Mine micro-seismic waveform P wave arrival time pickup method, device, equipment and medium
By generating target feature curves and gradient calculations, the P wave initial arrival point is determined, which solves the problems of large errors in P wave initial arrival point detection and insufficient real-time detection in the mining environment, and achieves high accuracy and real-time P wave arrival pickup.
Patent Information
- Application Number
- CN202510581702.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-07
- Publication Date
- 2025-07-08
AI Technical Summary
The existing microseismic monitoring system has problems such as large P-wave initial point detection error, high error detection rate, and high calculation complexity in the mining environment, which cannot meet the real-time early warning requirements.
The target characteristic curve is generated using the preset initial arrival detection rules, and the candidate points for the initial arrival position of the P wave are determined through AIC value and gradient calculation. Combined with the energy ratio verification, the actual P wave initial arrival point is corrected and output.
It improves the accuracy and real-timeness of the P-wave initial point, reduces the source positioning error, and meets the real-time processing needs of multi-nodes in the mine.
Smart Images
Figure CN120276018A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of microseismic monitoring, and particularly relates to a method, device, equipment and medium for picking up the arrival time of P-wave of microseismic waveforms in mines. Background Art
[0002] In the fields of mine safety and rockburst monitoring, microseismic monitoring technology is the core means for warning geological disasters (such as rockbursts and collapses). It analyzes microseismic signals generated by the rupture of underground rock masses, locates the epicenter and assesses risks. The current mainstream microseismic monitoring systems rely on the accurate detection of the P-wave arrival time. However, existing methods have significant limitations in the complex mine environment: The traditional short-time average and long-time average ratio method requires manual experience to interpret the waveform arrival point, which is highly subjective and inefficient, and it is difficult to meet the real-time data processing requirements of large-scale sensor networks in mines; There are multi-source noises such as mechanical vibrations, blasting operations, and electromagnetic interferences in the mine environment. Fixed threshold algorithms (such as the energy ratio method) have poor adaptability to low signal-to-noise ratio signals, and the false detection rate is as high as over 30%. The traditional AIC algorithm uses a preset likelihood function and cannot accurately distinguish the P-wave arrival point from subsequent seismic phases, resulting in an epicenter location error exceeding 10 meters, seriously affecting the reliability of rockburst warning. Although the arrival time detection method based on artificial intelligence (such as deep learning) has high accuracy, its computational complexity is high, and the processing delay exceeds 5 seconds under typical mine hardware configurations, unable to meet the second-level warning requirements. Summary of the Invention
[0003] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for picking up the arrival time of P-wave of microseismic waveforms in mines, which can accurately distinguish the P-wave arrival point from subsequent seismic phases and reduce the epicenter location error. The specific solutions are as follows:
[0004] In the first aspect, the present application discloses a method for picking up the arrival time of P-wave of microseismic waveforms in mines, including:
[0005] Obtaining the original vibration waveform data of the target mine area recorded by microseismic sensors;
[0006] Generating a corresponding target feature curve based on the preset arrival time detection rule and the original vibration waveform data, and determining candidate points for the P-wave arrival position based on the target feature curve;
[0007] Correcting each candidate point for the P-wave arrival position according to the gradient calculation result of the target feature curve, and taking the corrected P-wave arrival point that passes the validity verification as the actual P-wave arrival point;
[0008] Converting the data index of the actual P-wave arrival point into an actual timestamp based on the vibration waveform data sampling frequency and the original timestamp to output the P-wave arrival time result.
[0009] Optionally, the step of generating a corresponding target feature curve by using a preset first arrival detection rule and based on the original vibration waveform data, and determining candidate points for the P-wave first arrival position based on the target feature curve includes:
[0010] Traverse each data point of the original vibration waveform data through a sliding window, calculate the AIC value of each segmentation point to generate a target feature curve; wherein, the segmentation points are partial data points selected under the sliding window constraint conditions;
[0011] Determine the peak points of the target feature curve as candidate points for the P-wave first arrival position.
[0012] Optionally, the step of calculating the AIC value of each segmentation point includes:
[0013] By Calculate the AIC value of each segmentation point; wherein, represents the AIC value of the th segmentation point, represents the total number of data points of the original vibration waveform data, represents the first data variance of all data points of the original vibration waveform data before the th segmentation point, is the second data variance of all data points of the original vibration waveform data after the th segmentation point.
