 24 hours. In addition, while storing the compressed and merged data on the local memory card, it is uploaded to the host computer when the host computer communication network is good. In addition, when the host computer communication is interrupted, the compressed and merged data can be uploaded to the host computer in time after the network is restored.

[0088] The present application proposes a method for processing microseismic signals in coal mines, which monitors the mining-affected area of ​​coal mines underground by using a vibration sensor, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal, and performs cluster analysis on the vibration waveform signal based on the background noise characteristic information to obtain the microseismic waveform signal, and then based on the long window length and short window length in the long-short time window average method, according to the longitudinal wave signal and the transverse wave signal of the determined microseismic waveform signal, the microseismic waveform signal is finely segmented to obtain the effective microseismic waveform signal, and then the clock signal corresponding to the effective microseismic waveform signal is obtained, and the effective microseismic waveform signal and the clock signal are compressed to be stored in a memory card and uploaded to a host computer. Therefore, in the process of processing the microseismic signals in coal mines, based on the background noise characteristic information of the vibration waveform signal and the long-short time window average method, the effective microseismic waveform signal is accurately determined, compressed with the clock signal, and uploaded to the host computer, thereby improving the accuracy of judging the effective microseismic events when the background noise gradually stabilizes, and realizing the accurate judgment of the effective microseismic signals.

[0089] Based on the above embodiment, the above background noise characteristic information, filtering parameters, clustering analysis method, optimization trainer and its R(t)max, L, S, hit-p, hit-s, avgd-p, avgd-s are uploaded to the upper machine position. When multiple vibration sensors work together, the optimal parameter values ​​in the current coal mine microseismic signal processing method are extracted and sent to the underground vibration sensor to timely optimize and adjust the vibration sensor. For example, the sampling interval time and frequency range of the vibration sensor can be adjusted, but it is not limited to this.

[0090] Corresponding to the methods for processing coal mine microseismic signals provided in the above-mentioned embodiments, an embodiment of the present application also provides a device for processing coal mine microseismic signals. Since the device for processing coal mine microseismic signals provided in the embodiment of the present application corresponds to the methods for processing coal mine microseismic signals provided in the above-mentioned embodiments, the implementation method of the method for processing coal mine microseismic signals is also applicable to the device for processing coal mine microseismic signals provided in this embodiment, and will not be described in detail in this embodiment.

[0091] Figure 5 It is a structural schematic diagram of a device for processing microseismic signals in coal mines according to an embodiment of the present application.

[0092] like Figure 5 As shown, the processing device 500 for the microseismic signal of a coal mine includes a first acquisition module 501, a data acquisition module 502, a data preprocessing module 503, an effective waveform recognition module 504, a network timing module 505 and a microseismic event storage and upload module 506, wherein:

[0093] The first acquisition module 501 is used to acquire the real-time vibration signal obtained by the vibration sensor when monitoring the mining-affected area in the coal mine.

[0094] The data acquisition module 502 is used to obtain a vibration waveform signal corresponding to the real-time vibration signal.

[0095] The data preprocessing module 503 is used to determine the background noise characteristic information of the real-time vibration waveform signal.

[0096] The effective waveform identification module 504 is used to process the real-time vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal.

[0097] The network timing module 505 is used to obtain a clock signal corresponding to the effective microseismic waveform signal.

[0098] The microseismic event storage and upload module 506 is used to compress the effective microseismic waveform signal and clock signal to store them in the memory card and upload them to the host computer.

[0099] In one embodiment of the present application, the data preprocessing module 503 is specifically used to:

[0100] Determine the invalid waveform signal in the real-time vibration waveform signal.

[0101] The background noise characteristic information of the real-time vibration waveform signal is determined according to the time domain and frequency domain characteristics of the invalid waveform signal.

[0102] In one embodiment of the present application, the effective waveform identification module 504 is specifically used to:

[0103] According to the background noise characteristic information of the real-time vibration waveform signal, a cluster analysis is performed on the vibration waveform signal to determine the microseismic waveform signal of the real-time vibration waveform signal.

[0104] Based on the long-time window length, long-time window threshold and corresponding first arrival time in the long-short time window average method, as well as the short-time window length, short-time window threshold and corresponding second arrival time, the longitudinal wave signal and the shear wave signal of the microseismic waveform signal are determined.

