Coal mine microseismic signal first arrival picking method and device, electronic equipment and storage medium

Abstract

The application provides a coal mine microseismic signal first arrival picking method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a target microseismic signal corresponding to a coal mine microseismic event; for any sub-signal in the target microseismic signal, obtaining a target statistical value of any signal sampling point in the sub-signal on a target statistical quantity, and obtaining a statistical value sequence corresponding to the sub-signal; based on the sub-signal and the corresponding statistical value sequence, classifying any signal sampling point in the sub-signal, and obtaining a signal category of the signal sampling point; and based on the signal category of any signal sampling point in the sub-signal, determining a first arrival time of the coal mine microseismic event.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coal mine monitoring, and in particular to a coal mine microseismic signal first arrival picking method and device, electronic equipment and storage medium. BACKGROUND

[0002] In coal mining, rock will release microseismic signals (MS) in the process of stress deformation and fracture. Microseismic signals can reflect changes in rock internal stress, energy propagation direction, etc. With the help of a sensor array arranged in the underground or surrounding area, microseismic signal collection can be achieved. Subsequent analysis of the collected microseismic signals using frequency spectrum analysis, time-frequency transformation, etc. can enable researchers to deeply explore the microscopic processes of rock internal fracture initiation and expansion, etc.

[0003] Among them, the microseismic first arrival wave carries a lot of key information, such as the first arrival time reflecting the rock fracture occurrence time, the internal energy relationship of rock fracture, etc. Therefore, monitoring microseismic signals and picking up the first arrival of microseismic signals are one of the keys to studying the rock fracture process. SUMMARY

[0004] The present application aims to at least partially solve one of the technical problems in the related art.

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

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

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

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

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

[0010] To achieve the above objects, the first aspect of the present application proposes a coal mine microseismic signal first arrival picking method, comprising:

[0011] obtaining a target microseismic signal corresponding to a coal mine microseismic event;

[0012] For any sub-signal in the target microseismic signal, obtaining a target statistical value of any signal sampling point in the sub-signal in a target statistical quantity, to obtain a statistical value sequence corresponding to the sub-signal;

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

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

[0015] To achieve the above object, the second aspect of the present application provides a coal mine microseismic signal first arrival picking device, comprising:

[0016] The first obtaining module is configured to obtain a target microseismic signal corresponding to a coal mine microseismic event.

[0017] The second obtaining module is configured to obtain, for any sub-signal in the target microseismic signal, a target statistical value of any signal sampling point in the sub-signal in a target statistical quantity, to obtain a statistical value sequence corresponding to the sub-signal.

[0018] The classification module is configured to classify 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.

[0019] The determination module is configured to determine the first arrival time of the coal mine microseismic event based on the signal category of any of the signal sampling points in the sub-signal.

[0020] To achieve the above object, the third aspect of the present application provides an electronic device, comprising a processor and a memory connected with the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the coal mine microseismic signal first arrival picking method according to the first aspect of the present application.

[0021] To achieve the above object, the fourth aspect of the present application provides a computer readable storage medium, which stores computer execution instructions; when the computer execution instructions are executed by a processor, the computer execution instructions are used to implement the coal mine microseismic signal first arrival picking method according to the first aspect of the present application.

[0022] To achieve the above object, the fifth aspect of the present application provides a computer program product, comprising a computer program; when the computer program is executed by a processor, the computer program implements the coal mine microseismic signal first arrival picking method according to the first aspect of the present application.

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

[0024] The application obtains a target microseismic signal corresponding to a coal mine microseismic event; 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 is obtained, to obtain a statistical value sequence corresponding to the sub-signal; based on the sub-signal and the corresponding statistical value sequence, any signal sampling point in the sub-signal is classified to obtain a signal category of the signal sampling point; and based on the signal category of any signal sampling point in the sub-signal, a first arrival time of the coal mine microseismic event is determined. Based on the target statistical quantity, statistical correlation and nonlinear characteristics of higher-order target microseismic signals of the target microseismic signal can be captured, so that the accuracy of determining the signal category based on the target statistical quantity is higher, and thus the accuracy of first arrival picking based on the signal category is higher.

