Micro-seismic energy correction method and device
By integrating waveform signals and performing clustering and relative correction, the uncertainty problem in microseismic energy calculation was solved, and accurate microseismic energy calculation was achieved, providing a reliable early warning basis for coal mine safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-27
AI Technical Summary
Existing methods for calculating microseismic energy rely on the accuracy of sensor responses and location results, leading to accumulated uncertainty that affects the assessment of the impact of microseismic events and safety production decisions.
By integrating multiple waveform signals, microseismic events are clustered using the cross-correlation coefficients of the waveforms, and relative corrections are performed within the clusters. An overdetermined set of equations is constructed and solved, unifying the results onto an absolute magnitude scale and eliminating the influence of propagation paths and site effects.
It enables precise calculation of microseismic energy, providing a more reliable basis for microseismic activity trend analysis and rockburst disaster early warning, and eliminating the influence of propagation path and site effects.
Smart Images
Figure CN121741815A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of coal mine safety technology, and in particular to a micro-vibration energy correction method and device. Background Technology
[0002] In coal mines, microseismic monitoring is a common method for preventing rockburst disasters. Many mines have microseismic monitoring systems installed. A standardized microseismic monitoring process generally includes microseismic event acquisition, microseismic event location, and microseismic energy calculation. The calculation of microseismic energy helps determine the potential impact of microseismic events, influencing safety production decisions. However, current standard microseismic energy calculation methods rely on calculating the absolute energy of microseismic events, which is highly dependent on sensor response and coupling, as well as the accuracy of microseismic event location results. This introduces cumulative uncertainty into the microseismic energy calculation. Summary of the Invention
[0003] The present invention aims to at least partially solve one of the technical problems in the related art.
[0004] Therefore, the first objective of this invention is to propose a microseismic energy correction method. By integrating multiple waveform signals and using the cross-correlation coefficients of the waveforms to cluster microseismic events, and performing relative correction within the clusters, the influence of propagation paths and site effects can be effectively eliminated, and accurate microseismic energy can be obtained, providing a more reliable basis for microseismic activity trend analysis and rockburst disaster early warning.
[0005] The second objective of this invention is to provide a micro-vibration energy correction device.
[0006] The third objective of this invention is to provide an electronic device.
[0007] The fourth objective of this invention is to provide a non-transitory computer-readable storage medium storing computer instructions.
[0008] To achieve the above objectives, a first aspect of the present invention provides a microseismic energy correction method, the method comprising: Acquire waveform signals corresponding to multiple microseismic events; Based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal, multiple microseismic event clusters corresponding to different waveforms are identified; Based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, an overdetermined set of equations is constructed and solved to obtain a relative magnitude value that is consistent within the microseismic event cluster. The relative magnitude values of each microseismic event cluster are unified onto the absolute magnitude scale of a benchmark microseismic event and converted into microseismic energy.
[0009] To achieve the above objectives, a second aspect of the present invention provides a microseismic energy correction device, the device comprising: The acquisition module is used to acquire waveform signals corresponding to multiple microseismic events; The segmentation module is used to segment multiple microseismic event clusters corresponding to different waveforms based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal. The module is used to construct an overdetermined set of equations based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, and to solve the overdetermined set of equations to obtain a relative magnitude value with consistency within the microseismic event cluster. The conversion module is used to unify the relative magnitude values of each microseismic event cluster onto an absolute magnitude scale of a benchmark microseismic event and convert them into microseismic energy.
[0010] To achieve the above objectives, a third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.
[0011] To achieve the above objectives, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described in the first aspect.
[0012] The microseismic energy correction method and apparatus of this invention acquire waveform signals corresponding to multiple microseismic events; based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal, multiple microseismic event clusters corresponding to different waveforms are divided; based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, an overdetermined equation system is constructed and solved to obtain relative magnitude values with internal consistency within the microseismic event cluster; the relative magnitude values of each microseismic event cluster are unified onto an absolute magnitude scale of a benchmark microseismic event and converted into microseismic energy. Therefore, by integrating multiple waveform signals, using the cross-correlation coefficients of the waveforms to cluster microseismic events, and performing relative correction within each cluster, the influence of propagation paths and site effects can be effectively eliminated, resulting in accurate microseismic energy and providing a more reliable basis for microseismic activity trend analysis and rockburst disaster early warning.
