Micro-seismic signal integrated monitoring and processing system based on in-situ digitization

Abstract

The invention relates to the technical field of micro-seismic monitoring, and discloses an in-situ digitization-based micro-seismic signal integrated monitoring and processing system, which comprises a micro-seismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digitization analysis module and a micro-seismic signal fluctuation identification module, the method comprises the following steps: carrying out micro-seismic signal fluctuation monitoring on a target area by using a micro-seismic sensor to obtain real value signals at different time, comparing the real value signals with corresponding baseline signals to obtain index signals for micro-seismic signal fluctuation monitoring, and obtaining index characteristic signals; a digital analysis model is established in combination with the index characteristic signal to perform digital analysis on the index characteristic signal, a micro-seismic signal fluctuation index is obtained to perform risk identification on micro-seismic signal fluctuation, real-time monitoring of the micro-seismic signal is realized through an in-situ digital technology, and timeliness and effectiveness of data are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microseismic monitoring, and more particularly to an integrated monitoring and processing system for microseismic signals based on in-situ digitization. Background Art

[0002] The integrated monitoring and processing system for microseismic signals is a new type of high-tech monitoring technology developed based on the principles of acoustic emission and seismology. This technology monitors the underground state and its impact on production activities by observing and analyzing tiny seismic events generated by underground rock fractures or human activities. These tiny seismic events generate weak seismic waves that propagate around and are received by geophones arranged around the fracture area. In-situ digitization technology is an important part of this system, which can directly convert the monitored physical signals into digital signals for subsequent data processing and analysis. This technology ensures the accuracy and real-time nature of the signals and provides a reliable basis for subsequent data processing.

[0003] However, there are still some deficiencies in the existing integrated monitoring and processing system for microseismic signals. For example, when real-time monitoring microseismic signals, the timeliness and effectiveness of data are often restricted by various factors, such as data transmission delay and insufficient data processing capabilities; when real-time monitoring microseismic signals, the energy of microseismic signals is analyzed using the wave source, but the deviation of the wave source under the influence of microseismic signals is ignored, resulting in inaccurate energy analysis of microseismic signals and affecting the monitoring effect of microseismic signals. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an integrated monitoring and processing system for microseismic signals based on in-situ digitization to solve the problems existing in the above-mentioned background art.

[0005] The present invention provides the following technical solutions: An integrated monitoring and processing system for microseismic signals based on in-situ digitization, comprising: a microseismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digitization analysis module, and a microseismic signal fluctuation identification module; The microseismic signal fluctuation monitoring module uses microseismic sensors to monitor the microseismic signal fluctuations in the target area, and when abnormal fluctuations are detected, it transmits the monitoring data to the signal fluctuation acquisition and comparison module in real time; The signal fluctuation acquisition and comparison module performs signal acquisition at different times on the monitoring signals transmitted in real time by the microseismic signal fluctuation monitoring module, obtains real-valued signals at different times, and compares the real-valued signals with the corresponding baseline signals to obtain the index signals for microseismic signal fluctuation monitoring; The index signal feature extraction module extracts the fluctuation features of the index signal for monitoring the microseismic signal fluctuations to obtain the index feature signal, and transmits the index feature signal to the feature digital analysis module; The feature digital analysis module, based on the index feature signal transmitted by the index feature signal extraction module, establishes a digital analysis model to digitally analyze the index feature signal, obtains the microseismic signal fluctuation index, and transmits it to the microseismic signal fluctuation identification module; The microseismic signal fluctuation identification module, based on the microseismic signal fluctuation index transmitted by the feature signal digital analysis module, identifies the risk of the microseismic signal fluctuations and completes the integrated monitoring and processing of the microseismic signal.

[0006] Preferably, in the microseismic signal fluctuation monitoring module, the microseismic sensor monitors the vibration signal in the target area in real time, and the microseismic signal integrated monitoring terminal stores the vibration signal in the target area, and transmits the signal and the pre-stored signal when the monitoring is normal to the signal fluctuation acquisition and comparison module.

[0007] Preferably, the specific content of the signal fluctuation acquisition and comparison module is as follows: Collect the monitoring signals at different times to obtain the real value signals at different times, and the real value signals at different times represent the maximum waveform amplitudes corresponding to different times; Analyze the baseline signals at different times based on the maximum waveform amplitudes corresponding to different times, and the baseline signals represent the maximum waveform amplitudes adjusted by the real value signals at different times; Obtain the index signal for monitoring the microseismic signal fluctuations based on the real value signals at different times and the baseline signals at different times, and the index signal represents the difference between the maximum waveform amplitudes of the real value signals and the baseline signals at different times.