[0025] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0026] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A flowchart of a coal mine microseismic signal first arrival picking method provided by an embodiment of the application is shown in the figure.

[0028] Figure 2 A flowchart of a coal mine microseismic signal first arrival picking method provided by another embodiment of the application is shown in the figure.

[0029] Figure 3 A structural diagram of a coal mine microseismic signal first arrival picking device provided by an embodiment of the application is shown in the figure.

[0030] Figure 4 A block diagram of an electronic device provided by an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0031] The embodiments of the application are described in detail below, and examples of the embodiments are shown in the drawings, in which the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the application, and cannot be understood as a limitation of the application.

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

[0033] Figure 1 A flowchart of a coal mine microseismic signal first arrival picking method provided by an embodiment of the application is shown in the figure.

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

[0035] Step 101, obtaining a target microseismic signal corresponding to a coal mine microseismic event.

[0036] In the embodiment of the present application, the target microseismic signal can be a monitored microseismic signal, or a signal obtained after preprocessing (such as normalization) of the monitored microseismic signal; wherein the target microseismic signal includes a plurality of signal sampling points.

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

[0038] Wherein, the target microseismic signal includes a plurality of sub-signals, and each sub-signal includes a plurality of signal sampling points. It should be noted that there can be overlap between adjacent two sub-signals, or there can be no overlap.

[0039] As an example, a sliding window can be set to slide in the target microseismic signal according to a set sliding step, and each sliding obtains a sub-signal. For example, the sliding step can be 1, and the window length of the set sliding window can be 40.

[0040] Wherein, the target statistical quantity includes at least one of the fifth order statistical quantity, skewness and kurtosis. The number of statistical value sequences corresponding to the sub-signals is the same as the number of target statistical quantities; assuming that the target statistical quantity includes the fifth order statistical quantity, skewness and kurtosis, then the statistical value sequence corresponding to the sub-signal includes the fifth order statistical quantity sequence, the skewness sequence and the kurtosis sequence.

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

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

[0043] Each signal sample point corresponds to a signal segment, and a target statistical value of each signal sample point on the target statistical quantity is calculated based on the signal segment corresponding to the signal sample point. It should be noted that due to the window sliding characteristic, the number of sampling points included in the signal segment corresponding to the signal sample 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 the 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 statistical quantity, the statistical correlation and nonlinear characteristics of the target microseismic signal of higher order of the signal can be captured, the Gaussian noise in the signal can be suppressed, and the accuracy of subsequent signal classification can be improved.

[0046] In step 103, any signal sample point in the sub-signal is classified based on the sub-signal and the corresponding statistical value sequence, and the signal class of the signal sample point is obtained.

[0047] The signal class is used to distinguish between effective signal sample points and noise signal sample points.

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

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

[0050] By using the CNN based on the ResNet, the problems of gradient disappearance and gradient explosion in the training process are effectively solved, so that the network can effectively learn and feature extract the input sub-signal and statistical value sequence at a deeper level, enhance the expression ability and generalization performance of the network, and then accurately distinguish noise and effective signals for microseismic events generated under complex geological conditions in coal mines.

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

[0052] Step 104, 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.

[0053] As an example, the noise signal in the target microseismic signal is removed based on the signal category, and the effective signal is reserved; the first arrival time of the coal mine microseismic event is determined from the effective signal based on the probability that each signal sampling point in the effective signal belongs to the effective signal. For example, the effective signal sampling point corresponding to the maximum probability value is obtained, and the first arrival time is determined based on the sampling time corresponding to the effective signal sampling point. Wherein, the probability that the signal sampling point in the sub-signal belongs to the effective signal can be obtained after the signal sampling point in the sub-signal is classified in step 103.

