A microseismic signal processing method, system, device and storage medium
By using multi-channel signal characteristics design convolution kernel for noise reduction and reconstruction in microseismic signal processing, the problems of signal interruption and discontinuity in the prior art are solved, and efficient microseismic signal processing is achieved, ensuring the continuity of the signal and quality of analysis.
Patent Information
- Application Number
- CN202411534573.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2024-12-31
- Estimated Expiration
- 2044-10-31
AI Technical Summary
The existing microseismic signal processing methods cannot achieve effective noise reduction of continuous monitoring signals, resulting in signal interruption and discontinuity, affecting the quality of subsequent analysis.
By obtaining multi-channel initial continuous microseismic monitoring signals, extracting initial features and target features, designing target window functions and filters, automatically generating convolution kernels, performing convolution processing to reduce noise, and reconstructing signals through unilateral extension, restoring them Continuous signal.
It realizes accurate and efficient processing of micro-seismic signals, retains more effective information, and ensures signal continuity and integrity while reducing noise.
Smart Images
Figure CN119046672B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of signal processing technology, and in particular to a microseismic signal processing method, system, device and storage medium. Background Art
[0002] Microseismic monitoring technology is an important means of monitoring mining dynamic disasters, engineering structure damage, slope stability and other fields. The commonly used method is to collect vibration signals in real time and continuously through microseismic acquisition instruments, extract microseismic signals from vibration signals and analyze them to obtain the desired processing results. However, the mining application environment is complex. For example, manual operations, machinery use, electrical fluctuations, etc. will interfere with the signal waveform. The existing noise reduction work is mostly to intercept the microseismic waveform into time series segments through the threshold value of the microseismic signal amplitude, and then filter and reduce noise for a given period of data. It is just a static input-calculation-output process, which discards a lot of information in the original signal and interrupts the time series to varying degrees. Even adjacent time segments are difficult to reconnect after segmentation and noise reduction, which will cause some signals to be interrupted and discontinuous, resulting in a decrease in the quality of subsequent microseismic signal analysis. Summary of the invention
[0003] In view of this, the purpose of the present invention is to solve the problem that the existing methods cannot achieve noise reduction of continuous mine microseismic monitoring signals, and to provide a microseismic signal processing method, system, equipment and storage medium to achieve accurate and efficient processing of microseismic signals.
[0004] The present invention provides the following technical solutions:
[0005] In a first aspect, the present application provides a microseismic signal processing method, comprising:
[0006] Acquire a preset multi-channel initial continuous microseismic monitoring signal, extract initial features of the initial continuous microseismic monitoring signal, and determine target features;
[0007] According to the initial features and the target features, design a corresponding target window function and filter;
[0008] According to the target window function and the filter, automatically generate a corresponding target convolution kernel;
[0009] Inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and outputting a multi-channel noise reduction signal;
[0010] Multi-channel data reconstruction is performed on the multi-channel noise reduction signal to obtain a target continuous microseismic signal.
[0011] In one implementation, automatically generating a corresponding target convolution kernel according to the target window function and the filter includes:
[0012] Determining a convolution kernel coefficient of the target convolution kernel according to the target window function and the filter;
[0013] According to the convolution kernel coefficient, the existing convolution kernel is defined to obtain the target convolution kernel.
[0014] In one implementation, the convolution kernel coefficient of the target convolution kernel is:
[0015]
[0016]
[0017] in, is the window function, is the impulse response of the denoising filter, and is the cutoff frequency, is the sampling rate, is the length of the filter, and is the transition bandwidth, A is the db cutoff attenuation, is the selection window function, is the filter coefficient.
[0018] In one embodiment, the inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel and outputting a multi-channel noise reduction signal comprises:
[0019] Inputting the initial continuous microseismic monitoring signal of each channel in the preset multi-channel into the corresponding serial convolution kernel in the target convolution kernel;
[0020] The initial continuous microseismic monitoring signal is processed by a fast convolution method preset in each of the serial convolution kernels to obtain the multi-channel noise reduction signal.
