Coal mine micro-seismic signal first arrival pickup method and device, electronic equipment and storage medium

Abstract

The invention provides a coal mine micro-seismic signal first arrival pickup method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a target micro-seismic signal corresponding to a coal mine micro-seismic event; for any sub-signal in the target micro-seismic signal, obtaining a target statistical value of any signal sampling point in the sub-signal on the target statistical magnitude, and obtaining a statistical value sequence corresponding to the sub-signal; based on the sub-signals and the corresponding statistical value sequences, any signal sampling point in the sub-signals is classified, and signal categories of the signal sampling points are obtained; and determining the first arrival time of the coal mine micro-seismic event based on the signal category of any signal sampling point in the sub-signals.

Description

Technical Field

[0001] This application relates to the technical field of coal mine monitoring, and particularly relates to a method, device, electronic device, and storage medium for picking the first arrival of coal mine microseismic signals. Background Art

[0002] In coal mining, microseismic signals (MS) are released when rocks are stressed, deformed, and fractured. Microseismic signals can reflect changes in the internal stress of rocks, the direction of energy propagation, and other states. By means of a sensor array arranged underground or around, the acquisition of microseismic signals can be realized. Subsequently, by using means such as spectrum analysis and time-frequency transformation to analyze the collected microseismic signals, researchers can deeply explore the microscopic processes such as the generation and expansion of cracks inside rocks.

[0003] Among them, the first arrival wave of microseismic waves carries a lot of key information. For example, the first arrival time reflects the occurrence time of rock fracture, the internal energy relationship of rock fracture, etc. Therefore, monitoring microseismic signals and picking the first arrival of microseismic signals are one of the keys to studying the rock fracture process. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related art to some extent.

[0005] To this end, the first object of this application is to propose a method for picking the first arrival of coal mine microseismic signals.

[0006] The second object of this application is to propose a device for picking the first arrival of coal mine microseismic signals.

[0007] The third object of this application is to propose an electronic device.

[0008] The fourth object of this application is to propose a computer-readable storage medium.

[0009] The fifth object of this application is to propose a computer program product.

[0010] To achieve the above object, the first aspect embodiment of this application proposes a method for picking the first arrival of coal mine microseismic signals, including:

[0011] Obtain the target microseismic signal corresponding to the coal mine microseismic event;

[0012] For any sub-signal in the target microseismic signal, obtain the target statistical value of any signal sampling point in the sub-signal on the target statistic, and obtain the statistical value sequence corresponding to the sub-signal;

[0013] Based on the sub-signal and the corresponding statistical value sequence, classify any signal sampling point in the sub-signal to obtain the signal category of the signal sampling point;

[0014] Determine the arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal.

[0015] To achieve the above object, an embodiment of the second aspect of the present application provides a device for picking the arrival time of coal mine microseismic signals, including:

[0016] A first acquisition module, configured to acquire a target microseismic signal corresponding to a coal mine microseismic event;

[0017] A second acquisition module, configured to, for any sub-signal in the target microseismic signal, acquire a target statistical value of any signal sampling point in the sub-signal on a target statistic, and obtain a statistical value sequence corresponding to the sub-signal;

[0018] A classification module, configured to classify any signal sampling point in the sub-signal based on the sub-signal and the corresponding statistical value sequence, and obtain the signal category of the signal sampling point;

[0019] A determination module, configured to determine the arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal.

[0020] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement a method for picking the arrival time of coal mine microseismic signals as described in the first aspect of the embodiments of the present application.

[0021] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement a method for picking the arrival time of coal mine microseismic signals as described in the first aspect of the embodiments of the present application.

[0022] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements a method for picking the arrival time of coal mine microseismic signals as described in the first aspect of the embodiments of the present application.