[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a microseismic energy correction method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of a microseismic energy correction method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a micro-vibration energy correction device provided in an embodiment of the present invention. Detailed Implementation
[0015] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.
[0016] It should be noted that the acquisition, storage, use, and processing of data in this application's technical solution all comply with the relevant provisions of laws and regulations.
[0017] The microseismic energy correction method and apparatus of the present invention are described below with reference to the accompanying drawings.
[0018] Figure 1 This is a schematic flowchart of a microseismic energy correction method provided in an embodiment of the present invention.
[0019] like Figure 1 As shown, the method includes the following steps: Step 101: Obtain waveform signals corresponding to multiple microseismic events.
[0020] In some embodiments, problems such as interference, data redundancy, and poor data comparability in waveform signals are addressed. For example, the original waveform signals corresponding to multiple microseismic events are acquired. These original waveform signals are then preprocessed to obtain waveform signals corresponding to multiple microseismic events. The preprocessing includes: filtering and noise reduction, time window truncation, and waveform normalization. Thus, by standardizing the original waveform signal of each microseismic event, noise interference is eliminated and effective signal characteristics are highlighted.
[0021] For example, acquiring raw waveform signals from multiple (N) sensors monitoring microseismic events. Here, i represents the microseismic event number, s represents the sensor number, and t represents time. It should be noted that filtering and noise reduction involves applying a bandpass filter (e.g., a Butterworth filter) to the original waveform signal to remove high-frequency noise and low-frequency drift that are irrelevant to the frequency range of the target microseismic event. Time window truncation involves extracting a fixed-length time window containing the main energy based on the arrival time of the seismic phase, thus obtaining the analysis waveform signal. Waveform normalization: To eliminate the influence of the magnitude of the microseismic event itself on the waveform shape comparison, the truncated waveform signal is normalized.
[0022] Step 102: Based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal, divide the data into multiple microseismic event clusters corresponding to different waveforms.
[0023] In some embodiments, in order to utilize the high similarity of waveforms, microseismic events with similar physical causes and propagation paths are automatically grouped. As an example, for any two microseismic events acquired by the same sensor, the cross-correlation coefficient of the waveform signals corresponding to the two microseismic events is calculated; the cross-correlation coefficients of each sensor corresponding to multiple microseismic events are fused to obtain a similarity measure of any two microseismic events; by comparing the similarity measure with a set cross-correlation threshold, multiple microseismic events are divided to obtain multiple microseismic event clusters corresponding to different waveforms.
[0024] For example, calculating the cross-correlation coefficient includes: for any two microseismic events i and j, calculating the waveform signal on the same sensor s. Cross-correlation coefficient after waveform normalization .in, The cross-correlation coefficient fusion includes fusing the cross-correlation coefficients calculated from all sensors to obtain the final similarity measure between microseismic events i and j. Typically, the average or weighted average is used, where: , This refers to the total number of sensors used in the calculation. Dividing multiple microseismic events involves setting a high cross-correlation threshold (e.g., ...). ).like Greater than If the events are in the same cluster, then microseismic events i and j are considered to belong to the same cluster. This method divides all microseismic events into K event clusters. The final output consists of K microseismic event clusters, for example: , .
[0025] Step 103: Based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, construct an overdetermined set of equations and solve the overdetermined set of equations to obtain a relative magnitude value with consistency within the microseismic event cluster.
