Method, apparatus and computer device for picking earthquake wave arrival time points

Abstract

The present application proposes a method, device, and computer equipment for picking up the arrival time points of seismic waves. The method includes: obtaining the seismic waves to be processed; using the fuzzy clustering method to obtain the first arrival time point of the P wave of the seismic waves to be processed; selecting a time window centered on the first arrival time point of the P wave, and using the AIC method to pick up the arrival time points of the seismic waves within the time window, and the obtained arrival time point is the second arrival time point of the P wave; obtaining the average value of the first arrival time point of the P wave and the second arrival time point of the P wave, and determining the average value as the accurate arrival time point of the P wave of the seismic waves to be processed. The embodiment of the present application uses the unsupervised learning fuzzy clustering method to predict the arrival time points of seismic waves. At the same time, on the basis of this prediction, the AIC method is used to further pick up the arrival time points of the P wave, and the average value of the P wave arrival time points picked up twice is used as the final accurate P wave arrival time point. While reducing the data processing volume, the sensitivity to noise can be greatly reduced, and the accuracy and reliability of the picked up arrival time points are greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of earthquake monitoring, and in particular to a method, device and computer equipment for picking up the arrival time of earthquake waves. Background Art

[0002] Seismic waves are vibrations that spread from the earthquake source, including physical waves and surface waves. Physical waves are divided into P waves and S waves. The time when these two waves reach the ground is the P wave arrival time and the S wave arrival time of the seismic wave. Accurately and efficiently picking up the arrival time of seismic waves plays a very important role in seismic wave data processing, and is also of great significance for the precise location of earthquakes and the explanation of earthquake breeding mechanisms.

[0003] Traditional methods for picking up seismic wave arrival times include the STA / LTA method and the template matching method. The STA / LTA method is to give a sliding long-time window on the seismic waveform, and then take a short-time window within the long-time window. The end points or starting points of the two time windows coincide, and the ratio of the short-time window signal average (STA) to the long-time window signal average (LTA) is used to reflect the change in signal amplitude or energy. At the time when the seismic signal arrives, the ratio will increase significantly. When it is greater than a certain threshold, it can be considered that an earthquake event has occurred. However, this method is very susceptible to background noise. If the threshold is not properly selected, the result will have a large deviation. Therefore, for signals with low signal-to-noise ratio, the application effect of this method is poor. The template matching method cross-correlates the actual analyzed data with the template waveform to detect events. Its accuracy depends on the number of templates. Not only does it require a large amount of template data, but the amount of calculation will increase exponentially with the increase in the number of templates.

[0004] Although some recently proposed neural network-based arrival-pickup methods can automatically extract features, especially some features that cannot be discovered by humans, and have relatively high accuracy, training neural networks often requires a large amount of data and high computer computing power, so a certain amount of data resources and hardware foundation are required.

[0005] In addition, the above methods often require the seismic waves to be de-noised first. After the data is de-noised, it will inevitably lead to the damage of useful signals and the reduction of data fidelity, resulting in the neglect of tiny seismic signals and affecting the subsequent processing of seismic waves. Summary of the invention

[0006] The embodiment of the present application provides a method, device and computer equipment for picking up the arrival time of seismic waves to solve the problems existing in the related technologies. The technical solution is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for picking up a seismic wave arrival time point, comprising:

[0008] Obtain the seismic wave to be processed;

[0009] Use the fuzzy clustering method to obtain the first P-wave arrival time point of the seismic wave to be processed;

[0010] Select a time window centered on the first P-wave arrival time point, and use the AIC method to pick the arrival time point of the seismic wave within the time window. The obtained arrival time point is the second P-wave arrival time point;

[0011] Obtain the average value of the first P-wave arrival time point and the second P-wave arrival time point, and determine the average value as the accurate P-wave arrival time point of the seismic wave to be processed.

[0012] In one implementation, specifically including using the fuzzy clustering method to obtain the first P-wave arrival time point of the seismic wave to be processed:

[0013] Obtain n sample data from the seismic wave to be processed:

[0014] X = {x1, x2, x3, …, x n}

[0015] Set the number of clusters for fuzzy clustering to 2, which respectively represent not being the first P-wave arrival time point and being the first P-wave arrival time point;

[0016] Construct the membership degree matrix U with the eigenvalue data of the sample data and the number of clusters:

[0017]

[0018] Iteratively optimize the membership degree matrix with the objective function to obtain the membership degree functions of 2 clusters; obtain the first obvious mutation point in the membership degree functions of 2 clusters, and output the first obvious mutation point as the first P-wave arrival time point; where the objective function is:

[0019]

[0020] In the above formula: n is the number of sample data; x i is the i-th element in the n sample data; j is the number of clustering clusters; k is the number of clusters for fuzzy clustering; u pj represents the probability that the p-th point in the seismic waveform belongs to the j-th cluster; m is the fuzzy coefficient; is the membership degree of x i in the cluster class j; c j is the central element of the cluster class; ||x i - c j || is the similarity between the sample data and the central element.