[0105] According to the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal, the microseismic waveform signal is finely segmented to obtain the effective microseismic waveform signal.

[0106] In one embodiment of the present application, Figure 6 As shown, the device also includes:

[0107] The optimization module 507 is used to optimize the long time window length and the short time window length in the long and short time window average method by using the autoregressive Akaike information criterion method and the effective microseismic waveform signal to obtain the optimized long time window length and short time window length.

[0108] The first determination module 508 is used to determine the target longitudinal wave signal and the target shear wave signal of the microseismic waveform signal according to the optimized long time window length and short time window length.

[0109] The updating module 509 is used to obtain an effective microseismic waveform signal after the real-time vibration waveform signal is updated according to the target longitudinal wave signal and the target shear wave signal.

[0110] In one embodiment of the present application, the data acquisition module 502 is specifically used for:

[0111] The real-time vibration signal is input into the analog filter to obtain an analog continuous waveform signal.

[0112] The analog continuous waveform signal is input into the analog-to-digital conversion circuit to obtain a digital discrete waveform signal.

[0113] The digital discrete waveform signal is input into the digital filter to obtain the real-time vibration waveform signal.

[0114] In one embodiment of the present application, Figure 6 As shown, the device also includes:

[0115] The second acquisition module 510 is used to acquire a compressed signal after compressing the effective microseismic waveform signal and the clock signal.

[0116] The second determination module 511 is used to compare the compression signal with the real-time vibration signal obtained by the vibration sensor for monitoring the mining-affected area in the coal mine, and determine the comparison result.

[0117] The adjustment module 512 is used to adjust the vibration sensor according to the comparison result.

[0118] The present application proposes a processing device for coal mine microseismic signals, which monitors the mining-affected area of ​​the coal mine underground through a vibration sensor, determines the vibration waveform signal corresponding to the monitored real-time vibration signal, and determines the background noise characteristic information of the real-time vibration waveform signal, and processes the real-time vibration waveform signal based on the background noise characteristic information to obtain an effective microseismic waveform signal of the real-time vibration waveform signal, and then obtains a clock signal corresponding to the effective microseismic waveform signal, and compresses the effective microseismic waveform signal and the clock signal to store them in a memory card and upload them to a host computer. Therefore, in the process of processing the coal mine microseismic signals, the effective microseismic waveform signal is accurately determined based on the background noise characteristic information of the vibration waveform signal, thereby improving the accuracy of identifying the effective microseismic waveform signal and realizing high-precision monitoring of coal mine microseisms.

[0119] According to an embodiment of the present application, the present application also provides a non-transitory computer-readable storage medium storing computer instructions, and the computer instructions are used to enable a computer to execute the method for processing coal mine microseismic signals disclosed in the embodiment of the present application.

[0120] The present application also proposes a computer program product, which, when an instruction processor in the computer program product executes, implements the method for processing coal mine microseismic signals of the embodiment of the present application.

[0121] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0123] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for processing microseismic signals in coal mines, characterized in that: The method comprises: Acquire the real-time vibration signal obtained by the vibration sensor when monitoring the mining-affected area in the coal mine; Acquire a vibration waveform signal corresponding to the real-time vibration signal; Determining background noise characteristic information of the vibration waveform signal; Processing the vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the vibration waveform signal; Acquire a clock signal corresponding to the effective microseismic waveform signal; Compressing the effective microseismic waveform signal and clock signal to store them in a memory card and upload them to a host computer; The processing of the vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the vibration waveform signal includes: Performing cluster analysis on the vibration waveform signal according to background noise characteristic information of the vibration waveform signal to determine a microseismic waveform signal of the vibration waveform signal; Based on the long-time window length, long-time window threshold and corresponding first arrival time in the long-short time window average method, as well as the short-time window length, short-time window threshold and corresponding second arrival time, the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal are determined; According to the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal, the microseismic waveform signal is finely segmented to obtain the effective microseismic waveform signal; After the microseismic waveform signal is finely segmented according to the longitudinal wave signal and the shear wave signal of the microseismic waveform signal to obtain the effective microseismic waveform signal, the method further includes: The autoregressive Akaike information criterion method is adopted to optimize the long time window length and the short time window length in the long and short time window mean method by using the effective microseismic waveform signal, so as to obtain the optimized long time window length and the short time window length; Determining a target longitudinal wave signal and a target shear wave signal of the microseismic waveform signal according to the optimized long time window length and short time window length; According to the target longitudinal wave signal and the target shear wave signal, an effective microseismic waveform signal after the vibration waveform signal is updated is obtained; After compressing the effective microseismic waveform signal and the clock signal to store them in the memory card and upload them to the host computer, the method further includes: Obtaining a compressed signal after compressing the effective microseismic waveform signal and the clock signal; Determine a comparison result by comparing the compression signal with a real-time vibration signal obtained by the vibration sensor monitoring the mining-affected area in the coal mine; The vibration sensor is adjusted according to the comparison result.