[0023] The technical solution provided by the present application at least brings the following beneficial effects:

[0024] This application obtains a target microseismic signal corresponding to a coal mine microseismic event; for any sub-signal in the target microseismic signal, it obtains the target statistical value of any signal sampling point in the sub-signal on the target statistic, and obtains a statistical value sequence corresponding to the sub-signal; based on the sub-signal and the corresponding statistical value sequence, it classifies any signal sampling point in the sub-signal to obtain the signal category of the signal sampling point; based on the signal category of any signal sampling point in the sub-signal, it determines the first arrival time of the coal mine microseismic event. Since the target statistic can capture the statistical correlation and non-linear characteristics of the higher-order target microseismic signal, the accuracy of determining the signal category based on the target statistic is relatively high, and thus the accuracy of first arrival picking based on the signal category is relatively high.

[0025] Additional aspects and advantages of this application will be given in part in the following description, will become apparent in part from the following description, or will be learned through the practice of this application. Description of the Drawings

[0026] The above and / or additional aspects and advantages of this application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0027] Figure 1 is a schematic flow chart of a method for picking the first arrival of a coal mine microseismic signal provided by an embodiment of this application;

[0028] Figure 2 is a schematic flow chart of a method for picking the first arrival of a coal mine microseismic signal provided by another embodiment of this application;

[0029] Figure 3 is a schematic structural diagram of a device for picking the first arrival of a coal mine microseismic signal provided by an embodiment of this application;

[0030] Figure 4 is a block diagram of an electronic device provided by an embodiment of this application. Detailed Embodiments

[0031] The embodiments of this application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the drawings are exemplary and are intended to explain this application and should not be construed as limiting this application.

[0032] The method, device, electronic device, and storage medium for picking the first arrival of a coal mine microseismic signal according to the embodiments of this application will be described below with reference to the drawings.

[0033] Figure 1 is a schematic flow chart of a method for picking the first arrival of a coal mine microseismic signal provided by an embodiment of this application.

[0034] As Figure 1 shown, the method for picking the first arrival of microseismic signals in the coal mine includes the following steps:

[0035] Step 101, obtain the target microseismic signal corresponding to the microseismic event in the coal mine.

[0036] In the embodiment of the present application, the target microseismic signal may refer to the monitored microseismic signal, or may refer to the signal obtained after preprocessing (such as normalization processing) of the monitored microseismic signal; wherein, the target microseismic signal includes multiple signal sampling points.

[0037] Step 102, for any sub-signal in the target microseismic signal, obtain the target statistical value of any signal sampling point in the sub-signal on the target statistic, and obtain the statistical value sequence corresponding to the sub-signal.

[0038] Among them, the target microseismic signal includes multiple sub-signals, and each sub-signal includes multiple signal sampling points. It should be noted that there may be overlap or no overlap between two adjacent sub-signals.

[0039] As an example, according to the set sliding step size, the set sliding window can be slid in the target microseismic signal, and a sub-signal is obtained each time it slides. For example, the sliding step size can be 1, and the window length of the set sliding window can be 40.

[0040] Among them, the target statistic includes at least one of the fifth-order statistic, skewness, and kurtosis. The number of the statistical value sequences corresponding to the sub-signals is the same as the number of the target statistics; assuming that the target statistics include the fifth-order statistic, skewness, and kurtosis, the statistical value sequences corresponding to the sub-signals include the fifth-order statistic sequence, skewness sequence, and kurtosis sequence.

[0041] As an example, for any signal sampling point in the sub-signal, based on the signal value corresponding to the signal sampling point and a certain number of signal sampling points on its left and right sides, obtain the target statistical value of the signal sampling point on the target statistic.

[0042] As another example, for any signal sampling point in the sub-signal, obtain the signal segment centered on the signal sampling point in the target microseismic signal by setting a sliding window; obtain the target statistical value of the signal sampling point on the target statistic based on the signal segment; for any target statistic, combine the target statistical values of each signal sampling point in the sub-signal on the target statistic to obtain the statistical value sequence.

[0043] Among them, each signal sampling point corresponds to a signal segment, and the target statistical value of the signal segment corresponding to each signal sampling point is calculated on the target statistic. It should be noted that due to the window sliding characteristic, the number of sampling points included in the signal segments corresponding to the signal sampling points at both ends of the target microseismic signal may be less than the window length of the set sliding window.

[0044] It should be noted that the sliding step and window length of the set sliding window for obtaining the signal segment and the set sliding window for obtaining the sub-signal can be the same or different.