[0008] Preferably, the specific content of collecting the monitoring signals at different times to obtain the real value signals at different times is as follows: Perform time-frequency division on the monitoring signal of the target area, divide it into T segment monitoring sub-signals, where t = 1, 2, 3,..., T, T represents the total number of monitoring sub-signals, and t represents the number of the monitoring sub-signal; Perform local division on each monitoring sub-signal, divide it into k local signals, and obtain the waveform amplitudes of the k local signals in each monitoring sub-signal, denoted as , take the maximum value of the waveform amplitudes of the k local signals in each monitoring sub-signal as the real value signal in the monitoring sub-signal, and obtain the real value signals in each monitoring sub-signal, denoted as , where .

[0009] Preferably, the specific content of analyzing the baseline signals at different times based on the maximum waveform amplitudes corresponding to different times is as follows: The baseline signals at different times are expressed as , where , where represents the baseline signals at different times, represents the preset waveform amplitudes at different times when the monitoring is normal, represents the waveform amplitudes of k local signals at different times when the monitoring is normal, represents the real-valued signals in each monitoring sub-signal at different times, represents the waveform amplitudes of k local signals at different times.

[0010] Preferably, the specific content of obtaining the index signals for microseismic signal fluctuation monitoring based on the real-valued signals at different times and the baseline signals at different times is as follows: The index signals for microseismic signal fluctuation monitoring at different times are expressed as ; The expression formula of the index signals for microseismic signal fluctuation monitoring at different times is: , where represents the index signals for microseismic signal fluctuation monitoring at different times, represents the real-valued signals at different times, represents the baseline signals at different times.

[0011] Preferably, in the index feature signal extraction module, the fluctuation features of the index signals for microseismic signal fluctuation monitoring are extracted to obtain the index feature signals. The index signals represent the difference between the maximum waveform amplitudes of the real-valued signals and the baseline signals at different times, and the index feature signals represent the mean value of the index signals. The calculation formula is: , where represents the output value of the index feature signals, represents the index signals for microseismic signal fluctuation monitoring at different times.

[0012] Preferably, in the feature digital analysis module, based on the index feature signals transmitted by the index feature signal extraction module, the specific content of establishing a digital analysis model to perform digital analysis on the index feature signals is as follows: Collect P-wave data and S-wave data for the microseismic signals monitored by the microseismic signal fluctuation monitoring module. The P-wave data includes the medium density, P-wave velocity, P-wave duration, and P-wave amplitude of the target area, and the S-wave data includes the medium density, S-wave velocity, S-wave duration, and S-wave amplitude of the target area; Based on the output value of the index feature signals and the P-wave data, establish a P-wave digital analysis model to calculate the P-wave energy. The calculation formula is: , where represents the P-wave energy, represents the medium density of the target area, represents the P-wave velocity, represents the P-wave duration, represents the P-wave amplitude, represents the output value of the index characteristic signal; Based on the output value of the index characteristic signal and the S-wave data, an S-wave digital analysis model is established to calculate the S-wave energy. The calculation formula is: , where represents the S-wave energy, represents the medium density of the target area, represents the S-wave velocity, represents the S-wave duration, represents the S-wave amplitude, represents the output value of the index characteristic signal; Combining the P-wave energy and the S-wave energy to calculate the microseismic signal fluctuation index. The calculation formula is: , where represents the microseismic signal fluctuation index, represents the P-wave energy, represents the S-wave energy, represents the sum of the P-wave energy and the S-wave energy when the monitoring is normal.

[0013] Preferably, in the microseismic signal fluctuation recognition module, risk recognition is performed based on the microseismic signal fluctuation index transmitted by the characteristic signal digital analysis module: when the microseismic signal fluctuation index is less than the preset risk recognition threshold, it is determined that the recognition result of the target area is risk-free; on the contrary, when the microseismic signal fluctuation index is greater than or equal to the preset risk recognition threshold, it is determined that the recognition result of the target area is risky. The microseismic signal integrated monitoring terminal receives the risk recognition result and issues a warning message.