[0026] In some embodiments, within the same microseismic event cluster, the propagation paths and site responses of all microseismic events are almost identical. Therefore, the amplitude differences between them are determined only by their own source strength (i.e., magnitude or energy). Thus, to achieve accurate estimation of the relative amplitude ratio, as an example, for any pair of microseismic events in each microseismic event cluster, the maximum amplitude of the unnormalized waveform signal of the pair is measured on each sensor to calculate the amplitude ratio of the pair. The average or median of the amplitude ratios of the pairs of microseismic events on all sensors is taken as the relative amplitude ratio of any pair of microseismic events. Based on the proportional relationship between the magnitude difference between the two microseismic events in the pair and the logarithm of the relative amplitude ratio, an overdetermined system of equations is constructed. The overdetermined system of equations is solved using the least squares method to obtain a relative magnitude value with internal consistency within the microseismic event cluster.
[0027] For example, in each microseismic event cluster Internally, for all microseismic events Measure their relative amplitude ratio. Calculate the relative amplitude ratio: for microseismic events... On each sensor s, its unnormalized waveform signal is measured. Maximum amplitude and Calculate the amplitude ratio. To obtain a stable and reliable amplitude ratio... The average or median of the amplitude ratios of microseismic event pairs across all sensors is taken as the relative amplitude ratio for any pair of microseismic events. Where the median is: .
[0028] Furthermore, constructing the overdetermined equations involves the following: According to the standard definition of magnitude, the magnitude difference between two micro-events is proportional to the logarithm of their amplitude ratio. For each pair of micro-events within a micro-event cluster... A linear equation can be established: In a microseismic event cluster containing m microseismic events, a maximum of [number] microseismic events can be established. These equations constitute an overdetermined system of equations: In a microseismic event cluster containing m microseismic events, a maximum of [number] microseismic events can be established. There are several such equations. These equations can be written as a large matrix system. Its form is as follows:
[0029] In this matrix, the large matrix on the left is the coefficient matrix A, the column vector in the middle is the relative magnitude M to be solved (magnitude difference), and the column vector on the right is the observed data b.
[0030] Solving overdetermined equations using the least squares method includes finding an optimal set of relative magnitudes. , After solving, a set of internally consistent relative magnitude values for all microseismic events within the microseismic event cluster is obtained.
[0031] Step 104: Unify the relative magnitude values of each microseismic event cluster onto the absolute magnitude scale of a benchmark microseismic event, and convert them into microseismic energy.
[0032] In some embodiments, in order to assess and compare the microseismic energy of different microseismic events within a mine and provide a scientific basis for safe production in the mine, as an example, a benchmark microseismic event is determined in each microseismic event cluster. The benchmark microseismic event is selected based on the signal-to-noise ratio and recording integrity of each microseismic event in each cluster. Based on the benchmark microseismic event, the relative magnitude value of the corresponding microseismic event cluster is shifted onto an absolute magnitude scale to obtain the shifted target relative magnitude value. According to the empirical relationship between the target relative magnitude value and the corresponding microseismic energy of the microseismic event, the target relative magnitude value is converted into microseismic energy.
[0033] For example, in each microseismic event cluster In the process, a microseismic event with the highest signal-to-noise ratio and the best recording quality is selected as the benchmark microseismic event. And determine its absolute magnitude. Using benchmark microseismic events, the entire microseismic event cluster is... The relative magnitude value is shifted onto the absolute magnitude scale. For For any microseismic event i within the region, its corrected target relative magnitude value for: Finally, based on the empirical relationship between the target relative magnitude and the microseismic energy corresponding to the microseismic event (such as the Gutenberg-Richter relationship), the corrected target relative magnitude is... Convert into energy , ,in and This is an empirical constant.
[0034] The microseismic energy correction method of this invention acquires waveform signals corresponding to multiple microseismic events; based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal, it divides multiple microseismic event clusters corresponding to different waveforms; based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, it constructs an overdetermined set of equations and solves the overdetermined set of equations to obtain relative magnitude values with internal consistency within the microseismic event cluster; it unifies the relative magnitude values of each microseismic event cluster onto an absolute magnitude scale of a benchmark microseismic event and converts them into microseismic energy. Therefore, by integrating multiple waveform signals, using the cross-correlation coefficients of the waveforms to cluster microseismic events, and performing relative correction within each cluster, the influence of propagation paths and site effects can be effectively eliminated, resulting in accurate microseismic energy and providing a more reliable basis for microseismic activity trend analysis and rockburst disaster early warning.