2. The method according to claim 1, characterized in that The determining of the background noise characteristic information of the vibration waveform signal comprises: Determining an invalid waveform signal in the vibration waveform signal; The background noise characteristic information of the vibration waveform signal is determined according to the time domain and frequency domain characteristics of the invalid waveform signal.

3. The method according to claim 1, characterized in that The step of acquiring a vibration waveform signal corresponding to the real-time vibration signal comprises: Inputting the real-time vibration signal into an analog filter to obtain an analog continuous waveform signal; Inputting the analog continuous waveform signal into an analog-to-digital conversion circuit to obtain a digital discrete waveform signal; The digital discrete waveform signal is input into a digital filter to obtain the vibration waveform signal.

4. A processing device for microseismic signals in coal mines, characterized in that: The device comprises: The first acquisition module is used to acquire the real-time vibration signal obtained by the vibration sensor monitoring the mining-affected area in the coal mine; A data acquisition module, used to obtain a vibration waveform signal corresponding to the real-time vibration signal; A data preprocessing module, used to determine background noise characteristic information of the vibration waveform signal; An effective waveform recognition module, used for processing the vibration waveform signal according to the background noise characteristic information to obtain an effective microseismic waveform signal of the vibration waveform signal; A network timing module, used to obtain a clock signal corresponding to the effective microseismic waveform signal; A microseismic event storage and upload module, used for compressing the effective microseismic waveform signal and clock signal to store them in a memory card and upload them to a host computer; The effective waveform recognition module is specifically used for: Performing cluster analysis on the vibration waveform signal according to background noise characteristic information of the vibration waveform signal to determine a microseismic waveform signal of the vibration waveform signal; Based on the long-time window length, long-time window threshold and corresponding first arrival time in the long-short time window average method, as well as the short-time window length, short-time window threshold and corresponding second arrival time, the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal are determined; According to the longitudinal wave signal and the transverse wave signal of the microseismic waveform signal, the microseismic waveform signal is finely segmented to obtain the effective microseismic waveform signal; The device also includes: An optimization module is used to optimize the long time window length and the short time window length in the long and short time window mean method by using the effective microseismic waveform signal using the autoregressive Akaike information criterion method to obtain the optimized long time window length and short time window length; A first determination module is used to determine a target longitudinal wave signal and a target shear wave signal of the microseismic waveform signal according to the optimized long time window length and short time window length; An updating module, used for obtaining an effective microseismic waveform signal after the vibration waveform signal is updated according to the target longitudinal wave signal and the target shear wave signal; A second acquisition module is used to acquire a compressed signal after compressing the effective microseismic waveform signal and the clock signal; A second determination module is used to compare the compression signal with a real-time vibration signal obtained by the vibration sensor monitoring the mining-affected area in the coal mine, and determine a comparison result; An adjustment module is used to adjust the vibration sensor according to the comparison result.

5. The device according to claim 4, characterized in that The data preprocessing module is specifically used for: Determining an invalid waveform signal in the vibration waveform signal; The background noise characteristic information of the vibration waveform signal is determined according to the time domain and frequency domain characteristics of the invalid waveform signal.

6. The device according to claim 4, characterized in that The data acquisition module is specifically used for: Inputting the real-time vibration signal into an analog filter to obtain an analog continuous waveform signal; Inputting the analog continuous waveform signal into an analog-to-digital conversion circuit to obtain a digital discrete waveform signal; The digital discrete waveform signal is input into a digital filter to obtain the vibration waveform signal.

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