[0021] In one embodiment, the initial continuous microseismic monitoring signal is processed by a fast convolution method preset in each of the serial convolution kernels to obtain the multi-channel noise reduction signal, including:
[0022] Converting the initial continuous microseismic monitoring signal and the serial convolution kernel into vector representations respectively to obtain an input signal vector and a convolution kernel vector;
[0023] The length of the input signal vector and the length of the convolution kernel vector are respectively extended to the target length to obtain a target signal vector and a target convolution kernel vector;
[0024] Performing fast Fourier transform on the target signal vector and the target convolution kernel vector respectively to obtain a target signal frequency domain vector and a target convolution kernel frequency domain vector;
[0025] The target signal frequency domain vector and the corresponding target convolution kernel frequency domain vector are multiplied in sequence to obtain the multi-channel noise reduction signal.
[0026] In one implementation, the step of reconstructing multi-channel data of the multi-channel noise reduction signal includes:
[0027] Compensating for the signal group delay of the multi-channel noise reduction signal by unilateral extension and overlap;
[0028] A reconstruction function is determined according to the number of points of the unilateral extension overlap corresponding to the signal group delay, and multi-channel data of the multi-channel noise reduction signal is reconstructed by the reconstruction function.
[0029] In one implementation, the confirmation formula of the reconstruction function is:
[0030]
[0031] in, is the number of points corresponding to the group delay of the unilateral extension overlap, is the length of the filter, is the reconstruction function.
[0032] In a second aspect, the present application provides a microseismic signal processing system, comprising:
[0033] An extraction module, used to obtain a preset multi-channel initial continuous microseismic monitoring signal, extract the initial features of the initial continuous microseismic monitoring signal, and determine the target features;
[0034] A selection module, used for designing a corresponding target window function and filter according to the initial features and the target features;
[0035] A generation module, used to automatically generate a corresponding target convolution kernel according to the target window function and the filter;
[0036] A processing module, used for inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and outputting a multi-channel noise reduction signal;
[0037] The reconstruction module is used to perform multi-channel data reconstruction on the multi-channel noise reduction signal to obtain a target microseismic signal.
[0038] In a third aspect, the present application provides an electronic device, comprising a memory and at least one processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the microseismic signal processing method as described in the first aspect.
[0039] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the microseismic signal processing method as described in the first aspect is implemented.
[0040] The embodiments of the present invention have the following beneficial effects:
[0041] The microseismic signal processing method provided by the present invention collects multi-channel initial microseismic monitoring signals, then selects a corresponding window function and determines a corresponding filter according to the characteristics of the initial microseismic monitoring signals and the target microseismic signals, then generates a corresponding convolution kernel according to the window function and the filter, uses the convolution kernel to realize noise reduction of the multi-channel initial microseismic monitoring signals, and then reconstructs the noise-reduced multi-channel noise-reduced signals to obtain the target signals. The present invention realizes rapid processing of continuous monitoring signals according to the characteristics of monitoring signals of different channels, and retains more effective information of microseismic monitoring data while reducing noise.
[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.
[0044] Figure 1 A schematic diagram of a flow chart of a microseismic signal processing method provided in an embodiment of the present application;
[0045] Figure 2 A schematic diagram of a process for obtaining a target convolution kernel provided in an embodiment of the present application;
[0046] Figure 3 A schematic diagram of a flow chart of a multi-channel noise reduction signal acquisition method provided in an embodiment of the present application;
[0047] Figure 4 A schematic diagram of a flow chart of a multi-channel data reconstruction method provided in an embodiment of the present application;
[0048] Figure 5 A schematic diagram of the framework structure of a microseismic signal processing system provided in an embodiment of the present application.
[0049] Description of main component symbols:
[0050] 500, microseismic signal processing system; 501, extraction module; 502, selection module; 503, generation module; 504, processing module; 505, reconstruction module. DETAILED DESCRIPTION
[0051] Embodiments of the present invention are described in detail below, examples of which 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 only used to explain the present invention, and cannot be understood as limiting the present invention.
[0052] 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, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.
[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of the template are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.