[0021] In one implementation, a time window is selected centered on the arrival time point of the first P wave, and the AIC method is used to pick up the arrival time point of the seismic wave within the time window. The obtained arrival time point is the arrival time point of the second P wave. It also includes:

[0022] Use the random forest method to determine whether the arrival time point of the first P wave is accurate;

[0023] When the judgment result is accurate, a time window is selected centered on the arrival time point of the first P wave, and the AIC method is used to pick up the arrival time point of the seismic wave within the time window. The obtained arrival time point is the arrival time point of the second P wave.

[0024] In one implementation, using the random forest method to determine whether the arrival time point of the first P wave is accurate includes:

[0025] Obtain multiple positive example data and negative example data as sample data. Each sample data includes a corresponding label, and the label is 0 or 1. 0 represents negative example data, that is, not the arrival time point, and 1 represents positive example data, that is, the arrival time point;

[0026] Centered on the positive example data, select the first time window and calculate the average value of each eigenvalue within the first time window; take the point far from the positive example data as the negative example data, centered on the negative example data, select the second time window, and calculate the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal;

[0027] Perform prediction with the vector composed of the average values of each eigenvalue to obtain the prediction result;

[0028] Among them, the eigenvalues include the following 4:

[0029] Classical STA / LTA:

[0030]

[0031] Recursive STA / LTA:

[0032]

[0033] Carr STA / LTA:

[0034] eta = star - (ratio · ltar) - |sta - lta| - quiet

[0035] Delayed STA / LTA:

[0036]

[0037] In the above formula, sta is the short window mean, lta is the long window mean, nsta is the short window length, nlta is the long window length, i represents the i-th point, xi Data representing the i-th point; star is the average value of the differences between the data of each point within the short window and sta, ltar is the average value of the differences between the data of each point within the long window and lta, and ratio and quiet are preset parameters.

[0038] In one embodiment, a time window is selected with the first P-wave arrival time point as the center, and the AIC method is used to pick the arrival time points of the seismic waves within the time window. The obtained arrival time point is the second P-wave arrival time point, specifically including:

[0039] AIC(i) = ilog 10 Var(x[1,i])+(n - i)log 10 Var(x[i + 1,n])

[0040] In the formula, i represents the i-th point in the seismic wave, n is the total length of the seismic wave, AIC(i) is the AIC value of the i-th point, and Var(x[1,i]) represents the variance from the first point to the i-th point in the seismic wave. In one embodiment, the method further includes:

[0041] Obtain the first data at the maximum amplitude position of the seismic wave to be processed;

[0042] Calculate the average value of the first data and the data at the first P-wave arrival time point, and mark it as the second data;

[0043] Use the AIC method to pick the arrival time points within the time windows of the first data and the second data. The obtained arrival time point is the S-wave arrival time point.

[0044] In a second aspect, an embodiment of the present application provides a device for picking seismic wave arrival time points, including:

[0045] A seismic wave acquisition module for acquiring the seismic wave to be processed;

[0046] A first P-wave arrival time acquisition module for acquiring the first P-wave arrival time of the seismic wave to be processed by using the fuzzy clustering method;

[0047] A second P-wave arrival time acquisition module for selecting a time window with the first P-wave arrival time point as the center and using the AIC method to pick the arrival time points of the seismic waves within the time window. The obtained arrival time point is the second P-wave arrival time point;

[0048] An accurate P-wave arrival time determination module for obtaining the average value of the first P-wave arrival time point and the second P-wave arrival time point and determining the average value as the accurate P-wave arrival time of the seismic wave to be processed.