[0014] Optionally, before calculating the AIC value of each segmentation point, it further includes:
[0015] By Calculate the first data variance;
[0016] By Calculate the second data variance;
[0017] Wherein, represents the data mean of all data points of the original vibration waveform data before the th segmentation point, represents the data mean of all data points of the original vibration waveform data after the th segmentation point, represents the th data point of the original vibration waveform data, represents the total number of data points of the original vibration waveform data.
[0018] Optionally, the step of determining the peak points of the target feature curve as candidate points for the P-wave first arrival position includes:
[0019] If the AIC value of the current curve point of the target characteristic curve is greater than the AIC value of the previous curve point, and the AIC value of the current curve point is greater than the AIC value of the next curve point, then determine that the current curve point is a peak point to obtain a candidate point for the P-wave arrival position;
[0020] If the AIC value of the current curve point of the target characteristic curve is equal to the AIC value of the previous curve point, and the AIC value of the current curve point is equal to the AIC value of the next curve point, then determine that the current curve point is located in a flat region, and use the last curve point in the flat region as the peak point to obtain a candidate point for the P-wave arrival position.
[0021] Optionally, the correcting each candidate point for the P-wave arrival position according to the gradient calculation result of the target characteristic curve includes:
[0022] Using the central difference method to calculate the gradient information of the target characteristic curve point by point to obtain the corresponding gradient calculation result;
[0023] Search backward from the candidate point for the P-wave arrival position by a preset time window to find the first candidate point for the P-wave arrival position whose gradient calculation result is less than a preset gradient threshold;
[0024] Search backward from the position of the first candidate point for the P-wave arrival position to the second candidate point for the P-wave arrival position where the gradient calculation results of two consecutive segmentation points are greater than zero, and determine the second candidate point for the P-wave arrival position as the corrected P-wave arrival point.
[0025] Optionally, the step of using the corrected P-wave arrival point that passes the validity verification as the actual P-wave arrival point includes:
[0026] Calculate the energy ratio between a preset number of data points before the corrected P-wave arrival point and a preset number of data points after the corrected P-wave arrival point;
[0027] If the energy ratio is greater than a preset energy ratio threshold, then determine that the corrected P-wave arrival point passes the validity verification.
[0028] In a second aspect, the present application discloses a device for picking the arrival time of P-waves in mine microseismic waveforms, including:
[0029] A data acquisition module, configured to acquire the original vibration waveform data of a target mine area recorded by a microseismic sensor;
[0030] A candidate point determination module, configured to generate a corresponding target characteristic curve by using a preset arrival time detection rule and based on the original vibration waveform data, and determine a candidate point for the P-wave arrival position based on the target characteristic curve;
[0031] A correction module, configured to correct each candidate P-wave arrival position according to the calculation result of the gradient of the target feature curve, and use the corrected P-wave arrival points that pass the validity verification as the actual P-wave arrival points;
[0032] A data conversion module, configured to convert the data index of the actual P-wave arrival point into an actual timestamp based on the vibration waveform data sampling frequency and the original timestamp, so as to output the P-wave arrival time result.
[0033] In a third aspect, the present application discloses an electronic device, including:
[0034] A memory, configured to store a computer program;
[0035] A processor, configured to execute the computer program to implement the steps of the foregoing disclosed method for picking the P-wave arrival time of mine microseismic waveforms.
[0036] In a fourth aspect, the present application discloses a computer-readable storage medium, configured to store a computer program; wherein, when the computer program is executed by a processor, the steps of the foregoing disclosed method for picking the P-wave arrival time of mine microseismic waveforms are implemented.