[0054] Example 1
[0055] See also Figure 1 , Figure 1 A schematic flow chart of a microseismic signal processing method provided in this embodiment, which can be used to process microseismic signals, includes:
[0056] S101, obtaining a preset multi-channel initial continuous microseismic monitoring signal, extracting initial features of the initial continuous microseismic monitoring signal, and determining target features.
[0057] Traditionally, the acquisition of microseismic monitoring signals usually involves cutting the microseismic waveform into time series segments through a threshold value of the microseismic signal amplitude, and then filtering and denoising a given segment of data. This is just a static input-calculation-output process that discards a large amount of information in the original signal and interrupts the time series to varying degrees. Even adjacent time segments are difficult to reassemble after segmentation and denoising, which will cause some signals to be interrupted and discontinuous, making it impossible to perform subsequent microseismic signal analysis.
[0058] In order to reduce the loss of information in the microseismic monitoring signal, the present application uses a microseismic signal acquisition instrument to obtain a multi-channel continuous initial continuous microseismic monitoring signal containing noise in real time. In order to extract the key information in the initial continuous microseismic signal, the target microseismic signal, that is, the target feature of the microseismic signal after noise reduction processing, can be determined first. According to the target feature, the initial feature of the initial continuous microseismic signal to be extracted is determined, so that the initial feature corresponds to the target feature, so that after the initial feature is subjected to noise reduction processing, the target feature obtained is exactly the target feature that the user wants. The target feature can be obtained from historical data, or obtained by simulated noise reduction based on the initial microseismic signal. The simulated noise reduction process is usually an idealized process.
[0059] Before sampling the microseismic monitoring signal, you can set the corresponding parameters, such as sampling rate, stopband attenuation (dB), transition bandwidth (Hz), response type, low-pass / stop cutoff frequency, window type, calculation window beta value, calculation of minimum window length N = order + 1, etc. This is convenient for calculating the convolution kernel coefficient based on these parameters. The response type can be selected as: low-pass, high-pass, band-pass, band-stop, multi-pass, multi-stop and offset. The window type can be selected as: Hanning window, Kaiser window, Hamming window, Blackman window and rectangular window.
[0060] Window Function It is used to weight the ideal impulse response to reduce the sidelobe level. Taking the Kaiser window calculation as an example, the Kaiser window calculation formula is:
[0061]
[0062] in, is the zero-order modified Bessel function, is the parameter of the Kaiser window. The beta value of the Kaiser window can be determined according to the required cutoff attenuation A. The approximate formula is:
[0063] = .
[0064] Where A is the stopband attenuation in dB. The window length N is usually set to the order of the filter plus 1, that is, N = order + 1. The choice of window length depends on the requirements of transition bandwidth and stopband attenuation.
[0065] The convolution kernel parameters are set as follows: sampling rate is 6000, stopband attenuation is 20, transition bandwidth is 10, corresponding type is low-pass, low-pass blocking frequency is 50, and high-frequency blocking frequency is 600.
[0066] S102: Design a corresponding target window function and filter according to the initial features and the target features.
[0067] The main mathematical tool for digital signal processing is Fourier transform. The Fourier transform studies the relationship between the entire time domain and the frequency domain. However, when using computers to implement engineering test signal processing, it is impossible to measure and calculate infinitely long signals, but to analyze them in finite time segments. The method is to cut a time segment from the signal, and then use the cut signal time segment to perform periodic extension processing to obtain a virtual infinitely long signal, and then the signal can be subjected to mathematical processing such as Fourier transform and correlation analysis. After the infinitely long signal is cut off, its spectrum is distorted, and the energy originally concentrated at f(0) is dispersed into two wider frequency bands. This phenomenon is called spectrum energy leakage.
[0068] In order to reduce spectrum energy leakage, different truncation functions can be used to truncate the signal. The truncation function is called a window function. Common window functions include rectangular window, Hanning window, Hamming window, etc. These window functions improve the accuracy and precision of spectrum analysis by reducing spectrum leakage.
[0069] Since the collected initial continuous microseismic monitoring signals are multi-channel, the corresponding initial features of initial continuous microseismic monitoring signals with different characteristics or types may be different. Therefore, different target window functions and filters can be designed for processing according to the initial characteristics or requirements.