[0054] In this embodiment, the target microseismic signal corresponding to the coal mine microseismic event is obtained; for any sub-signal in the target microseismic signal, the target statistical value of any signal sampling point in the sub-signal on the target statistical quantity is obtained, and the statistical value sequence corresponding to the sub-signal is obtained; based on the sub-signal and the corresponding statistical value sequence, any signal sampling point in the sub-signal is classified to obtain the signal category of the signal sampling point; and the first arrival time of the coal mine microseismic event is determined based on the signal category of any signal sampling point in the sub-signal. Based on the target statistical quantity, the statistical correlation and nonlinear characteristics of the target microseismic signal of higher order of the target microseismic signal can be captured, so the accuracy of determining the signal category based on the target statistical quantity is higher, and the accuracy of the first arrival picking based on the signal category is higher. In addition, the present application can quickly and accurately perform signal first arrival picking work on large-scale microseismic events, and the picking efficiency is higher.

[0055] This embodiment provides another coal mine microseismic signal first arrival picking method, Figure 2 The flowchart of the coal mine microseismic signal first arrival picking method provided by the embodiment of the present application is shown in the figure.

[0056] As Figure 2 shown, the coal mine microseismic signal first arrival picking method can include the following steps:

[0057] Step 201, obtaining the target microseismic signal corresponding to the coal mine microseismic event.

[0058] Step 202, for any sub-signal in the target microseismic signal, obtaining the target statistical value of any signal sampling point in the sub-signal on the target statistical quantity, and obtaining the statistical value sequence corresponding to the sub-signal.

[0059] Step 203, based on the sub-signal and the corresponding statistical value sequence, classifying 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, removing the noise signal sampling point in the sub-signal to obtain the effective signal sampling point.

[0061] 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 points is 1, and the signal category of the noise signal sampling points is 0.

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

[0063] In step 205, the sampling point score corresponding to the effective signal sampling point is obtained, and the score threshold corresponding to the sub-signal is obtained.

[0064] The sampling point score is used to indicate the probability that the sampling time corresponding to the effective signal sampling point is the first arrival time; and 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 in which the noise signal sampling points are removed is input into the scoring model to obtain the sampling point score output by the scoring model for the effective signal sampling points.

[0066] As an example, the probability that the effective signal sampling point belongs to the effective signal category is obtained; and the sampling point score corresponding to the effective signal sampling point is determined based on the probability that the effective signal sampling point belongs to the effective signal category. 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 score corresponding to any effective signal sampling point in the sub-signal, mean value processing and standard deviation processing are performed to obtain a score mean value and a score standard deviation; and the score threshold corresponding to the sub-signal is determined based on the score mean value and the score standard deviation.

[0068] Each sub-signal corresponds to a score threshold, and the score thresholds corresponding to any two sub-signals can be the same or different. As an example but not limitation, the sum of the score mean value and N times the score standard deviation is used as the score threshold; N is a set value.

[0069] In step 206, the effective signal sampling points in the sub-signal with the sampling point score greater than or equal to the score threshold are determined as candidate signal sampling points.

[0070] For any sub-signal, the effective signal sampling points in the sub-signal with the sampling point score greater than or equal to the score threshold are determined as 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 score and the corresponding score threshold.

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

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

[0074] By using the dynamic threshold method (score thresholds corresponding to each sub-signal) to pick up the initial arrival of the classification results, the optimal state of different signal scores can be distinguished to the greatest extent, thereby effectively picking up the initial arrival results of the signals.

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

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

[0077] It should be noted that the relevant content in steps 201-203 can be found in the relevant descriptions in the foregoing embodiments, and will not be repeated here.

[0078] In this embodiment, for any sub-signal, noise signal sampling points are removed based on the signal category to obtain valid signal sampling points. The sampling point score corresponding to each valid signal sampling point is obtained, as well as the score threshold corresponding to the sub-signal. Valid signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold are identified as candidate signal sampling points. Based on the sampling point scores, a target signal sampling point is selected from the candidate signal sampling points in any sub-signal. The arrival time is determined based on the sampling time corresponding to the target signal sampling point. By filtering candidate signal sampling points in each sub-signal using the score threshold corresponding to each sub-signal, the optimal state of different signal scores can be distinguished to the greatest extent, thereby achieving accurate acquisition of the arrival time of microseismic events based on the candidate signal sampling points.