[0037] It can be seen that the present application discloses a method for picking the P-wave arrival time of mine microseismic waveforms, including: obtaining the original vibration waveform data of the target mine area recorded by a microseismic sensor; using a preset arrival detection rule and generating a corresponding target feature curve based on the original vibration waveform data, and determining candidate P-wave arrival positions based on the target feature curve; correcting each candidate P-wave arrival position according to the calculation result of the gradient of the target feature curve, and using the corrected P-wave arrival points that pass the validity verification as the actual P-wave arrival points; converting the data index of the actual P-wave arrival point into an actual timestamp based on the vibration waveform data sampling frequency and the original timestamp, so as to output the P-wave arrival time result. Thus, by using a preset arrival detection rule to perform arrival detection on the original vibration waveform data to obtain a corresponding target feature curve, and then determining candidate P-wave arrival positions from the curve information of the target feature curve, in this way, using the preset arrival detection rule can complete the preliminary screening of candidate P-wave arrival positions without manual experience intervention, meeting the real-time processing requirements of multiple nodes in the mine. Further, by gradient calculation and validity verification, the candidate P-wave arrival positions are further filtered to remove invalid arrival points, improving the accuracy of P-wave arrival time picking and meeting the real-time requirements. Description of the Drawings
[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the provided drawings.
[0039] Figure 1 Flow chart of a method for picking the arrival time of P-wave of mine microseismic waveform disclosed in this application;
[0040] Figure 2 Schematic diagram of the original vibration waveform data and the target characteristic curve disclosed in this application;
[0041] Figure 3 Schematic diagram of the peak points and valley points of the target characteristic curve disclosed in this application;
[0042] Figure 4 Schematic diagram of the candidate points for the initial arrival position of P-wave disclosed in this application;
[0043] Figure 5 Schematic diagram of the structure of a device for picking the arrival time of P-wave of mine microseismic waveform disclosed in this application;
[0044] Figure 6 Schematic diagram of the structure of an electronic device disclosed in this application. Detailed implementation manners
[0045] The following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
[0046] In the field of mine safety and rock burst monitoring, microseismic monitoring technology is the core means of warning geological disasters (such as rock bursts and collapses). It analyzes microseismic signals generated by the rupture of underground rock masses, locates the epicenter, and assesses risks. The current mainstream microseismic monitoring systems rely on the accurate detection of the P-wave arrival time. However, existing methods have significant limitations in the complex mine environment: The traditional short-time average and long-time average ratio method requires manual experience to interpret the waveform arrival point, which is highly subjective and inefficient, and it is difficult to meet the real-time data processing requirements of large-scale sensor networks in mines; there are multi-source noises such as mechanical vibrations, blasting operations, and electromagnetic interference in the mine environment. Fixed threshold algorithms (such as the energy ratio method) have poor adaptability to low signal-to-noise ratio signals, and the false detection rate is as high as over 30%. The traditional AIC algorithm uses a preset likelihood function and cannot accurately distinguish the P-wave arrival point from subsequent seismic phases, resulting in an epicenter location error exceeding 10 meters, seriously affecting the reliability of rock burst warnings. Although the arrival detection method based on artificial intelligence (such as deep learning) has high accuracy, its computational complexity is high, and the processing delay exceeds 5 seconds under typical mine hardware configurations, unable to meet the second-level warning requirements.
[0047] Therefore, the present invention provides a scheme for picking up the arrival time of the P-wave of mine microseismic waveforms, which can accurately distinguish the P-wave arrival point from subsequent seismic phases and reduce the epicenter location error.
[0048] Refer to Figure 1 As shown, an embodiment of the present invention discloses a method for picking up the arrival time of the P-wave of mine microseismic waveforms, including:
[0049] Step S11: Obtain the original vibration waveform data of the target mine area recorded by microseismic sensors.
[0050] In this embodiment, the microseismic activities of the underground mine are monitored in real time through microseismic sensors to obtain the original vibration waveform data of the target mine area, which is used to reflect the change of vibration velocity or acceleration over time.
[0051] Step S12: Generate a corresponding target feature curve based on the preset arrival detection rule and the original vibration waveform data, and determine the candidate points for the P-wave arrival position based on the target feature curve.