[0035] Figure 2This is a schematic diagram illustrating the principle of a microseismic energy correction method provided in an embodiment of the present invention. It includes a data preprocessing and clustering stage and a magnitude correction and output stage. The data preprocessing and clustering stage includes: acquiring original waveform signals corresponding to multiple microseismic events; preprocessing the original waveform signals to obtain waveform signals corresponding to multiple microseismic events, wherein the preprocessing includes: filtering and denoising, time window truncation, and waveform normalization. For any two microseismic events acquired through the same sensor, the cross-correlation coefficient of the waveform signals corresponding to the two microseismic events is calculated; the cross-correlation coefficients of the various sensors corresponding to multiple microseismic events are fused to obtain a similarity measure for any two microseismic events; by comparing the similarity measure with a set cross-correlation threshold, the multiple microseismic events are divided to obtain multiple microseismic event clusters corresponding to different waveforms. The magnitude correction and output stage includes: for any pair of micro-seismic events in each micro-seismic event cluster, measuring the maximum amplitude of the original waveform signal of the pair on each sensor to calculate the amplitude ratio of the pair; taking the average or median of the amplitude ratios of the pairs of micro-seismic events on all sensors as the relative amplitude ratio of any pair of micro-seismic events; constructing an overdetermined equation system based on the proportional relationship between the magnitude difference between two micro-seismic events in a pair and the logarithm of the relative amplitude ratio; solving the overdetermined equation system using the least squares method to obtain a relative magnitude value with internal consistency within the micro-seismic event cluster. A reference micro-seismic event is determined in each micro-seismic event cluster; the reference micro-seismic event is selected based on the signal-to-noise ratio and recording integrity of each micro-seismic event in each cluster; based on the reference micro-seismic event, the relative magnitude value of the corresponding micro-seismic event cluster is translated onto an absolute magnitude scale to obtain the translated target relative magnitude value; and the target relative magnitude value is converted into micro-seismic energy according to the empirical relationship between the target relative magnitude value and the energy corresponding to the micro-seismic event. Finally, the corrected microseismic energy sequences calculated from all microseismic event clusters are aggregated to form a final output that is time-continuous, highly accurate, and internally consistent. This microseismic energy sequence can provide a high-quality data foundation for subsequent disaster risk assessment, engineering stability analysis, and scientific research.
[0036] To achieve the above embodiments, the present invention also proposes a microseismic energy correction device.
[0037] Figure 3 This is a schematic diagram of a micro-vibration energy correction device provided in an embodiment of the present invention.
[0038] like Figure 3 As shown, the microseismic energy correction device 300 includes an acquisition module 301, a division module 302, a construction module 303, and a conversion module 304.
[0039] The system includes: an acquisition module 301 for acquiring waveform signals corresponding to multiple microseismic events; a division module 302 for dividing multiple microseismic event clusters corresponding to different waveforms based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal; a construction module 303 for constructing an overdetermined set of equations based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, and solving the overdetermined set of equations to obtain relative magnitude values with internal consistency within the microseismic event cluster; and a conversion module 304 for unifying the relative magnitude values of each microseismic event cluster onto an absolute magnitude scale of a benchmark microseismic event and converting them into microseismic energy.
[0040] Furthermore, in one possible implementation of this invention, the acquisition module 301 is specifically used to: acquire the original waveform signals corresponding to multiple microseismic events; preprocess the original waveform signals to obtain the waveform signals corresponding to multiple microseismic events, wherein the preprocessing includes: filtering and denoising, time window truncation, and waveform normalization.
[0041] Furthermore, in one possible implementation of this invention, the partitioning module 302 is specifically used for: calculating the cross-correlation coefficient of the waveforms corresponding to any two microseismic events acquired by the same sensor; fusing the cross-correlation coefficients of each sensor corresponding to the multiple microseismic events to obtain a similarity measure of any two microseismic events; and partitioning the multiple microseismic events by comparing the similarity measure with a set cross-correlation threshold to obtain multiple microseismic event clusters corresponding to different waveforms.