[0079] This application also proposes a device for picking up the first arrival of microseismic signals in coal mines. Figure 3 This is a schematic diagram of the structure of a coal mine micro-vibration signal first arrival pickup device provided in an embodiment of this application.

[0080] like Figure 3 As shown, the coal mine microseismic signal initial arrival pickup device 300 includes:

[0081] The first acquisition module 310 is used to acquire the target microseismic signal corresponding to the microseismic event in the coal mine.

[0082] The second acquisition module 320 is used to acquire the target statistical value of any signal sampling point in the target statistics for any sub-signal in the target microseismic signal, and obtain the statistical value sequence corresponding to the sub-signal.

[0083] The classification module 330 is configured to classify any signal sample point in the sub-signal based on the sub-signal and the corresponding sequence of statistical values, to obtain a signal category of the signal sample point.

[0084] The determination module 340 is configured to determine the first arrival time of the coal mine microseismic event based on the signal category of any signal sample 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, the noise signal sample points in the sub-signal are removed to obtain effective signal sample points;

[0087] The sample point score corresponding to the effective signal sample point is obtained, and a score threshold corresponding to the sub-signal is obtained;

[0088] The effective signal sample points with a sample point score greater than or equal to the score threshold in the sub-signal are determined as candidate signal sample points;

[0089] Based on the sample point score, a target signal sample point is selected from the candidate signal sample points in any sub-signal;

[0090] Based on the sampling time corresponding to the target signal sample point, the first arrival time is determined.

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

[0092] The probability that the effective signal sample point belongs to the effective signal category is obtained;

[0093] Based on the probability that the effective signal sample point belongs to the effective signal category, the sample point score corresponding to the effective signal sample point is determined.

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

[0095] Based on the sample point score corresponding to any effective signal sample point in the sub-signal, mean value processing and standard deviation processing are performed to obtain a score mean value and a score standard deviation;

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

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

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

[0099] Based on the signal segment, a target statistical value of the signal sample point on the target statistical quantity is obtained;

[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 the trained signal classification model;

[0103] input the sub-signal and the corresponding statistical value sequence into the signal classification model to obtain a 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 and description of the coal mine microseismic signal first arrival picking method embodiment also applies to the coal mine microseismic signal first arrival picking device of this embodiment, which will not be described here.

[0105] Figure 4 A structural schematic diagram of an electronic device provided by the embodiment of the present application is shown. The electronic device 400 in the embodiment is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0106] As shown in Figure 4 The electronic device 400 described above includes:

[0107] The memory 401 and the processor 402, the bus 403 connecting different components (including the memory 401 and the processor 402), the memory 401 stores a computer program, and the processor 402 executes the program to realize the coal mine microseismic signal first arrival picking method of the embodiment of the present application.

[0108] The bus 403 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of 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] Electronic device 400 typically includes a variety of computer system readable media. These media can be any available media that is located either internally or externally to electronic device 400, including both volatile and nonvolatile media, removable and non-removable media.

[0110] Storage 401 can also include memory in the form of computer system readable media, such as random access memory (RAM) 404 and / or cache memory 405. Electronic device 400 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 406 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 4 Although not shown, a magnetic hard drive, a magnetic disk drive (e.g., to read from or write to a removable, non-volatile magnetic disk (e.g., a "floppy drive" within the storage 401 can be provided. A magnetic hard disk drive can also be provided, in these cases, each can be connected to the bus 403 by one or more data media interfaces. The storage 401 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application. Figure 4

[0111] Program / utility 408, having a set (at least one) of program modules 407, can be stored in, for example, storage 401 by way of example, such program modules 407 include an operating system, one or more application programs, other program modules, and program data, each of which or a combination can include implementation of a network environment. Program modules 407 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0112] Electronic device 400 can also communicate with one or more external devices 409 such as a keyboard or a pointing device, a display 411, etc.; one or more devices that enable a user to interact with electronic device 400; and / or one or more devices that enable electronic device 400 to communicate with one or more other computing devices. Such communication can be via input / output (I / O) interfaces 412. Still yet, electronic device 400 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via network adapter 413. As Figure 4 ​As shown, network adapter 413 communicates with other modules of electronic device 400 over bus 403. It should be appreciated that although not shown, other hardware and / or software modules could 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 archival storage systems, etc.