[0014] The technical effects and advantages of the present invention: The present invention is provided with a microseismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digital analysis module, and a microseismic signal fluctuation recognition module. The microseismic sensor is used to monitor the microseismic signal fluctuation of the target area to obtain the real-value signal at different times. The real-value signal is compared with the corresponding baseline signal to obtain the index signal for microseismic signal fluctuation monitoring, and the index characteristic signal is obtained. By comparing the real-value signal with the baseline signal, the change of the microseismic signal can be more accurately identified, improving the accuracy of monitoring and enhancing the signal recognition ability; A digital analysis model is established by combining index feature signals to digitally analyze the index feature signals, obtaining the microseismic signal fluctuation index to identify the risk of microseismic signal fluctuations. Through in-situ digital technology, real-time monitoring of microseismic signals is achieved, and they are converted into digital signals for processing and analysis. A large amount of data is efficiently processed through the digital analysis model, the index features of microseismic signals are extracted, and then the microseismic signal fluctuation index is calculated to quickly identify the fluctuation of microseismic signals, providing strong support for risk identification, timely detecting abnormal fluctuations of microseismic signals, and triggering an early warning mechanism. Description of the Drawings

[0015] Figure 1 It is a schematic structural diagram of an integrated microseismic signal monitoring and processing system based on in-situ digitization. Detailed Embodiments

[0016] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the drawings in the present invention. In addition, the forms of each structure described in the following embodiments are merely examples, and an integrated microseismic signal monitoring and processing system based on in-situ digitization involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0017] As Figure 1 shown, the present invention provides an integrated microseismic signal monitoring and processing system based on in-situ digitization, including: a microseismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digital analysis module, and a microseismic signal fluctuation identification module; The microseismic signal fluctuation monitoring module uses a microseismic sensor to monitor the microseismic signal fluctuations in the target area, and when abnormal fluctuations are detected, the monitoring data is transmitted to the signal fluctuation acquisition and comparison module in real time; The signal fluctuation acquisition and comparison module performs signal acquisition at different times on the monitoring signals transmitted in real time by the microseismic signal fluctuation monitoring module, obtains the real-value signals at different times, and compares the real-value signals with the corresponding baseline signals to obtain the index signals for microseismic signal fluctuation monitoring; The index signal feature extraction module extracts the fluctuation features of the index signals for microseismic signal fluctuation monitoring to obtain index feature signals, and transmits the index feature signals to the feature digital analysis module; The feature digital analysis module, based on the index feature signals transmitted by the index signal feature extraction module, establishes a digital analysis model to digitally analyze the index feature signals, obtains the microseismic signal fluctuation index, and transmits it to the microseismic signal fluctuation identification module; The microseismic signal fluctuation identification module performs risk identification on the microseismic signal fluctuations based on the microseismic signal fluctuation index transmitted by the feature signal digital analysis module and completes the integrated monitoring and processing of the microseismic signals.

[0018] In this embodiment, it should be specifically noted that in the microseismic signal fluctuation monitoring module, the microseismic sensor continuously monitors the vibration signals in the target area. The microseismic signal integrated monitoring terminal stores the vibration signals in the target area and transmits the signals and the pre-stored signals when the monitoring is normal to the signal fluctuation acquisition and comparison module for subsequent signal processing and risk identification. In practical applications, the high sensitivity and real-time monitoring ability of the microseismic sensor ensure that the system can quickly capture the microseismic signal fluctuations in the target area, providing reliable data support for subsequent analysis and processing.

[0019] In this embodiment, it should be specifically noted that the specific content of the signal fluctuation acquisition and comparison module is as follows: Collect the monitoring signals at different times to obtain the real-valued signals at different times. The real-valued signals at different times represent the maximum waveform amplitudes corresponding to different times. Analyze the baseline signals at different times based on the maximum waveform amplitudes corresponding to different times. The baseline signals represent the maximum waveform amplitudes adjusted by the real-valued signals at different times. Obtain the index signals for microseismic signal fluctuation monitoring based on the real-valued signals at different times and the baseline signals at different times. The index signals represent the differences between the maximum waveform amplitudes of the real-valued signals and the baseline signals at different times.

[0020] In this embodiment, it should be specifically noted that the specific content of collecting the monitoring signals at different times to obtain the real-valued signals at different times is as follows: Perform time-frequency partitioning on the monitoring signals in the target area, dividing them into T monitoring sub-signals, where t = 1, 2, 3,..., T. Here, T represents the total number of monitoring sub-signals, and t represents the number of the monitoring sub-signal. Perform local partitioning on each monitoring sub-signal, dividing each monitoring sub-signal into k local signals, and obtain the waveform amplitudes of the k local signals in each monitoring sub-signal, denoted as Take the maximum value among the waveform amplitudes of the k local signals in each monitoring sub-signal as the real-valued signal in the monitoring sub-signal, and obtain the real-valued signals in each monitoring sub-signal, denoted as where If is then .