[0070] S103. Automatically generate a corresponding target convolution kernel according to the target window function and the filter.
[0071] In order to achieve accurate processing of the initial microseismic monitoring signal, a finite impulse response filter, referred to as FIR digital filter, can be selected as the initial filter. In deep learning, the concepts of filter and convolution kernel are often used interchangeably. A filter is actually a set of multiple convolution kernels, each of which corresponds to a channel of the input image and is used to extract specific features. By using different convolution kernels, different features of the image, such as edges, textures, etc., can be extracted.
[0072] See also Figure 2 , step S103 includes:
[0073] S1031. Determine the convolution kernel coefficient of the target convolution kernel according to the target window function and the filter.
[0074] S1032. Define the existing convolution kernel according to the convolution kernel coefficient to obtain the target convolution kernel.
[0075] The convolution kernel coefficient of the target convolution kernel is:
[0076]
[0077]
[0078] in, is the window function, is the impulse response of the denoising filter, and is the cutoff frequency, is the sampling rate, is the length of the filter, and is the transition bandwidth, A is the db cutoff attenuation, is the selection window function, is the filter coefficient. Among them, db cutoff attenuation usually means that a parameter or factor in the data has reached its corresponding cutoff value. This value represents a critical state. Exceeding this value may affect the performance of the system or cause system failure.
[0079] After the initial filter is determined, the convolution kernel coefficient of the target convolution kernel is determined according to the target window function and the filter. A filter is a collection of multiple convolution kernels. Therefore, the convolution kernel coefficient represents the number of convolution kernels, the connection relationship, etc., so as to obtain the target convolution kernel. Since convolution operations are basically performed through convolution kernels, when performing noise reduction processing on the initial continuous microseismic monitoring signals of different channels, it is only necessary to input the initial continuous microseismic monitoring signals of the channel into the corresponding target convolution kernel. The noise reduction filter calculation service is a multi-core multi-threaded parallel mode. Each channel of microseismic data stream corresponds to a noise reduction thread, and threads can be shared at the same time.
[0080] S104: input the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and output a multi-channel noise reduction signal.
[0081] See also Figure 3 , step S104 includes:
[0082] S1041. Input the initial continuous microseismic monitoring signal of each channel in the preset multi-channel into the corresponding serial convolution kernel in the target convolution kernel.
[0083] S1042. Process the initial continuous microseismic monitoring signal by using the fast convolution method preset in each of the serial convolution kernels to obtain the multi-channel noise reduction signal.
[0084] In order to make the initial continuous microseismic monitoring signal meet the requirements of fast Fourier transform, it is first necessary to convert the initial continuous microseismic monitoring signal and the serial convolution kernel into vector representations respectively to obtain an input signal vector and a convolution kernel vector.
[0085] Then, the length of the input signal vector and the length of the convolution kernel vector are respectively extended to the target length to obtain the target signal vector and the target convolution kernel vector. The multiple of the equal length extension is 2 n , where n is a positive integer.
[0086] Then, the target signal vector and the target convolution kernel vector are respectively subjected to fast Fourier transform to obtain a target signal frequency domain vector and a target convolution kernel frequency domain vector.
[0087] Since the Fourier transform studies the relationship between the entire time domain and the frequency domain, and the microseismic continuous monitoring signal is usually a representation of a time series, it is necessary to convert both the target signal vector and the target convolution kernel vector into a representation of a frequency domain vector. Since the target convolution kernel is already determined, in order to improve the accuracy of the calculation, it may usually be necessary to perform multiple convolution calculations on the target signal vector. In order to reduce the amount of calculation, the target convolution kernel frequency domain vector can be obtained and saved. In this way, when performing multiple convolution calculations, only one frequency domain vector calculation of the target convolution kernel is required.
[0088] Finally, the target signal frequency domain vector and the corresponding target convolution kernel frequency domain vector are multiplied in sequence to obtain the multi-channel noise reduction signal. That is, the target signal frequency domain vector of each channel is multiplied by its corresponding target convolution kernel frequency domain vector to obtain the noise reduction signal corresponding to the target signal frequency domain vector of the channel. After the target signal frequency domain vectors of multiple channels are calculated accordingly, the convolution calculation is completed.