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

[0114] It should be noted that the implementation process and technical principles of the electronic device of the embodiment are referred to the aforementioned explanation and description of the coal mine microseismic signal first arrival picking method of the embodiment of the application, and will not be repeated here.

[0115] In order to realize the above-mentioned embodiment, the application further provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. The computer execution instructions are executed by the processor to realize the method provided by the foregoing embodiment.

[0116] In order to realize the above-mentioned embodiment, the application further provides a computer program product, comprising a computer program, which is executed by the processor to realize the method provided by the foregoing embodiment.

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

[0118] It should be noted that the personal information from the user should be collected for legal and reasonable purposes, and should not be shared or sold outside these legal uses. In addition, such collection / sharing should be carried out after the user's informed consent is received, including but not limited to informing the user to read the user agreement / user notice before the user uses the function, and signing the agreement / authorization including authorization of relevant user information. In addition, any necessary steps should be taken to protect and ensure access to such personal information data, and to ensure that other people with access to personal information data comply with their privacy policy and processes.

[0119] The application is expected to provide an embodiment in which the user can selectively prevent the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or prevent access to such personal information data. Once the personal information data is no longer needed, the risk is minimized by limiting data collection and deleting data. In addition, such personal information is de-identified to protect the privacy of the user, if applicable.

[0120] In the foregoing detailed description, reference is made to descriptive terms such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. for describing various embodiments of the application. These descriptive terms are used for the purpose of the description and are not meant to limit or restrict the scope of the application. The use of these terms does not imply that the application is comprised of at least the described embodiments, or that the described embodiments are the only embodiments the application is comprised of. The scope of the application is not limited to the described embodiments, but is rather defined by the appended claims. In the description of the embodiments of the application, reference is made to the terms "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. which are meant to describe a particular feature, structure, material or characteristic included in at least one embodiment of the application. The illustrative description of these terms does not imply that the application is comprised of at least the described embodiments or that the described embodiments are the only embodiments the application is comprised of. In the description of the embodiments of the application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example described previously. Moreover, the described features, structures, materials or characteristics can be combined in any suitable manner in one or more embodiments or examples of the application. Furthermore, the described embodiments or examples of the application and the features thereof can be combined and combined in any suitable manner, without contradicting each other, by those skilled in the art.

[0121] Furthermore, the terms "first", "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or an indicated number of technical features. Thus, features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the application, the meaning of "a plurality" is at least two, for example two, three, etc., unless explicitly specified otherwise.

[0122] Any process or method descriptions or descriptions of the flow diagrams described herein or otherwise described in this application can be understood as representing the steps of a method or process, including a computer program in which the functions of the steps are performed by executable instructions. The preferred embodiments of this application include additional implementations in which the steps of the method or process are performed by a computer program that is executed by a computer or a processor. The program instructions can be stored on a computer-readable medium that can be accessed by a computer or a processor. The described processes can be implemented in software programs or computer programs that are executable on programmable systems. Each of the described processes can be implemented in a computer program that is executable on a programmable system to perform the functions described herein.