[0052] In this embodiment, each data point of the original vibration waveform data is traversed through a sliding window, and the AIC value of each segmentation point is calculated to generate a target feature curve; wherein, the segmentation point is a selected part of the data points under the sliding window constraint condition; the peak point of the target feature curve is determined as the candidate point of the P-wave arrival position. It can be understood that for the arrival detection based on the AIC criterion (Akaike Information Criterion), the AIC value of each segmentation point is calculated. Specifically, the AIC value of each segmentation point is obtained by calculating the log-likelihood function of the original vibration waveform data. The log-likelihood function of the original vibration waveform data is calculated as follows:
[0053] By calculating the AIC value of each segmentation point; wherein, is denoted as the AIC value of the th segmentation point, is denoted as the total number of data points of the original vibration waveform data, is denoted as the first data variance of all the data points of the original vibration waveform data before the th segmentation point, is the th segmentation point after all the data points of the original vibration waveform data. The second data variance. To ensure that there is a sufficient sliding window length, it should start calculating from the next data point of the sliding window length and loop to calculate up to (N - window length). For example: the sliding window length is taken as 100 and the total data point length is 3000, it should start calculating from the 101st point and end at the 2900th point. N - window length, that is, N minus the window length. In this embodiment, the window length is 100, so it loops through to 3000 - 100 = 2900.
[0054] Wherein, before calculating the AIC value of each segmentation point, it further includes:
[0055] By calculating the first data variance;
[0056] By calculating the second data variance;
[0057] Wherein, is denoted as the data mean of all the data points of the original vibration waveform data before the th segmentation point, is denoted as the data mean of all the data points of the original vibration waveform data after the th segmentation point, is denoted as the th data point of the original vibration waveform data, The total number of data points representing the original vibration waveform data.
[0058] By calculating the AIC value of each segmentation point as described above, a target feature curve is generated. As Figure 2 shown, the blue curve represents the original vibration waveform data, which represents the fluctuation of the vibration signal. The abscissa represents time, with the unit of ms, and the ordinate represents the vibration speed. Figure 2 Among them, the green curve is the target feature curve, the abscissa is the data sequence index, and the ordinate corresponding to the AIC curve is the AIC value.
[0059] In this embodiment, if the AIC value of the current curve point of the target feature curve is greater than the AIC value of the previous curve point and the AIC value of the current curve point is greater than the AIC value of the next curve point, then the current curve point is determined as the peak point to obtain the candidate point for the P-wave arrival position; if the AIC value of the current curve point of the target feature curve is equal to the AIC value of the previous curve point and the AIC value of the current curve point is equal to the AIC value of the next curve point, then it is determined that the current curve point is located in a flat region, and the last curve point in the flat region is used as the peak point to obtain the candidate point for the P-wave arrival position. It can be understood that starting from the second curve point of the target feature curve, traverse to the second-to-last point in turn (because adjacent points before and after need to be compared, so the starting point is the second current curve point). For the current curve point, it is a peak if it meets the following conditions:
[0060] ;
[0061] Among them, represents the data of the th curve point, refers to the data of the curve point after the th curve point, refers to the data of the curve point before the th curve point.
[0062] All the peak and trough points that meet the conditions are recorded for the next step of optimization. As follows Figure 3 , the blue is the original waveform data, the green is the target feature curve, and the red points are the peak and trough points.
[0063] If , then it is determined that the current curve point is located in a flat region, and the last curve point in the flat region is selected as the peak point.
[0064] For the peak values within adjacent 100 ms (according to the sampling frequency, if the sampling frequency is 500 Hz, 50 points are selected, and if the sampling frequency is 1000 Hz, 100 curve points are selected), the rest are filtered out.
[0065] Based on the above selection criteria, determine the candidate points for the P-wave arrival position, as Figure 4 shown.
[0066] Step S13: Modify each of the candidate points for the P-wave arrival position according to the gradient calculation result of the target characteristic curve, and use the modified P-wave arrival points that pass the validity verification as the actual P-wave arrival points.
[0067] In this embodiment, the central difference method is used to calculate the gradient information of the target characteristic curve at each segmentation point to obtain the corresponding gradient calculation result; from the candidate points for the P-wave arrival position, a preset time window is regressed backward, and the first candidate point for the P-wave arrival position with a gradient calculation result less than the preset gradient threshold is searched backward; from the position of the first candidate point for the P-wave arrival position, search backward to the second candidate point for the P-wave arrival position where the gradient calculation results of two consecutive segmentation points are greater than zero, and determine the second candidate point for the P-wave arrival position as the modified P-wave arrival point. It can be understood that the gradient of the above target characteristic curve is calculated, the corresponding gradient calculation result is obtained, and a corresponding gradient curve is formed based on each gradient calculation result; among them, the gradient calculation formula is as follows:
[0068] ;
[0069] Among them, represents the gradient calculation result of the th curve point. represents the AIC value at the curve point, represents the AIC value at the curve point, and here it is calculated point by point,
[0070] Take 1.