[0089] S105 , performing multi-channel data reconstruction on the multi-channel noise reduction signal to obtain a target microseismic signal.
[0090] See also Figure 4 , step S105 includes:
[0091] S1051. Compensate for the signal group delay of the multi-channel noise reduction signal by unilateral extension and overlap.
[0092] S1052: Determine a reconstruction function according to the number of points of the unilateral extension overlap corresponding to the signal group delay, and reconstruct multi-channel data of the multi-channel noise reduction signal by using the reconstruction function.
[0093] The confirmation formula of the reconstruction function is:
[0094]
[0095] in, is the number of points corresponding to the group delay of the unilateral extension overlap, is the length of the filter, is the reconstruction function.
[0096] Single-side extension is a signal processing technique that is mainly used to improve the boundary effect of the signal. In signal processing, especially in the design of digital filters, traditional filtering methods (such as Matlab's filter function) may cause signal delay, and the degree of this delay is related to the order of the filter. To solve this problem, Matlab provides the filtfilt function, which is often called zero-phase filtering or bidirectional filtering. The filtfilt function processes the boundary of the signal through a technique called single-side extension to reduce or eliminate the phase deviation and boundary effect caused by filtering.
[0097] Signal group delay is an important concept in signal processing. It describes the delay of the amplitude envelope of each sinusoidal component of the signal passing through the device under test, and is a function of each frequency component. Group delay is the gradient of the phase characteristic, that is, a representation of the linear distortion of the system and network. It directly affects the phase of the signal, and thus affects the correctness of information transmission. In communication systems and networks, group delay is one of the key parameters to measure the degree of linear distortion of the system and network.
[0098] This embodiment collects multi-channel initial continuous microseismic monitoring signals, and then designs corresponding window functions and filters according to the characteristics of the initial continuous microseismic monitoring signals and target microseismic signals. Then, a corresponding convolution kernel is generated according to the window function and the filter, and the convolution kernel is used to realize noise reduction of the multi-channel initial continuous microseismic monitoring signals. Then, the noise-reduced multi-channel noise-reduced signals are reconstructed to obtain the target signal. The present invention realizes rapid processing of continuous monitoring signals according to the characteristics of monitoring signals of different channels, and retains more effective information of microseismic monitoring data while reducing noise.
[0099] Example 2
[0100] Please refer to Figure 5 , Figure 5 A microseismic signal processing system 500 provided in this embodiment includes:
[0101] The extraction module 501 is used to obtain a preset multi-channel initial continuous microseismic monitoring signal, extract the initial features of the initial continuous microseismic monitoring signal, and determine the target features.
[0102] The selection module 502 is used to design a corresponding target window function and filter according to the initial feature and the target feature.
[0103] The generating module 503 is used to automatically generate a corresponding target convolution kernel according to the target window function and the filter.
[0104] The processing module 504 is used to input the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and output a multi-channel noise reduction signal.
[0105] The reconstruction module 505 is used to perform multi-channel data reconstruction on the multi-channel noise reduction signal to obtain a target microseismic signal.
[0106] It can be understood that the implementation of the microseismic signal processing method described in the first embodiment is also applicable to this embodiment, so it will not be described again here.
[0107] Example 3
[0108] An embodiment of the present application further provides a server, which includes a memory and at least one processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the microseismic signal processing method of the above-mentioned embodiment 1.
[0109] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. At least one of the general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.
[0110] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.
[0111] Furthermore, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data (such as iteration data, version data, etc.) created according to the use of the computer device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0112] Example 4
[0113] The embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the microseismic signal processing method in the above-mentioned embodiment 1.
[0114] It can be understood that the implementation of the microseismic signal processing method in the above-mentioned embodiment 1 is also applicable to this embodiment, so it will not be described repeatedly here.
[0115] The computer-readable storage medium may be either a non-volatile storage medium or a volatile storage medium. For example, the computer-readable storage medium may include, but is not limited to, a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media that can store program codes.
[0116] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus embodiments described above are merely schematic, for example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and a module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.