[0042] Further, in one possible implementation of this invention, the construction module 303 is specifically configured to: for any pair of microseismic events in each microseismic event cluster, measure the maximum amplitude of the unnormalized waveform signal of the pair of microseismic events on each sensor to calculate the amplitude ratio of the pair of microseismic events; take the average or median of the amplitude ratios of the pairs of microseismic events on all sensors as the relative amplitude ratio of any pair of microseismic events; construct an overdetermined system of equations based on the proportional relationship between the magnitude difference between the two microseismic events in the pair and the logarithm of the relative amplitude ratio; and solve the overdetermined system of equations using the least squares method to obtain a relative magnitude value with internal consistency within the microseismic event cluster.
[0043] Further, in one possible implementation of this invention, the conversion module 304 is specifically used for: determining a reference microseismic event in each microseismic event cluster; wherein the reference microseismic event is selected by considering the signal-to-noise ratio and recording integrity of each microseismic event in each microseismic event cluster; based on the reference microseismic event, shifting the relative magnitude value of the corresponding microseismic event cluster to an absolute magnitude scale to obtain the shifted target relative magnitude value; and converting the target relative magnitude value into microseismic energy according to the empirical relationship between the target relative magnitude value and the microseismic energy corresponding to the microseismic event.
[0044] The microseismic energy correction device of this invention acquires waveform signals corresponding to multiple microseismic events; based on the cross-correlation coefficients of the waveforms corresponding to each waveform signal, it divides multiple microseismic event clusters corresponding to different waveforms; based on the relative amplitude ratio and magnitude difference of any pair of microseismic events in each microseismic event cluster, it constructs an overdetermined system of equations and solves the overdetermined system of equations to obtain relative magnitude values with internal consistency within the microseismic event cluster; it unifies the relative magnitude values of each microseismic event cluster onto an absolute magnitude scale of a benchmark microseismic event and converts them into microseismic energy. Therefore, by integrating multiple waveform signals, using the cross-correlation coefficients of the waveforms to cluster microseismic events, and performing relative correction within each cluster, the influence of propagation paths and site effects can be effectively eliminated, resulting in accurate microseismic energy and providing a more reliable basis for microseismic activity trend analysis and rockburst disaster early warning.
[0045] To achieve the above embodiments, the present invention also proposes an electronic device, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the aforementioned method.
[0046] To implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the aforementioned method.
[0047] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0048] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0049] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.
[0050] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0051] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0052] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
[0053] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
[0054] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.
Claims
1. A microseismic energy calibration method, characterized in that, The method comprises: obtaining waveform signals corresponding to a plurality of microseismic events; dividing a plurality of microseismic event clusters corresponding to different waveforms according to cross-correlation coefficients of waveforms corresponding to each of the waveform signals; constructing an over-determined equation set according to relative amplitude ratios and magnitude differences of any microseismic event pair in each microseismic event cluster, and solving the over-determined equation set to obtain a relative magnitude value with internal consistency in the microseismic event cluster; unifying the relative magnitude values of each microseismic event cluster to an absolute magnitude scale of a reference microseismic event, and converting the relative magnitude values into microseismic energy.