[0045] Based on the target statistic, the statistical correlation and non-linear characteristics of the target microseismic signal of higher order in the signal can be captured, Gaussian noise in the signal can be suppressed, and the accuracy of subsequent signal classification can be improved.

[0046] Step 103: Classify any signal sampling point in the sub-signal based on the sub-signal and the corresponding statistical value sequence to obtain the signal category of the signal sampling point.

[0047] Among them, the signal category is used to distinguish the valid signal sampling points and the noise signal sampling points.

[0048] As an example, obtain a trained signal classification model; input the sub-signal and the corresponding statistical value sequence into the signal classification model to obtain the signal category output by the signal classification model for any signal sampling point in the sub-signal.

[0049] Among them, the signal classification model can refer to a convolutional neural network (Convolutional Neural Networks, CNN) based on a residual network (Residual Network, ResNet). It should be noted that the difference between the category label corresponding to the signal sampling point in the sample signal and the predicted signal category can be calculated according to the L2 loss function, and then the signal classification model can be trained based on the difference.

[0050] Through the CNN based on ResNet, the problems of gradient disappearance and gradient explosion occurring in the training process are effectively solved, enabling the network to effectively learn and extract features from the input sub-signal and statistical value sequence at a deeper level, enhancing the expression ability and generalization performance of the network, and then being able to accurately distinguish noise and valid signals for microseismic events generated under complex coal mine geological conditions.

[0051] In addition, classifying the signal sampling points based on the sub-signal and the statistical value sequence corresponding to the sub-signal can enable the signal classification model to more carefully capture the features of the signal, which is beneficial to improving the classification accuracy.

[0052] Step 104: Determine the first arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal.

[0053] As an example, remove the noise signals from the target microseismic signals based on the signal category, and retain the effective signals; determine the first arrival time of the coal mine microseismic event from the effective signals based on the probability that each signal sampling point in the effective signals belongs to the effective signals. For example, obtain the signal sampling point corresponding to the maximum probability, and determine the first arrival time based on the sampling time corresponding to the effective signal sampling point. Among them, after classifying any signal sampling point in the sub-signal in step 103, the probability that the signal sampling point belongs to the effective signal can be obtained.

[0054] In this embodiment, obtain the target microseismic signals corresponding to the coal mine microseismic events; for any sub-signal in the target microseismic signals, obtain the target statistical value of any signal sampling point in the sub-signal on the target statistic to obtain the statistical value sequence corresponding to the sub-signal; based on the sub-signal and the corresponding statistical value sequence, classify any signal sampling point in the sub-signal to obtain the signal category of the signal sampling point; determine the first arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal. Based on the target statistic, the statistical correlation and non-linear characteristics of the higher-order target microseismic signals can be captured. Therefore, the accuracy of determining the signal category based on the target statistic is relatively high, and thus the accuracy of picking the first arrival based on the signal category is relatively high. In addition, this application can also quickly and accurately perform the signal first arrival picking work for large-scale microseismic events, and the picking efficiency is relatively high.

[0055] This embodiment provides another method for picking the first arrival of coal mine microseismic signals. Figure 2 It is a schematic flow chart of a method for picking the first arrival of coal mine microseismic signals provided by an embodiment of this application.

[0056] As Figure 2 shown, the method for picking the first arrival of coal mine microseismic signals may include the following steps:

[0057] Step 201: Obtain the target microseismic signals corresponding to the coal mine microseismic events.

[0058] Step 202: For any sub-signal in the target microseismic signals, obtain the target statistical value of any signal sampling point in the sub-signal on the target statistic to obtain the statistical value sequence corresponding to the sub-signal.

[0059] Step 203: Based on the sub-signal and the corresponding statistical value sequence, classify any signal sampling point in the sub-signal to obtain the signal category of the signal sampling point.

[0060] Step 204: For any sub-signal, based on the signal category, remove the noise signal sampling points in the sub-signal to obtain the effective signal sampling points.

[0061] Among them, the signal categories corresponding to the effective signal sampling points and the noise signal sampling points are different. For example, the signal category of the effective signal sampling point is 1, and the signal category of the noise signal sampling point is 0.