[0049] In one embodiment, the first P-wave arrival time acquisition module specifically includes:

[0050] The first sample data acquisition sub-module is used to obtain n sample data from the seismic wave to be processed:

[0051] X = {x1, x2, x3, …, x n}

[0052] The cluster number setting sub-module is used to set the number of clusters for fuzzy clustering to 2, which respectively represent not the first P-wave arrival time point and the first P-wave arrival time point;

[0053] The membership degree matrix construction sub-module is used to construct the membership degree matrix U with the eigenvalue data of the sample data and the number of clusters:

[0054]

[0055] The optimization output sub-module is used to iteratively optimize the membership degree matrix with the objective function to obtain the membership degree functions of 2 clusters; obtain the first obvious mutation point in the membership degree functions of 2 clusters, and output the first obvious mutation point as the first P-wave arrival time point; among them, the objective function is:

[0056]

[0057] In the above formula: n is the number of sample data; x i is the i-th element in the n sample data; j is the number of clustering clusters; k is the number of clusters for fuzzy clustering; u pj represents the probability that the p-th point in the seismic waveform belongs to the j-th cluster; m is the fuzzy coefficient; is the membership degree of x i in the cluster class j; c j is the central element of the cluster class; ||x i - c j || is the similarity between the sample data and the central element.

[0058] In one implementation, it further includes:

[0059] The first P-wave arrival time point judgment sub-module is used to judge whether the first P-wave arrival time point is accurate by using the random forest method;

[0060] The second P-wave arrival time point acquisition module is used to select a time window centered on the first P-wave arrival time point when the judgment result is accurate, and use the AIC method to pick up the arrival time point of the seismic wave within the time window, and the obtained arrival time point is the second P-wave arrival time point.

[0061] In one implementation, the first P-wave arrival time point judgment sub-module specifically includes:

[0062] The second sample data acquisition sub-module is used to acquire multiple positive example data and negative example data as sample data. Each sample data includes a corresponding label, and the label is 0 or 1. 0 represents negative example data, that is, not the arrival time point, and 1 represents positive example data, that is, the arrival time point;

[0063] The eigenvalue calculation sub-module is used to take the positive example data as the center, select the first time window, and calculate the average value of each eigenvalue within the first time window; take the point far from the positive example data as the negative example data, take the positive example data as the center, select the second time window, and calculate the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal;

[0064] The prediction module is used to make a prediction based on the average value of each eigenvalue to obtain a prediction result;

[0065] Among them, there are 4 eigenvalues:

[0066] Classical STA / LTA:

[0067]

[0068] Recursive STA / LTA:

[0069]

[0070] Carr STA / LTA:

[0071] eta = star - (ratio · ltar) - |sta - lta| - quiet

[0072] Delayed STA / LTA:

[0073]

[0074] In the above formula, sta is the short window mean, lta is the long window mean, nsta is the short window length, nlta is the long window length, i represents the i-th point, and x i represents the data of the i-th point; star is the average value of the difference between each point data in the short window and sta, ltar is the average value of the difference between each point data in the long window and lta, and ratio and quiet are preset parameters.

[0075] In one implementation manner, the second P-wave arrival time point acquisition module specifically includes:

[0076] AIC(i) = ilog 10 Var(x[1,i]) + (n - i)log 10 Var(x[i + 1,n]).

[0077] In one implementation manner, the device further includes:

[0078] The first data acquisition module is used to acquire the first data of the maximum amplitude position of the seismic wave to be processed;

[0079] The second data acquisition module is used to calculate the average value of the first data and the first P-wave arrival time point data, which is marked as the second data;

[0080] The S-wave arrival time point acquisition module uses the AIC method to pick up the arrival time point within the time window of the first data and the second data, and the obtained arrival time point is the S-wave arrival time point.

[0081] In a third aspect, an embodiment of the present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, so that the at least one processor can execute the method for picking up the arrival time point of the seismic wave as described above.

[0082] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions. When the computer instructions are run on a computer, the method in any one of the above aspects is executed.

[0083] The advantages or beneficial effects in the above technical solutions at least include:

[0084] The embodiment of the present application uses the fuzzy clustering method of unsupervised learning to predict the arrival time point of the seismic wave, without training a large amount of data, reducing the training time cost. At the same time, based on the fuzzy clustering unsupervised learning, the AIC method is used to further pick up the P-wave arrival time point. While reducing the amount of data processing, the sensitivity to noise can be greatly reduced. For general signals, no denoising processing is required, and for signals with a low signal-to-noise ratio, only preliminary denoising is required. The accuracy and reliability of the picked arrival time points are greatly improved.

[0085] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the above-described illustrative aspects, embodiments, and features, further aspects, embodiments, and features of the present application will become apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In the drawings, unless otherwise specified, the same reference numerals throughout the several views denote the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in accordance with the present application and should not be regarded as limiting the scope of the present application.