2. The method of claim 1, wherein, The method comprises: obtaining waveform signals corresponding to a plurality of microseismic events; obtaining original waveform signals corresponding to a plurality of microseismic events; 3. The method of claim 1, wherein, preprocessing the original waveform signals to obtain waveform signals corresponding to a plurality of microseismic events, wherein the preprocessing comprises filtering and denoising, time window cutting, and waveform normalization. The method comprises: calculating cross-correlation coefficients of waveforms of waveform signals corresponding to any two microseismic events for any two microseismic events collected by the same sensor; fusing the cross-correlation coefficients of each sensor corresponding to the plurality of microseismic events to obtain a similarity measure of any two microseismic events; 4. The method of claim 2, wherein, dividing a plurality of microseismic events by comparing the similarity measure with a set cross-correlation threshold to obtain a plurality of microseismic event clusters corresponding to different waveforms. The method comprises: measuring the maximum amplitude of the waveform signals of the microseismic event pair without waveform normalization on each sensor for any microseismic event pair in each microseismic event cluster to calculate the amplitude ratio of the microseismic event pair; averaging or taking the median of the amplitude ratios of the microseismic event pair on all sensors to obtain the relative amplitude ratio of any microseismic event pair; constructing an over-determined equation set based on the proportional relationship between the logarithms of the magnitude difference and the relative amplitude ratio of the two microseismic events in the microseismic event pair; 5. The method of claim 1, wherein, solving the over-determined equation set using the least squares method to obtain a relative magnitude value with internal consistency in the microseismic event cluster. The method comprises: determining a reference microseismic event in each microseismic event cluster, wherein the reference microseismic event is selected based on the signal-to-noise ratio and recording integrity of each microseismic event in the microseismic event cluster; translating the relative magnitude value of the corresponding microseismic event cluster to an absolute magnitude scale based on the reference microseismic event to obtain a target relative magnitude value after translation; 6. A microseismic energy correction device, characterized in that, converting the target relative magnitude value to microseismic energy according to an empirical relationship between the target relative magnitude value and the microseismic energy corresponding to the microseismic event. The device comprises: an acquisition module configured to obtain waveform signals corresponding to a plurality of microseismic events; a division module configured to divide a plurality of microseismic event clusters corresponding to different waveforms according to cross-correlation coefficients of waveforms corresponding to each of the waveform signals; The constructing module is configured to construct an over-determined equation set according to the relative amplitude ratio and the magnitude difference of any microseismic event pair in each microseismic event cluster, and solve the over-determined equation set to obtain a relative magnitude value with internal consistency in the microseismic event cluster; The converting module is configured to unify the relative magnitude value of each microseismic event cluster to an absolute magnitude scale of a reference microseismic event, and convert the relative magnitude value into microseismic energy.
7. The apparatus of claim 6, wherein, The obtaining module is specifically configured to: Obtain original waveform signals corresponding to a plurality of microseismic events; Preprocess the original waveform signals to obtain waveform signals corresponding to the plurality of microseismic events, wherein the preprocessing includes filtering and denoising, time window cutting, and waveform normalization.
8. The apparatus of claim 6, wherein, The dividing module is specifically configured to: For any two microseismic events collected by the same sensor, calculate a cross-correlation coefficient of waveforms of the waveform signals corresponding to the two microseismic events; Fuse the cross-correlation coefficients of the sensors corresponding to the plurality of microseismic events to obtain a similarity measure of the two microseismic events; Divide the plurality of microseismic events by comparing the similarity measure with a set cross-correlation threshold to obtain a plurality of microseismic event clusters corresponding to different waveforms.
9. The apparatus of claim 7, wherein, The constructing module is specifically configured to: For any microseismic event pair in each microseismic event cluster, measure a maximum amplitude of the waveform signal of the microseismic event pair without waveform normalization on each sensor to calculate an amplitude ratio of the microseismic event pair; Average or take a median value of the amplitude ratios of the microseismic event pairs on all sensors as a relative amplitude ratio of any microseismic event pair; Based on a proportional relationship between the logarithms of the magnitude difference and the relative amplitude ratio of the microseismic event pair, construct an over-determined equation set; Solve the over-determined equation set using a least square method to obtain a relative magnitude value with internal consistency in the microseismic event cluster.
10. The apparatus of claim 6, wherein, The converting module is specifically configured to: Determine a reference microseismic event in each microseismic event cluster, wherein the reference microseismic event is obtained by selecting each microseismic event in each microseismic event cluster according to a signal-to-noise ratio and recording integrity; Based on the reference microseismic event, translate the relative magnitude value of the corresponding microseismic event cluster to an absolute magnitude scale to obtain a target relative magnitude value after translation; Convert the target relative magnitude value to microseismic energy according to an empirical relationship between the target relative magnitude value and the microseismic energy corresponding to the microseismic event.