[0123] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a 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, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0124] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, specifically configured hardware can be used to implement at least some of the functionality described herein. For example, if implemented in hardware, the hardware can include any or a combination of the following: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0126] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can 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 can also be stored in a computer readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements 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, Includes the following steps: Acquire the target microseismic signals corresponding to microseismic events in coal mines; 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; Based on the sub-signal and the corresponding statistical value sequence, any signal sampling point in the sub-signal is classified to obtain the signal category of the signal sampling point; Based on the signal category of any of the signal sampling points in the sub-signals, the arrival time of the coal mine microseismic event is determined, including: 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; Obtain the sampling point score corresponding to the effective signal sampling point, and obtain the score threshold corresponding to the sub-signal, wherein the sampling point score is used to indicate the probability / likelihood that the sampling time corresponding to the effective signal sampling point is the initial arrival time; The valid signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold are determined as candidate signal sampling points. The score threshold is used to determine whether the sampling time corresponding to the valid signal sampling point is the first arrival time. Each sub-signal has a corresponding score threshold. The score thresholds corresponding to any two sub-signals may be the same or different. Based on the sampling point scores, a target signal sampling point is selected from the candidate signal sampling points in any of the sub-signals; The initial arrival time is determined based on the sampling time corresponding to the target signal sampling point; The step of obtaining the score threshold corresponding to the sub-signal includes: Based on the sampling point score corresponding to any valid signal sampling point in the sub-signal, mean processing and standard deviation processing are performed to obtain the mean score and the standard deviation score. Based on the mean score and the standard deviation of the score, the score threshold corresponding to the sub-signal is determined.

2. The method according to claim 1, characterized in that, The step of obtaining the sampling point score corresponding to the valid signal sampling point includes: Obtain the probability that the valid signal sampling point belongs to the valid signal category; Based on the probability that the valid signal sampling point belongs to the valid signal category, the sampling point score corresponding to the valid signal sampling point is determined.

3. The method according to claim 1, characterized in that, The target statistic includes at least one of fifth-order statistics, skewness, and kurtosis. Obtaining the target statistical value of any signal sampling point in the sub-signal on the target statistic, and obtaining the statistical value sequence corresponding to the sub-signal, includes: For any signal sampling point in the sub-signal, a signal segment centered on the signal sampling point is obtained in the target microseismic signal by setting a sliding window; Based on the signal segment, obtain the target statistical value of the signal sampling point on the target statistic; For any of the target statistics, the target statistical values ​​of each of the signal sampling points in the sub-signal on the target statistics are combined to obtain the statistical value sequence.

4. The method according to claim 1, characterized in that, The step of 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: Obtain a 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.

5. A device for acquiring the first arrival of microseismic signals in a coal mine, characterized in that, include: The first acquisition module is used to acquire the target microseismic signal corresponding to the microseismic event in the coal mine. The second acquisition module is used to acquire, for any sub-signal in the target microseismic signal, the target statistical value of any signal sampling point in the sub-signal on the target statistical quantity, and obtain the statistical value sequence corresponding to the sub-signal; The classification module is used 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. The determination module is used to determine the arrival time of the coal mine microseismic event based on the signal category of any of the signal sampling points in the sub-signals; The determining module is specifically used for: 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; Obtain the sampling point score corresponding to the effective signal sampling point, and obtain the score threshold corresponding to the sub-signal, wherein the sampling point score is used to indicate the probability / likelihood that the sampling time corresponding to the effective signal sampling point is the initial arrival time; The valid signal sampling points in the sub-signal whose sampling point scores are greater than or equal to the score threshold are determined as candidate signal sampling points. The score threshold is used to determine whether the sampling time corresponding to the valid signal sampling point is the first arrival time. Each sub-signal has a corresponding score threshold. The score thresholds corresponding to any two sub-signals may be the same or different. Based on the sampling point scores, a target signal sampling point is selected from the candidate signal sampling points in any of the sub-signals; The initial arrival time is determined based on the sampling time corresponding to the target signal sampling point; The step of obtaining the score threshold corresponding to the sub-signal includes: Based on the sampling point score corresponding to any valid signal sampling point in the sub-signal, mean processing and standard deviation processing are performed to obtain the mean score and the standard deviation score. Based on the mean score and the standard deviation of the score, the score threshold corresponding to the sub-signal is determined.

6. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-4.

8. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-4.

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