[0087] Figure 1Shows an exemplary system architecture diagram to which the present application can be applied;

[0088] Figure 2 Is a schematic flow chart of a method for picking up the arrival time point of seismic waves according to an embodiment of the present application;

[0089] Figure 3 Is the waveform diagram of the membership function obtained according to an embodiment of the present application; wherein, Figure 3 (a) is the waveform diagram of the membership function of cluster 1; Figure 3 (b) is the waveform diagram of the membership function of cluster 2;

[0090] Figure 4 Is a schematic flow chart of a method for picking up the arrival time point of seismic waves according to another embodiment of the present application;

[0091] Figure 5 Is a schematic structural block diagram of a device for picking up the arrival time point of seismic waves according to an embodiment of the present application;

[0092] Figure 6 Is a schematic structural block diagram of a device for picking up the arrival time point of seismic waves according to another embodiment of the present application;

[0093] Figure 7 Is a block diagram of an electronic device for implementing the method of the structural block of the embodiment of the present application. Detailed implementation manners

[0094] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments, and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects, rather than to describe a specific order.

[0095] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0096] In order to enable those skilled in the art of this technology to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0097] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0098] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0099] The terminal devices 101, 102, 103 may be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, and desktop computers, etc.

[0100] The server 105 may be a server providing various services, such as a background server supporting the pages displayed on the terminal devices 101, 102, 103.

[0101] It should be noted that the seismic wave arrival time picking method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the seismic wave arrival time picking device is generally arranged in the server / terminal device.

[0102] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0103] Figure 2 are merely illustrative. According to the implementation requirements, there may be any number of terminal devices, networks, and servers. Figure 3 shown, the seismic wave arrival time picking method may include:

[0104] S210, obtain the seismic wave to be processed.

[0105] In the embodiments of the present application, the seismic wave to be processed obtained may be the seismic wave waveform data obtained historically; it may also be the seismic wave waveform data obtained in real time. The required seismic wave waveform data can be obtained as the seismic wave to be processed according to specific application requirements.

[0106] In one example, it may be to obtain the historical seismic wave waveform data in the database, pick up the arrival time points of the seismic wave, and verify the accuracy of the arrival time point picking results.

[0107] In another example, it may be to obtain the historical seismic wave waveform data in the database, pick up the arrival time points of the seismic wave, and obtain geological information of the seismic wave, etc.

[0108] In the embodiments of the present application, the electronic device (such as Figure 1 the server / terminal device shown) on which the seismic wave arrival time point picking method runs can obtain the seismic wave to be processed through a wired connection method or a wireless connection method. It should be noted that the above wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultrawideband) connections, and other currently known or future-developed wireless connection methods.

[0109] S220, adopt the fuzzy clustering method to obtain the first P-wave arrival time point of the seismic wave to be processed.

[0110] In the embodiments of the present application, the fuzzy clustering method of unsupervised learning is used to predict the arrival time points of seismic waves, without the need to train a large amount of data, reducing the training time cost.

[0111] S230, select a time window centered on the first P-wave arrival time point, and adopt the AIC method to pick up the arrival time points of the seismic waves within the time window. The obtained arrival time point is the second P-wave arrival time point.

[0112] In the embodiments of the present application, after obtaining the first P-wave arrival time point, then select a time window centered on the first P-wave arrival time point and perform AIC method processing to obtain the second P-wave arrival time point, which can effectively reduce the amount of data processing, weaken the sensitivity to noise, and eliminate the need for noise reduction processing.

[0113] S240, obtain the average value of the first P-wave arrival time point and the second P-wave arrival time point, and determine the average value as the accurate P-wave arrival time point of the seismic wave to be processed.

[0114] Based on fuzzy clustering unsupervised learning, the embodiment of the present application uses the AIC method for further P-wave arrival time picking, and takes the average value calculated from the first P-wave arrival time obtained by the fuzzy clustering method and the second P-wave arrival time obtained by the AIC method as the final accurate P-wave arrival time. Combining the advantages of the fuzzy clustering method and the AIC method, while reducing the amount of data processing, the sensitivity to noise can be greatly weakened, no denoising processing is required, and signals with a low signal-to-noise ratio only need preliminary denoising, and the accuracy and reliability of the arrival time are greatly improved.

[0115] Through the combination of the fuzzy clustering method and the AIC method, the embodiment of the present application obtains the final accurate P-wave arrival time, which can effectively improve the processing efficiency and avoid weak seismic wave signals from being ignored. The picked P-wave arrival time has high accuracy and greatly improved reliability.