[0062] As an example, the signal sampling points with a signal category of 0 in the sub-signal are removed, and the remaining signal sampling points are the effective signal sampling points.

[0063] Step 205: Obtain the sampling point scores corresponding to the effective signal sampling points and obtain the score threshold corresponding to the sub-signal.

[0064] Among them, the sampling point score is used to indicate the probability / possibility that the sampling time corresponding to the effective signal sampling point is the first arrival time; the score threshold is used to determine whether the sampling time corresponding to the effective signal sampling point is the first arrival time.

[0065] As an example, the sub-signal with the noise signal sampling points removed is input into the scoring model, and the sampling point scores output by the scoring model for the effective signal sampling points are obtained.

[0066] As an example, obtain the probability that the effective signal sampling point belongs to the effective signal category; based on the probability that the effective signal sampling point belongs to the effective signal category, determine the sampling point score corresponding to the effective signal sampling point. Among them, the probability can be used as the sampling point score, or the probability can be converted according to certain rules to obtain the sampling point score.

[0067] As an example, based on the sampling point scores corresponding to any effective signal sampling point in the sub-signal, perform mean processing and standard deviation processing to obtain the score mean and the score standard deviation; based on the score mean and the score standard deviation, determine the score threshold corresponding to the sub-signal.

[0068] Among them, each sub-signal corresponds to a score threshold, and the score thresholds corresponding to any two sub-signals may be the same or different. By way of example and not limitation, the sum of the score mean and N times the score standard deviation is used as the score threshold; N is a set value.

[0069] Step 206: Determine the effective signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold as the candidate signal sampling points.

[0070] For any sub-signal, determine the effective signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold as the candidate signal sampling points.

[0071] It should be noted that the candidate signal sampling points can be screened in each sub-signal based on the sampling point scores and the corresponding score thresholds.

[0072] Step 207: Based on the sampling point scores, select target signal sampling points from the candidate signal sampling points in any sub-signal.

[0073] Among them, the candidate signal sampling point corresponding to the maximum sampling point score can be used as the target signal sampling point.

[0074] Performing first arrival picking on the classification results based on the dynamic threshold method (the score threshold corresponding to each sub-signal) can distinguish the optimal state of different signal scores to the greatest extent, thereby effectively picking up the first arrival results of the signals.

[0075] Step 208: Determine the first arrival time based on the sampling time corresponding to the target signal sampling points.

[0076] Among them, the sampling time corresponding to the target signal sampling point can be used as the first arrival time.

[0077] It should be noted that the relevant content in Steps 201 - 203 can be referred to the relevant descriptions in the foregoing embodiments, and will not be elaborated here.

[0078] In this embodiment, for any sub-signal, based on the signal category, the noise signal sampling points in the sub-signal are removed to obtain effective signal sampling points; the sampling point scores corresponding to the effective signal sampling points are obtained, and the score threshold corresponding to the sub-signal is obtained; the effective signal sampling points in the sub-signal with sampling point scores greater than or equal to the score threshold are determined as candidate signal sampling points; based on the sampling point scores, target signal sampling points are selected from the candidate signal sampling points in any sub-signal; the first arrival time is determined based on the sampling time corresponding to the target signal sampling points. By screening candidate signal sampling points in each sub-signal through the score threshold corresponding to each sub-signal, the optimal state of different signal scores can be distinguished to the greatest extent, and then the accurate picking of the first arrival time of microseismic events can be realized based on the candidate signal sampling points.

[0079] An embodiment of the present application also proposes a device for picking the first arrival of coal mine microseismic signals. Figure 3 It is a schematic structural diagram of a device for picking the first arrival of coal mine microseismic signals provided by an embodiment of the present application.

[0080] As Figure 3 shown, the device 300 for picking the first arrival of coal mine microseismic signals includes:

[0081] A first acquisition module 310, configured to acquire a target microseismic signal corresponding to a coal mine microseismic event;

[0082] A second acquisition module 320, configured to, for any sub-signal in the target microseismic signal, acquire the target statistical value of any signal sampling point in the sub-signal on the target statistic, and obtain the statistical value sequence corresponding to the sub-signal;

[0083] A classification module 330, configured to classify any signal sampling point in the sub-signal based on the sub-signal and the corresponding statistical value sequence, so as to obtain the signal category of the signal sampling point;

[0084] A determination module 340, configured to determine the arrival time of the first arrival of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal.