[0021] In this embodiment, it should be specifically noted that the specific content of analyzing the baseline signal at different times based on the maximum waveform amplitude corresponding to different times is as follows: The baseline signal at different times is expressed as , where , where represents the baseline signal at different times, represents the preset waveform amplitude at different times when the monitoring is normal, represents the waveform amplitude of k local signals at different times when the monitoring is normal, represents the real-valued signal in each monitoring sub-signal at different times, represents the waveform amplitude of k local signals at different times; When k = 5: , where: ; ; ; .

[0022] In this embodiment, it should be specifically noted that the specific content of obtaining the index signal for microseismic signal fluctuation monitoring based on the real-valued signal at different times and the baseline signal at different times is as follows: Perform time-frequency division on the monitoring signals of the target area, dividing them into T segments of monitoring sub-signals, where t = 1, 2, 3,..., T, T represents the total number of monitoring sub-signals, and t represents the number of the monitoring sub-signal; Perform local division on each monitoring sub-signal, dividing it into k local signals, and obtain the waveform amplitudes of the k local signals in each monitoring sub-signal, expressed as , and take the maximum value among the waveform amplitudes of the k local signals in each monitoring sub-signal as the real-valued signal in the monitoring sub-signal, and obtain the real-valued signal in each monitoring sub-signal, expressed as , where ; The index signal for microseismic signal fluctuation monitoring at different times is expressed as ; The expression of the index signal for microseismic signal fluctuation monitoring at different times is: , where represents the index signal for microseismic signal fluctuation monitoring at different times, represents the real-valued signal at different times, represents the baseline signal at different times.

[0023] In this embodiment, it should be specifically noted that in the index feature signal extraction module, the fluctuation characteristics of the index signal for monitoring the microseismic signal fluctuation are extracted to obtain the index feature signal. The index signal represents the difference between the maximum amplitude of the real-value signal and the baseline signal at different times, and the index feature signal represents the mean value of the index signal. The calculation formula is: , where represents the output value of the index feature signal, represents the index signal for monitoring the microseismic signal fluctuation at different times. If are 0.3, 0.3, 0.6, 0.4, 0.2, 0.3 respectively, then .

[0024] In this embodiment, it should be specifically noted that in the feature digital analysis module, based on the index feature signal transmitted by the index feature signal extraction module, the specific content of establishing a digital analysis model to perform digital analysis on the index feature signal is as follows: Collect P-wave data and S-wave data of the microseismic signal monitored by the microseismic signal fluctuation monitoring module. The P-wave data includes the medium density of the target area, P-wave velocity, P-wave duration, and P-wave amplitude. The S-wave data includes the medium density of the target area, S-wave velocity, S-wave duration, and S-wave amplitude; Based on the output value of the index feature signal and the P-wave data, establish a P-wave digital analysis model to calculate the P-wave energy. The calculation formula is: , where represents the P-wave energy, represents the medium density of the target area, represents the P-wave velocity, represents the P-wave duration, represents the P-wave amplitude, represents the output value of the index feature signal, represents the integral of the square of the difference between the P-wave amplitude and the output value of the index feature signal; Based on the output value of the index feature signal and the S-wave data, establish an S-wave digital analysis model to calculate the S-wave energy. The calculation formula is: , where represents the S-wave energy, represents the medium density of the target area, represents the S-wave velocity, represents the S-wave duration, represents the S-wave amplitude, represents the output value of the index feature signal, represents the integral of the square of the difference between the S-wave amplitude and the output value of the index feature signal; Calculate the microseismic signal fluctuation index by combining the P-wave energy and the S-wave energy. The calculation formula is: , where represents the microseismic signal fluctuation index, represents the P-wave energy, represents the S-wave energy, represents the sum of the P-wave energy and the S-wave energy when the monitoring is normal.

[0025] In this embodiment, it should be specifically noted that in the microseismic signal fluctuation recognition module, risk recognition is performed based on the microseismic signal fluctuation index transmitted by the feature signal digital analysis module: when the microseismic signal fluctuation index is less than the preset risk recognition threshold, it is determined that the recognition result of the target area is risk-free; on the contrary, when the microseismic signal fluctuation index is greater than or equal to the preset risk recognition threshold, it is determined that the recognition result of the target area is risky. The microseismic signal integrated monitoring terminal receives the risk recognition result and issues a warning message; The ways for the microseismic signal integrated monitoring terminal to issue warning messages can include sound alarms, light alarms, or wirelessly transmitting the warning messages to the handheld terminals of relevant personnel so that relevant personnel can quickly take countermeasures to ensure the safety of the target area; in addition, the microseismic signal integrated monitoring terminal can also record the historical data of risk recognition to provide a reference for subsequent data analysis and processing.