[0116] In the embodiment of the present application, the average value calculated from the first P-wave arrival time obtained by the fuzzy clustering method and the second P-wave arrival time obtained by the AIC method is used as the final accurate P-wave arrival time. It can be understood that any other calculation method that combines the first P-wave arrival time and the second P-wave arrival time to obtain the accurate P-wave arrival time is within the protection scope of the present application.

[0117] In one embodiment, step S220 specifically includes:

[0118] Obtain n sample data from the seismic wave to be processed:

[0119] X = {x1, x2, x3, …, x n}

[0120] Set the number of clusters for fuzzy clustering to 2, respectively representing not the first P-wave arrival time and being the first P-wave arrival time;

[0121] Construct a membership matrix U with the eigenvalue data of the sample data and the number of clusters:

[0122]

[0123] Iteratively optimize the membership matrix with the objective function to obtain the membership functions of 2 clusters; obtain the first obvious mutation point in the membership functions of 2 clusters, and output the first obvious mutation point as the first P-wave arrival time; among them, the objective function is:

[0124]

[0125] In the above formula: n is the number of sample data; x i is the i-th element in n sample data; j is the number of clustering clusters; k is the number of clusters for fuzzy clustering; u pjIt represents the probability that the p-th point in the seismic waveform belongs to the j-th cluster; the sum of the values in each row of the membership matrix U is 1; m is the fuzzy coefficient; is the membership degree of x in cluster class j i ; c j is the central element of the cluster class; ||x i - c j || is the similarity between the sample data and the central element.

[0126] In the embodiment of the present application, each sample data vector has 3 eigenvalues, that is, x i = [x i1 , x i2 , x i3 . They respectively correspond to: 1. The average value (Mean) of the amplitudes of all points within a time window centered on the i-th point of the seismic wave; 2. The sum of the squares of the amplitudes of all points within a time window centered on the i-th point (Power); 3. The classical STA / LTA value (STA / LTA) of the i-th point. The calculation methods of the 3 eigenvalues are as follows:

[0127] Mean M:

[0128] Power P:

[0129] STA / LTA R:

[0130]

[0131] R(i) = STA(i) / LTA(i)

[0132] In the above formulas, i represents the i-th point of the seismic wave to be processed, w is half of the time window length, nsta is the short window length, nlta is the long window length, and x j represents the amplitude of the j-th point of the seismic wave to be processed.

[0133] After obtaining the eigenvalue data of the sample data, the fuzzy clustering method is used to process the eigenvalue data.

[0134] In an example, the fuzzy coefficient m is set to 2, and the termination threshold ξ is set to 0.0001. After multiple iterations, the membership function as shown in Figure 3 is finally obtained. As shown in Figure 3 , the first obvious mutation point in the membership function can be considered as the arrival time point of the first P wave.

[0135] In one implementation manner, before step S230, it further includes: using the random forest method to judge whether the arrival time point of the first P wave is accurate.

[0136] In the embodiment of the present application, the first P-wave arrival time obtained by the fuzzy clustering method is obtained by unsupervised learning. In order to improve the accuracy of the first P-wave arrival time, the random forest method is further used to judge whether the first P-wave arrival time is accurate. The random forest method belongs to supervised learning and requires training of data. However, the random forest method in the embodiment of the present application predicts the accuracy of the obtained P-wave arrival time, rather than predicting the P-wave arrival time of seismic waves. Therefore, its training data is greatly reduced, and the data processing efficiency can be improved while improving the accuracy of the first P-wave arrival time.

[0137] When the judgment result is accurate, step S230 is executed to select a time window centered on the first P-wave arrival time, and the AIC method is used to pick up the arrival time of the seismic waves within the time window, and the obtained arrival time is the second P-wave arrival time.

[0138] If the judgment result is inaccurate, the termination threshold ξ and the fuzzy coefficient m in the objective function S need to be adjusted, and step S220 is executed again until the result is accurate.

[0139] In the embodiment of the present application, when the judgment result of the first P-wave arrival time is accurate, step S230 is further executed, which can further improve the accuracy of the second P-wave arrival time.

[0140] In one embodiment, using the random forest method to judge whether the first P-wave arrival time is accurate specifically includes:

[0141] Obtain a plurality of positive example data and negative example data as sample data, and each sample data includes a corresponding label, and the label is 0 or 1. 0 represents negative example data, that is, not an arrival time, and 1 represents positive example data, that is, an arrival time.

[0142] In an example, the obtained positive example data and negative example data can be 100 respectively. The positive example data is the data that has been determined in advance as the arrival time; the negative example data can be the data of the points far from the data determined in advance as the arrival time.