[0071] In this embodiment, calculate the energy ratio between a preset number of data points before the corrected P-wave arrival point and a preset number of data points after the corrected P-wave arrival point; if the energy ratio is greater than a preset energy ratio threshold, it is determined that the corrected P-wave arrival point passes the validity verification. It can be understood that after the search for the corrected P-wave arrival point is completed, it is necessary to further verify whether the corrected P-wave arrival point meets the waveform start condition. Using the energy ratio verification method, calculate the energy ratio of 300 data points before and after the corrected P-wave arrival point. It should be noted that 300 data points before and after the corrected P-wave arrival point refer to 300 data points before the determined corrected P-wave arrival point and 300 data points after it. The energy ratio verification formula is as follows:
[0072] ;
[0073] ;
[0074] Among them, refers to the energy value of the point data (corrected P-wave arrival point). Since energy is proportional to the square of velocity, here directly use the square of the point data to replace energy, which does not affect the calculation of the energy ratio.
[0075] The energy ratio is:
[0076] ;
[0077] When the energy ratio is greater than 2, it is considered that the corrected P-wave arrival point passes the validity verification, and the actual P-wave arrival point is obtained.
[0078] Step S14: Convert the data index of the actual P-wave arrival point into an actual timestamp based on the sampling frequency of the vibration waveform data and the original timestamp, so as to output the P-wave arrival time result.
[0079] In this embodiment, the calculated actual P-wave arrival point obtained above is a data index. It is necessary to convert the index number into a timestamp, and the final output result is a timestamp similar to 2025-03-05 19:51:55.343, where the last three digits are milliseconds. The index conversion timestamp formula is:
[0080] ;
[0081] Among them, represents the timestamp corresponding to the data index , represents the start time (referring to the timestamp of the first data of the original waveform data, such as 2025-03-05 19:51:55.343), Indicates the data index of the calculated P-wave starting position (counting starts from 1. If the index starts from 0, take ) Indicates the sampling frequency, in Hz.
[0082] It can be seen that the present application discloses a method for picking up the arrival time of P-wave in mine microseismic waveforms, including: obtaining the original vibration waveform data of the target mine area recorded by microseismic sensors; using a preset first arrival detection rule and generating a corresponding target characteristic curve based on the original vibration waveform data, and determining candidate points for the first arrival position of the P-wave based on the target characteristic curve; correcting each of the candidate points for the first arrival position of the P-wave according to the gradient calculation result of the target characteristic curve, and taking the corrected first arrival points of the P-wave that pass the validity verification as the actual first arrival points of the P-wave; converting the data index of the actual first arrival points of the P-wave into an actual time stamp based on the sampling frequency of the vibration waveform data and the original time stamp, so as to output the P-wave arrival time result. Thus, by using a preset first arrival detection rule to perform first arrival detection on the original vibration waveform data to obtain a corresponding target characteristic curve, and then determining candidate points for the first arrival position of the P-wave from the curve information of the target characteristic curve, in this way, using the preset first arrival detection rule can complete the preliminary screening of candidate points for the first arrival position of the P-wave without manual experience intervention, meeting the real-time processing requirements of multiple nodes in the mine. Further, by performing gradient calculation and validity verification to further filter the candidate points for the first arrival position of the P-wave, removing invalid first arrival points, improving the accuracy of picking up the P-wave arrival time, and meeting the real-time requirements.