[0026] In this embodiment, it should be specifically noted that the main difference between this embodiment and the prior art is that this embodiment is provided with a microseismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digital analysis module, and a microseismic signal fluctuation recognition module. The microseismic sensor is used to monitor the microseismic signal fluctuations in the target area to obtain the real-value signals at different times, and the real-value signals are compared with the corresponding baseline signals to obtain the index signals for microseismic signal fluctuation monitoring, and the index feature signals are obtained. By comparing the real-value signals with the baseline signals, the changes in the microseismic signals can be more accurately identified, the monitoring accuracy can be improved, and the signal recognition ability can be enhanced; A digital analysis model is established in combination with the index feature signals to perform digital analysis on the index feature signals, and the microseismic signal fluctuation index is obtained to perform risk recognition on the microseismic signal fluctuations. Through in-situ digital technology, real-time monitoring of the microseismic signals is realized, and they are converted into digital signals for processing and analysis. A large amount of data is efficiently processed through the digital analysis model, the index features of the microseismic signals are extracted, and then the microseismic signal fluctuation index is calculated to quickly identify the fluctuation conditions of the microseismic signals, providing strong support for risk recognition, timely discovering abnormal fluctuations in the microseismic signals, and triggering the warning mechanism.

[0027] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0028] As described above, this is only the specific implementation manner of this application. However, the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or replacements, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims described.

Claims

1. An integrated monitoring and processing system for microseismic signals based on in-situ digitization, characterized in that: Including: A microseismic signal fluctuation monitoring module, a signal fluctuation acquisition and comparison module, an index signal feature extraction module, a feature digital analysis module, and a microseismic signal fluctuation identification module; The microseismic signal fluctuation monitoring module uses a microseismic sensor to monitor the microseismic signal fluctuations in the target area, and transmits the monitoring data to the signal fluctuation acquisition and comparison module in real time when abnormal fluctuations are detected; The signal fluctuation acquisition and comparison module performs signal acquisition at different times on the monitoring signals transmitted in real time by the microseismic signal fluctuation monitoring module, obtains the real-value signals at different times, and compares the real-value signals with the corresponding baseline signals to obtain the index signals for microseismic signal fluctuation monitoring; The index signal feature extraction module extracts the fluctuation features of the index signals for microseismic signal fluctuation monitoring to obtain index feature signals, and transmits the index feature signals to the feature digital analysis module; The feature digital analysis module, based on the index feature signals transmitted by the index feature signal extraction module, establishes a digital analysis model to perform digital analysis on the index feature signals, obtains the microseismic signal fluctuation index, and transmits it to the microseismic signal fluctuation identification module; The microseismic signal fluctuation identification module, based on the microseismic signal fluctuation index transmitted by the feature signal digital analysis module, performs risk identification on the microseismic signal fluctuations and completes the integrated monitoring and processing of the microseismic signals.

2. The integrated monitoring and processing system for microseismic signals based on in-situ digitization according to claim 1, wherein: In the microseismic signal fluctuation monitoring module, the microseismic sensor monitors the vibration signals in the target area in real time, and the microseismic signal integrated monitoring terminal stores the vibration signals in the target area, and transmits the signals and the pre-stored signals when the monitoring is normal to the signal fluctuation acquisition and comparison module.

3. The integrated microseismic signal monitoring and processing system based on in-situ digitization according to claim 1, wherein: The specific content of the signal fluctuation acquisition and comparison module is as follows: Perform signal acquisition at different times on the monitoring signals to obtain the real-value signals at different times, and the real-value signals at different times represent the maximum waveform amplitudes corresponding to different times; Analyze the baseline signals at different times based on the maximum waveform amplitudes corresponding to different times, and the baseline signals represent the maximum waveform amplitudes adjusted by the real-value signals at different times; Obtain the index signals for microseismic signal fluctuation monitoring based on the real-value signals at different times and the baseline signals at different times, and the index signals represent the difference between the maximum waveform amplitudes of the real-value signals and the baseline signals at different times.