[0143] In an example, the seismic wave data in the above positive example data and negative example data belongs to the same region as the seismic wave to be processed.

[0144] Centering on the positive example data, select the first time window, and calculate the average value of each eigenvalue within the first time window; use the points far from the positive example data as negative example data, center on the positive example data, select the second time window, and calculate the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal.

[0145] In an example, the time window lengths of the first time window and the second time window can be 150 points (sampling frequency of 100 Hz).

[0146] Predict using the vector composed of the average values of each eigenvalue to obtain the prediction result.

[0147] Among them, the eigenvalues include the following 4:

[0148] Classic STA / LTA:

[0149]

[0150] Recursive STA / LTA:

[0151]

[0152] Cal STA / LTA:

[0153] eta = star - (ratio · ltar) - |sta - lta| - quiet

[0154] Delay STA / LTA:

[0155]

[0156] In the above formula, sta is the short-window mean, lta is the long-window mean, nsta is the short-window length, nlta is the long-window length, i represents the i-th point, and x i represents the data of the i-th point; star is the average value of the differences between the data of each point in the short window and sta, ltar is the average value of the differences between the data of each point in the long window and lta, and ratio and quiet are preset parameters.

[0157] In one implementation, step S230 specifically includes:

[0158] AIC(i) = ilog 10 Var(x[1,i]) + (n - i)log 10 Var(x[i + 1,n])

[0159] In the formula, i represents the i-th point in the seismic wave, n is the total length of the seismic wave, AIC(i) is the AIC value of the i-th point, and Var(x[1,i]) represents the variance from the 1st point to the i-th point in the seismic wave. In one implementation, as Figure 4 shown, the seismic wave arrival time picking method of the embodiments of the present application further includes:

[0160] S250, obtain the first data at the position of the maximum amplitude of the seismic wave to be processed.

[0161] S260, calculate the average value of the first data and the first P-wave arrival time data, and mark it as the second data.

[0162] At S270, the arrival time point within the time window of the first data and the second data is picked up by using the AIC method, and the obtained arrival time point is the S-wave arrival time point.

[0163] In the embodiment of the present application, the AIC sequence corresponding to the waveforms between these two points is calculated, and the position where the minimum value in the sequence is located is the S-wave arrival time point. The S-wave arrival time point of the seismic wave to be processed can be obtained quickly and accurately.

[0164] Figure 5 The structural block diagram of the seismic wave arrival time point picking device 400 according to an embodiment of the present application is shown.

[0165] As Figure 5 shown, the seismic wave arrival time point picking device 400 may include:

[0166] A seismic wave acquisition module 410, configured to acquire the seismic wave to be processed;

[0167] A first P-wave arrival time point acquisition module 420, configured to acquire the first P-wave arrival time point of the seismic wave to be processed by using the fuzzy clustering method;

[0168] A second P-wave arrival time point acquisition module 430, configured to select a time window centered on the first P-wave arrival time point, and pick up the arrival time point of the seismic wave within the time window by using the AIC method, and the obtained arrival time point is the second P-wave arrival time point;

[0169] An accurate P-wave arrival time point determination module 440, configured to obtain the average value of the first P-wave arrival time point and the second P-wave arrival time point, and determine the average value as the accurate P-wave arrival time point of the seismic wave to be processed.

[0170] In one implementation manner, the first P-wave arrival time point acquisition module 420 specifically includes:

[0171] A first sample data acquisition sub-module, configured to acquire n sample data from the seismic wave to be processed:

[0172] X = {x1, x2, x3, …, x n}

[0173] A cluster number setting sub-module, configured to set the number of clusters of the fuzzy clustering to 2, respectively representing not the first P-wave arrival time point and being the first P-wave arrival time point;

[0174] A membership degree matrix construction sub-module, configured to construct a membership degree matrix U with the eigenvalue of the sample data and the number of clusters:

[0175]

[0176] An optimized output sub-module is used to iteratively optimize the membership matrix with an objective function to obtain the membership functions of two clusters; obtain the first obvious mutation point in the membership functions of the two clusters, and output the first obvious mutation point as the arrival time point of the first P wave; wherein, the objective function is:

[0177]

[0178] In the above formula: n is the number of sample data; x i is the i-th element in the n sample data; j is the number of clustering clusters; k is the number of clusters for fuzzy clustering; u pj represents the probability that the p-th point in the seismic waveform belongs to the j-th cluster; m is the fuzzy coefficient; is the membership degree of x i in the cluster class j; c j is the central element of the cluster class; ||x i -c j || is the similarity between the sample data and the central element.