[0117] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
[0118] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.
[0119] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.
[0120] In all examples shown and described herein, any specific values should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.
[0121] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.
[0122] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention.
Claims
1. A microseismic signal processing method, characterized in that: include: Acquire a preset multi-channel initial continuous microseismic monitoring signal, extract initial features of the initial continuous microseismic monitoring signal, and determine target features; According to the initial features and the target features, design a corresponding target window function and filter; Determining a convolution kernel coefficient of a target convolution kernel according to the target window function and the filter; According to the convolution kernel coefficient, the existing convolution kernel is defined to obtain the target convolution kernel; The convolution kernel coefficient of the target convolution kernel is: in, is the window function, is the impulse response of the denoising filter, and is the cutoff frequency, is the length of the filter, and is the transition bandwidth, A is the db cutoff attenuation, is the selection window function, is the filter coefficient; Inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and outputting a multi-channel noise reduction signal; Multi-channel data reconstruction is performed on the multi-channel noise reduction signal to obtain a target continuous microseismic signal.
2. The microseismic signal processing method according to claim 1, characterized in that: The step of inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel and outputting a multi-channel noise reduction signal comprises: Inputting the initial continuous microseismic monitoring signal of each channel in the preset multi-channel into the corresponding serial convolution kernel in the target convolution kernel; The initial continuous microseismic monitoring signal is processed by a fast convolution method preset in each of the serial convolution kernels to obtain the multi-channel noise reduction signal.
3. The microseismic signal processing method according to claim 2, characterized in that: The method of processing the initial continuous microseismic monitoring signal by using the fast convolution method preset in each of the serial convolution kernels to obtain the multi-channel noise reduction signal includes: Converting the initial continuous microseismic monitoring signal and the serial convolution kernel into vector representations respectively to obtain an input signal vector and a convolution kernel vector; The length of the input signal vector and the length of the convolution kernel vector are respectively extended to the target length to obtain a target signal vector and a target convolution kernel vector; Performing fast Fourier transform on the target signal vector and the target convolution kernel vector respectively to obtain a target signal frequency domain vector and a target convolution kernel frequency domain vector; The target signal frequency domain vector and the corresponding target convolution kernel frequency domain vector are multiplied in sequence to obtain the multi-channel noise reduction signal.
4. The microseismic signal processing method according to claim 1, characterized in that: The performing multi-channel data reconstruction on the multi-channel noise reduction signal comprises: Compensating for the signal group delay of the multi-channel noise reduction signal by unilateral extension and overlap; A reconstruction function is determined according to the number of points of the unilateral extension overlap corresponding to the signal group delay, and multi-channel data of the multi-channel noise reduction signal is reconstructed by the reconstruction function.
5. The microseismic signal processing method according to claim 4, characterized in that: The confirmation formula of the reconstruction function is: in, is the number of points corresponding to the group delay of the unilateral extension overlap, is the length of the filter, is the reconstruction function.
6. A microseismic signal processing system, characterized in that: include: An extraction module, used to obtain a preset multi-channel initial continuous microseismic monitoring signal, extract the initial features of the initial continuous microseismic monitoring signal, and determine the target features; A selection module, used for designing a corresponding target window function and filter according to the initial features and the target features; A generating module, used for determining a convolution kernel coefficient of a target convolution kernel according to the target window function and the filter; According to the convolution kernel coefficient, the existing convolution kernel is defined to obtain the target convolution kernel; The convolution kernel coefficient of the target convolution kernel is: in, is the window function, is the impulse response of the denoising filter, and is the cutoff frequency, is the length of the filter, and is the transition bandwidth, A is the db cutoff attenuation, is the selection window function, is the filter coefficient; A processing module, used for inputting the preset multi-channel initial continuous microseismic monitoring signal into the target convolution kernel, and outputting a multi-channel noise reduction signal; The reconstruction module is used to perform multi-channel data reconstruction on the multi-channel noise reduction signal to obtain a target microseismic signal.
7. An electronic device, characterized in that: The method comprises a memory and at least one processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the microseismic signal processing method according to any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the microseismic signal processing method according to any one of claims 1 to 5 is implemented.