[0085] Optionally, the determination module 340 is specifically configured to:

[0086] For any sub-signal, based on the signal category, remove the noise signal sampling points in the sub-signal to obtain effective signal sampling points;

[0087] Obtain the sampling point score corresponding to the effective signal sampling point, and obtain the score threshold corresponding to the sub-signal;

[0088] Determine the effective signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold as candidate signal sampling points;

[0089] Based on the sampling point scores, select target signal sampling points from the candidate signal sampling points in any sub-signal;

[0090] Determine the arrival time based on the sampling time corresponding to the target signal sampling point.

[0091] Optionally, the determination module 340 is specifically configured to:

[0092] Obtain the probability that the effective signal sampling point belongs to the effective signal category;

[0093] Based on the probability that the effective signal sampling point belongs to the effective signal category, determine the sampling point score corresponding to the effective signal sampling point.

[0094] Optionally, the determination module 340 is specifically configured to:

[0095] Based on the sampling point scores corresponding to any effective signal sampling points in the sub-signal, perform mean processing and standard deviation processing to obtain a score mean and a score standard deviation;

[0096] Based on the score mean and the score standard deviation, determine the score threshold corresponding to the sub-signal.

[0097] Optionally, the target statistic includes at least one of a fifth-order statistic, skewness, and kurtosis. The second acquisition module 320 is specifically configured to:

[0098] For any signal sampling point in the sub-signal, obtain a signal segment centered on the signal sampling point in the target microseismic signal by setting a sliding window;

[0099] Based on the signal segment, obtain the target statistical value of the signal sampling point on the target statistic;

[0100] For any target statistic, the target statistical values of each signal sampling point in the sub-signal on the target statistic are combined to obtain a statistical value sequence.

[0101] Optionally, the classification module 330 is specifically configured to:

[0102] Obtain a trained signal classification model;

[0103] Input the sub-signal and the corresponding statistical value sequence into the signal classification model to obtain the signal category output by the signal classification model for any signal sampling point in the sub-signal.

[0104] It should be noted that the foregoing explanation of the embodiment of the method for picking the first arrival of coal mine microseismic signals also applies to the device for picking the first arrival of coal mine microseismic signals in this embodiment, and will not be repeated here.

[0105] Figure 4 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Among them, the electronic device 400 in this embodiment is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0106] As Figure 4 shown, the above-mentioned electronic device 400 includes:

[0107] A memory 401 and a processor 402, a bus 403 connecting different components (including the memory 401 and the processor 402), and the memory 401 stores a computer program, and when the processor 402 executes the program, the method for picking the first arrival of coal mine microseismic signals in the embodiment of the present application is implemented.

[0108] The bus 403 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0109] The electronic device 400 typically includes a variety of electronic device-readable media. These media can be any available media that can be accessed by the electronic device 400, including volatile and non-volatile media, removable and non-removable media.

[0110] The memory 401 may also include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. The electronic device 400 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 406 can be used for reading and writing non-removable, non-volatile magnetic media ( Figure 4 not shown, commonly referred to as a "hard disk drive"). Although Figure 4 not shown in the figure, a disk drive for reading and writing removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing removable non-volatile optical disks (such as CD-ROM, DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to the bus 403 through one or more data media interfaces. The memory 401 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present application.

[0111] A program / utility 408 having a set (at least one) of program modules 407 can be stored, for example, in the memory 401. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 407 generally execute the functions and / or methods in the embodiments described in the present application.

[0112] The electronic device 400 can also communicate with one or more external devices 409 (such as a keyboard, a pointing device, a display 411, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 412. Moreover, the electronic device 400 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 413. As Figure 4As shown, network adapter 413 communicates with other modules of electronic device 400 via bus 403. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0113] Processor 402 performs various functional applications and data processing by running programs stored in memory 401.

[0114] It should be noted that for the implementation process and technical principle of the electronic device in this embodiment, refer to the foregoing explanation of the method for picking the first arrival of coal mine microseismic signals in the embodiments of the present application, which will not be elaborated here.