[0083] Referring to Figure 5 as shown, the present invention also correspondingly discloses a device for picking up the arrival time of P-wave in mine microseismic waveforms, including:
[0084] A data acquisition module 11, configured to obtain the original vibration waveform data of the target mine area recorded by microseismic sensors;
[0085] A candidate point determination module 12, configured to use a preset first arrival detection rule and generate a corresponding target characteristic curve based on the original vibration waveform data, and determine candidate points for the first arrival position of the P-wave based on the target characteristic curve;
[0086] A correction module 13, configured to correct each of the candidate points for the first arrival position of the P-wave according to the gradient calculation result of the target characteristic curve, and take the corrected first arrival points of the P-wave that pass the validity verification as the actual first arrival points of the P-wave;
[0087] A data conversion module 14, configured to convert the data index of the actual first arrival points of the P-wave into an actual time stamp based on the sampling frequency of the vibration waveform data and the original time stamp, so as to output the P-wave arrival time result.
[0088] It can be seen that the present application discloses obtaining the original vibration waveform data of the target mine area recorded by microseismic sensors; generating a corresponding target characteristic curve by using a preset first arrival detection rule and based on the original vibration waveform data, and determining candidate P-wave first arrival positions based on the target characteristic curve; correcting each candidate P-wave first arrival position according to the gradient calculation result of the target characteristic curve, and taking the corrected P-wave first arrival points that pass the validity verification as the actual P-wave first arrival points; converting the data index of the actual P-wave first arrival points into actual timestamps based on the vibration waveform data sampling frequency and the original timestamps, so as to output the P-wave arrival time result. Thus, by using the preset first arrival detection rule to perform first arrival detection on the original vibration waveform data to obtain the corresponding target characteristic curve, and then determining the candidate P-wave first arrival positions from the curve information of the target characteristic curve, in this way, the preset first arrival detection rule can complete the preliminary screening of the candidate P-wave first arrival positions without manual experience intervention, meeting the real-time processing requirements of multiple nodes in the mine. Further, by gradient calculation and validity verification, the candidate P-wave first arrival positions are further filtered to remove invalid first arrival points, improving the accuracy of P-wave arrival time picking and meeting the real-time requirements.
[0089] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 6 which is a structural diagram of the electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be regarded as any limitation on the scope of use of the present application.
[0090] Figure 6 This is a schematic structural diagram of an electronic device 20 provided by an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the method for picking the P-wave arrival time of mine microseismic waveforms disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.
[0091] In this embodiment, the power supply 23 is used to provide working voltages for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and specific limitations are not imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application needs, and specific limitations are not imposed here.
[0092] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may further include an AI (Artificial Intelligence) processor, and the AI processor is used to process computational operations related to machine learning.
[0093] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a magnetic disk, an optical disc, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.
[0094] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21. It may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the method for picking the arrival time of the P wave of the mine microseismic waveform executed by the electronic device 20 disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program that can be used to complete other specific tasks. The data 223 may include not only the data transmitted by external devices received by the electronic device, but also the data collected by its own input / output interface 25, etc.
[0095] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the method for picking the arrival time of the P wave of the mine microseismic waveform disclosed above is realized. For the specific steps of this method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.
[0096] In the present specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.
[0097] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. The steps of the methods or algorithms described in combination with the embodiments disclosed in this article can be directly implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium well-known in the technical field.
[0098] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.
[0099] The above has introduced the solution provided by the present invention in detail. Specific examples are used herein to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.
Claims
1. A method for picking up the arrival time of P-wave in mine microseismic waveforms, characterized in that, Including: Obtaining the original vibration waveform data of the target mine area recorded by microseismic sensors; Using a preset first arrival detection rule and generating a corresponding target feature curve based on the original vibration waveform data, and determining candidate points for the P-wave first arrival position based on the target feature curve; Correcting each candidate point for the P-wave first arrival position according to the gradient calculation result of the target feature curve, and taking the corrected P-wave first arrival point that passes the validity verification as the actual P-wave first arrival point; Converting the data index of the actual P-wave first arrival point to the actual timestamp based on the vibration waveform data sampling frequency and the original timestamp, so as to output the P-wave arrival time result.
2. The method for picking up the arrival time of the P wave of the mine microseismic waveform according to claim 1, wherein The step of using a preset first arrival detection rule and generating a corresponding target feature curve based on the original vibration waveform data, and determining candidate points for the P-wave first arrival position based on the target feature curve includes: Traversing each data point of the original vibration waveform data through a sliding window, calculating the AIC value of each segmentation point to generate a target feature curve; wherein, the segmentation point is a selected part of data points under the sliding window constraint condition; Determining the peak point of the target feature curve as the candidate point for the P-wave first arrival position.