[0179] In one implementation, the first P-wave arrival time point acquisition module 420 further includes:

[0180] The first P-wave arrival time point judgment sub-module is used to judge whether the first P-wave arrival time point is accurate by using the random forest method;

[0181] The second P-wave arrival time point acquisition module 430 is used to select a time window centered on the first P-wave arrival time point and pick up the arrival time point of the seismic wave within the time window by using the AIC method when the judgment result is accurate, and the obtained arrival time point is the second P-wave arrival time point.

[0182] In one implementation, the first P-wave arrival time point judgment sub-module specifically includes:

[0183] The second sample data acquisition sub-module is used to acquire multiple positive example data and negative example data as sample data, and each sample data includes a corresponding label, and the label is 0 or 1, where 0 represents negative example data, that is, not the arrival time point, and 1 represents positive example data, that is, the arrival time point;

[0184] The eigenvalue calculation sub-module is used to select the first time window centered on the positive example data and calculate the average value of each eigenvalue within the first time window; use the point far from the positive example data as the negative example data, select the second time window centered on the positive example data, and calculate the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal;

[0185] The prediction module is used to make a prediction with the average value of each eigenvalue to obtain a prediction result;

[0186] Among them, the eigenvalues include the following 4:

[0187] Classical STA / LTA:

[0188]

[0189] Recursive STA / LTA:

[0190]

[0191] Cal STA / LTA:

[0192] eta = star - (ratio·ltar) - |sta - lta| - quiet

[0193] Delay STA / LTA:

[0194]

[0195] In the above formula, sta is the short - window mean value, lta is the long - window mean value, nsta is the short - window length, nlta is the long - window length, i represents the i - th point, and x i represents the data of the i - th point; star is the average value of the differences between each point data in the short window and sta, ltar is the average value of the differences between each point data in the long window and lta, and ratio and quiet are preset parameters.

[0196] In one embodiment, the second P - wave arrival time point acquisition module 430 specifically includes:

[0197] AIC(i) = ilog 10 Var(x[1,i])+(n - i)log 10 Var(x[i + 1,n]).

[0198] In one embodiment, in one embodiment, the seismic wave arrival time point picking device 400 further includes:

[0199] The first data acquisition module 450, which is used to acquire the first data at the maximum amplitude position of the seismic wave to be processed;

[0200] The second data acquisition module 460, which is used to calculate the average value of the first data and the first P - wave arrival time point data, and mark it as the second data;

[0201] The S - wave arrival time point acquisition module 470, which uses the AIC method to pick the arrival time point within the time window of the first data and the second data, and the obtained arrival time point is the S - wave arrival time point.

[0202] For the functions of each module in each device of the embodiments of the present application, reference can be made to the corresponding descriptions in the above - mentioned method, and details are not described herein again.

[0203] Figure 7 A structural block diagram of an electronic device according to an embodiment of the present application is shown. As Figure 7 shown, the electronic device includes: a memory 710 and a processor 720. Instructions that can run on the processor 720 are stored in the memory 710. When the processor 720 executes the instructions, the earthquake wave arrival time picking method in the above embodiment is implemented. The number of the memory 710 and the processor 720 can be one or more. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, 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 application described herein and / or claimed.

[0204] The electronic device may further include a communication interface 730 for communicating with external devices and performing data interaction and transmission. Each device is interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor 720 can process instructions executed within the electronic device, including instructions stored in the memory or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (such as, as a server array, a set of blade servers, or a multi-processor system). The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0205] Optionally, in a specific implementation, if the memory 710, the processor 720, and the communication interface 730 are integrated on a single chip, the memory 710, the processor 720, and the communication interface 730 can communicate with each other through an internal interface.

[0206] It should be understood that the above-mentioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. It is worth noting that the processor can be a processor that supports the advanced RISC machines (ARM) architecture.

[0207] An embodiment of the present application provides a computer-readable storage medium (such as the above-mentioned memory 710), which stores computer instructions, and when the program is executed by a processor, it implements the method provided in the embodiment of the present application.

[0208] Optionally, the memory 710 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the electronic device picked up at the arrival time of the seismic wave, etc. In addition, the memory 710 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 710 may optionally include a memory remotely set relative to the processor 720, and these remote memories can be connected to the electronic device that picks up the arrival time of the seismic wave through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise internal network, a local area network, a mobile communication network, and combinations thereof.

[0209] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means 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. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0210] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, "a plurality of" means two or more unless specifically defined otherwise.

[0211] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more (two or more) executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of this application includes additional implementations, where functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed.