[0115] To implement the above embodiments, the present application also proposes a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method provided by the foregoing embodiments.

[0116] To implement the above embodiments, the present application also proposes a computer program product including a computer program, and when the computer program is executed by a processor, it implements the method provided by the foregoing embodiments.

[0117] For the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the present application, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0118] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the users, including but not limited to notifying the users to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the users use this function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0119] The present application anticipates providing embodiments where users can selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0120] In the descriptions of the foregoing embodiments, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0121] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0122] Any process or method description shown in a flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0126] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0127] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A method for picking up the first arrival of microseismic signals in coal mines, characterized in that: The following steps are involved: Obtain target microseismic signals corresponding to microseismic events in coal mines; For any sub-signal in the target microseismic signal, obtain a target statistical value of any signal sampling point in the sub-signal on a target statistical quantity to obtain a statistical value sequence corresponding to the sub-signal; Based on the sub-signal and the corresponding statistical value sequence, classify any signal sampling point in the sub-signal to obtain a signal category of the signal sampling point; Based on the signal category of any signal sampling point in the sub-signal, the first arrival time of the coal mine microseismic event is determined.

2. The method according to claim 1, characterized in that The determining the first arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal comprises: For any of the sub-signals, based on the signal category, noise signal sampling points in the sub-signal are removed to obtain valid signal sampling points; Obtaining a sampling point score corresponding to the valid signal sampling point, and obtaining a score threshold corresponding to the sub-signal; Determine the valid signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold as candidate signal sampling points; Selecting a target signal sampling point from the candidate signal sampling points in any of the sub-signals based on the sampling point scores; The first arrival time is determined based on the sampling time corresponding to the target signal sampling point.

3. The method according to claim 2, characterized in that The obtaining the sampling point score corresponding to the valid signal sampling point includes: Obtaining the probability that the valid signal sampling point belongs to a valid signal category; Based on the probability that the valid signal sampling point belongs to the valid signal category, a sampling point score corresponding to the valid signal sampling point is determined.

4. The method according to claim 2, characterized in that: The obtaining a score threshold corresponding to the sub-signal includes: Based on the sampling point scores corresponding to any of the valid signal sampling points in the sub-signals, mean processing and standard deviation processing are performed to obtain a score mean and a score standard deviation; A score threshold corresponding to the sub-signal is determined based on the score mean and the score standard deviation.

5. The method according to claim 1, characterized in that The target statistic includes at least one of a fifth-order statistic, skewness, and kurtosis, and the acquiring a target statistical value of any signal sampling point in the sub-signal on the target statistic to obtain a statistical value sequence corresponding to the sub-signal includes: For any signal sampling point in the sub-signal, a signal segment with the signal sampling point as the center point is obtained in the target microseismic signal by setting a sliding window; Acquire a target statistical value of the signal sampling point on a target statistical quantity based on the signal segment; For any of the target statistics, the target statistical values ​​of the signal sampling points in the sub-signals on the target statistics are combined to obtain the statistical value sequence.

6. The method according to claim 1, characterized in that The classifying any signal sampling point in the sub-signal based on the sub-signal and the corresponding statistical value sequence to obtain the signal category of the signal sampling point includes: Get the trained signal classification model; The sub-signal and the corresponding statistical value sequence are input into the signal classification model to obtain the signal category output by the signal classification model for any signal sampling point in the sub-signal.

7. A coal mine microseismic signal first arrival picking device, characterized in that: include: The first acquisition module is used to acquire the target microseismic signal corresponding to the microseismic event of the coal mine; A second acquisition module is used to acquire, for any sub-signal in the target microseismic signal, a target statistical value of any signal sampling point in the sub-signal on a target statistical quantity, and obtain a statistical value sequence corresponding to the sub-signal; A classification module, used for classifying any signal sampling point in the sub-signal based on the sub-signal and the corresponding statistical value sequence to obtain a signal category of the signal sampling point; A determination module is used to determine the first arrival time of the coal mine microseismic event based on the signal category of any signal sampling point in the sub-signal.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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