3. The method for picking the arrival time of the P-wave of the mine microseismic waveform according to claim 2, characterized in that The calculating the AIC value of each segmentation point includes: By calculating the AIC value of each segmentation point; wherein, is expressed as the AIC value of the th segmentation point, is expressed as the total number of data points of the original vibration waveform data, is expressed as the first data variance of all data points of the original vibration waveform data before the th segmentation point, is the second data variance of all data points of the original vibration waveform data after the th segmentation point.
4. The method for picking up the arrival time of the P wave of the mine microseismic waveform according to claim 3, characterized in that, Before calculating the AIC value of each segmentation point, it further includes: By calculating the variance of the first data; By calculating the variance of the second data; Among them, represents the data mean of all data points of the original vibration waveform data before the th segmentation point, represents the data mean of all data points of the original vibration waveform data after the th segmentation point, represents the th data point of the original vibration waveform data, represents the total number of data points of the original vibration waveform data.
5. The method for picking the arrival time of the P wave of the mine microseismic waveform according to claim 2, wherein The step of determining the peak point of the target feature curve as the candidate point for the P-wave first arrival position includes: If the AIC value of the current curve point of the target feature curve is greater than the AIC value of the previous curve point and the AIC value of the current curve point is greater than the AIC value of the subsequent curve point, then determine that the current curve point is the peak point to obtain the candidate point for the P-wave first arrival position; If the AIC value of the current curve point of the target feature curve is equal to the AIC value of the previous curve point and the AIC value of the current curve point is equal to the AIC value of the subsequent curve point, then determine that the current curve point is in a flat region, and take the last curve point in the flat region as the peak point to obtain the candidate point for the P-wave first arrival position.
6. The method for picking the arrival time of the P wave of the mine microseismic waveform according to any one of claims 1 to 5, characterized in that The step of correcting each candidate point for the P-wave first arrival position according to the gradient calculation result of the target feature curve includes: Using the central difference method to calculate the gradient information of the target feature curve point by point to obtain the corresponding gradient calculation result; Retreating a preset time window from the candidate point for the P-wave first arrival position and searching backward for the first candidate point for the P-wave first arrival position where the gradient calculation result is less than the preset gradient threshold; Searching backward from the position of the first candidate point for the P-wave first arrival position to the second candidate point for the P-wave first arrival position where the gradient calculation results of two consecutive segmentation points are greater than zero, and determining the second candidate point for the P-wave first arrival position as the corrected P-wave first arrival point.
7. The method for picking up the arrival time of the P wave of the mine microseismic waveform according to claim 6, wherein The step of taking the corrected P-wave first arrival point that passes the validity verification as the actual P-wave first arrival point includes: Calculating the energy ratio between a preset number of data points before the corrected P-wave first arrival point and a preset number of data points after the corrected P-wave first arrival point; If the energy ratio is greater than the preset energy ratio threshold, then determine that the corrected P-wave first arrival point passes the validity verification.
8. A device for picking up the arrival time of the P-wave of microseismic waveforms in a mine, characterized in that, Including: A data acquisition module, configured to acquire the original vibration waveform data of the target mine area recorded by microseismic sensors; A candidate point determination module, configured to generate a corresponding target feature curve based on the preset first arrival detection rule and the original vibration waveform data, and determine candidate points for the P-wave first arrival position based on the target feature curve; A correction module, configured to correct each of the candidate points for the P-wave first arrival position according to the gradient calculation result of the target feature curve, and use the corrected P-wave first arrival points that pass the validity verification as the actual P-wave first arrival points; A data conversion module, configured to convert the data index of the actual P-wave first arrival point into an actual timestamp based on the vibration waveform data sampling frequency and the original timestamp, so as to output the P-wave arrival time result.
9. An electronic device, characterized in that, including: A memory, configured to store a computer program; A processor, configured to execute the computer program to implement the steps of the method for picking the P-wave arrival time of the mine microseismic waveform according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, For storing a computer program; wherein, when the computer program is executed by the processor, the steps of the method for picking the P-wave arrival time of the mine microseismic waveform according to any one of claims 1 to 7 are implemented.