4. The integrated monitoring and processing system for microseismic signals based on in-situ digitization according to claim 3, wherein: The specific content of performing signal acquisition at different times on the monitoring signals to obtain the real-value signals at different times is as follows: Perform time-frequency division on the monitoring signals in the target area, divide them into T segment monitoring sub-signals, where t = 1, 2, 3,..., T, T represents the total number of monitoring sub-signals, and t represents the number of the monitoring sub-signals; Perform local partitioning on each monitored sub-signal, partitioning it into k local signals, and obtain the waveform amplitudes of the k local signals in each monitored sub-signal, denoted as , take the maximum value among the waveform amplitudes of the k local signals in each monitored sub-signal as the real-valued signal in the monitored sub-signal, and obtain the real-valued signals in each monitored sub-signal, denoted as , where .

5. The integrated microseismic signal monitoring and processing system based on in-situ digitization according to claim 4, characterized in that: The specific content of analyzing the baseline signals at different times based on the maximum waveform amplitudes corresponding to different times is as follows: The baseline signals at different times are represented as , where , where represents the baseline signals at different times, represents the preset waveform amplitudes at different times when the monitoring is normal, represents the waveform amplitudes of k local signals at different times when the monitoring is normal, represents the real-valued signals in each monitoring sub-signal at different times, represents the waveform amplitudes of k local signals at different times.

6. The integrated microseismic signal monitoring and processing system based on in-situ digitization according to claim 3, characterized in that: The specific content of obtaining the index signals for microseismic signal fluctuation monitoring based on the real-value signals at different times and the baseline signals at different times is as follows: The index signals for monitoring the fluctuations of microseismic signals at different times are expressed as ; The expression of the index signal for monitoring the microseismic signal fluctuation at different times is as follows: , where represents the index signal for monitoring the microseismic signal fluctuation at different times, represents the real-valued signal at different times, represents the baseline signal at different times.

7. The integrated monitoring and processing system for microseismic signals based on in-situ digitization according to claim 1, characterized in that: In the index feature signal extraction module, the fluctuation characteristics of the index signal for microseismic signal fluctuation monitoring are extracted to obtain the index feature signal. The index signal represents the difference between the maximum amplitude of the waveforms of the real-valued signal and the baseline signal at different times, and the index feature signal represents the mean value of the index signal. The calculation formula is: , where represents the output value of the index feature signal, represents the index signal for microseismic signal fluctuation monitoring at different times.

8. The integrated microseismic signal monitoring and processing system based on in-situ digitization according to claim 1, characterized in that: In the feature digital analysis module, the specific content of establishing a digital analysis model to perform digital analysis on the index feature signals based on the index feature signals transmitted by the index feature signal extraction module is as follows: Collect P-wave data and S-wave data of the microseismic signals monitored by the microseismic signal fluctuation monitoring module. The P-wave data includes the medium density, P-wave velocity, P-wave duration, and P-wave amplitude of the target area, and the S-wave data includes the medium density, S-wave velocity, S-wave duration, and S-wave amplitude of the target area; Based on the output value of the index characteristic signal and the P-wave data, a P-wave digital analysis model is established to calculate the P-wave energy. The calculation formula is as follows: , where represents the P-wave energy, represents the medium density of the target area, represents the P-wave velocity, represents the P-wave duration, represents the P-wave amplitude, represents the output value of the index characteristic signal; Based on the output value of the index characteristic signal and the S-wave data, an S-wave digital analysis model is established to calculate the S-wave energy. The calculation formula is as follows: , where represents the S-wave energy, represents the medium density of the target area, represents the S-wave velocity, represents the S-wave duration, represents the S-wave amplitude, represents the output value of the index characteristic signal; Calculate the microseismic signal fluctuation index by combining P-wave energy and S-wave energy. The calculation formula is as follows: , where represents the microseismic signal fluctuation index, represents the P-wave energy, represents the S-wave energy, represents the sum of the P-wave energy and the S-wave energy when the monitoring is normal.

9. The integrated microseismic signal monitoring and processing system based on in-situ digitization according to claim 1, characterized in that: In the microseismic signal fluctuation identification module, risk identification is performed based on the microseismic signal fluctuation index transmitted by the characteristic signal digital analysis module: when the microseismic signal fluctuation index is less than the preset risk identification threshold, it is determined that the identification result of the target area is risk-free; on the contrary, when the microseismic signal fluctuation index is greater than or equal to the preset risk identification threshold, it is determined that the identification result of the target area is risky. The microseismic signal integrated monitoring terminal receives the risk identification result and issues a warning message.

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