[0212] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing a logical function, 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 such instruction execution systems, apparatuses, or devices.

[0213] It should be understood that each part of this 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. All or part of the steps of the method in the above embodiments can be completed by a program instructing relevant hardware, and this program can be stored in a computer-readable storage medium. When this program is executed, it includes one or a combination of the steps of the method embodiment.

[0214] In addition, each functional unit in various embodiments of this application can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above 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. This storage medium can be a read-only memory, a magnetic disk, an optical disc, etc.

[0215] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various changes or substitutions thereof, and these should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for picking earthquake wave arrival times, characterized in that, Including: Obtain the seismic wave to be processed; Adopt the fuzzy clustering method to obtain the first P-wave arrival time point of the seismic wave to be processed; Select a time window centered on the first P-wave arrival time point, and adopt the AIC method to pick up the arrival time point of the seismic wave within the time window, and the obtained arrival time point is the second P-wave arrival time point; Obtain the average value of the first P-wave arrival time point and the second P-wave arrival time point, and determine the average value as the accurate P-wave arrival time point of the seismic wave to be processed; wherein, before the step of selecting a time window centered on the first P-wave arrival time point and adopting the AIC method to pick up the arrival time point of the seismic wave within the time window, and the obtained arrival time point is the second P-wave arrival time point, it further includes: Adopt the random forest method to judge whether the first P-wave arrival time point is accurate; In the case where the judgment result is accurate, select a time window centered on the first P-wave arrival time point, and adopt the AIC method to pick up the arrival time point of the seismic wave within the time window, and the obtained arrival time point is the second P-wave arrival time point; The step of adopting the random forest method to judge whether the first P-wave arrival time point is accurate includes: Obtain a plurality of positive example data and negative example data as sample data, and each sample data includes a corresponding label, and the label is 0 or 1, where 0 represents negative example data, that is, not an arrival time point, and 1 represents positive example data, that is, an arrival time point; Centered on the positive example data, select the first time window, and calculate the average value of each eigenvalue within the first time window; take the point far from the positive example data as the negative example data, and centered on the negative example data, select the second time window, and calculate the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal; Perform prediction with the vector composed of the average values of the respective eigenvalues to obtain a prediction result; Wherein, the eigenvalues include the following 4: Classical STA / LTA: Recursive STA / LTA: Causal STA / LTA: Delayed STA / LTA: In the above formula, sta is the short-window average value, lta is the long-window average value, nsta is the short-window length, nlta is the long-window length, i represents the i th point, x i represents the data of the i th point; star is the average value of the differences between the data of each point in the short window and sta , ltar is the average value of the differences between the data of each point in the long window and lta , ratio and quiet are preset parameters.

2. The method according to claim 1, wherein The step of adopting the fuzzy clustering method to obtain the first P-wave arrival time point of the seismic wave to be processed specifically includes: Obtain from the seismic wave to be processed n sample data: Set the number of clusters for fuzzy clustering to 2, respectively representing not the first P-wave arrival time point and being the first P-wave arrival time point; Construct a membership matrix U with the eigenvalue data of the sample data and the number of clusters; Iteratively optimize the membership matrix with the objective function to obtain the membership functions of 2 clusters; obtain the first obvious mutation point in the membership functions of the 2 clusters, and output the first obvious mutation point as the first P-wave arrival time point; wherein, the objective function is: In the above formula: n is the number of sample data; is n the i th element in j the sample data; k is the number of clusters in fuzzy clustering; p represents the probability that the j th point in the seismic waveform belongs to the m th cluster; is the fuzziness coefficient; j is the membership degree of the th element in the cluster; is the similarity between the sample data and the central element.

3. The method according to claim 1, wherein The step of selecting a time window centered on the first P-wave arrival time point and adopting the AIC method to pick up the arrival time point of the seismic wave within the time window, and the obtained arrival time point is the second P-wave arrival time point, specifically includes: In the formula, i represents the i -th point in the seismic wave, n is the total length of the seismic wave, is the AIC value of the i -th point, represents the variance from the 1st point to the i -th point in the seismic wave.

4. The method according to claim 1, characterized in that The method further includes: Obtain the first data at the maximum amplitude position of the seismic wave to be processed; Calculate the average value of the first data and the first P-wave arrival time point data, and mark it as the second data; The arrival time picking is performed within the time window of the first data and the second data by using the AIC method, and the obtained arrival time is the S-wave arrival time.

5. An earthquake wave arrival time picking device, characterized in that, It includes: A seismic wave acquisition module for acquiring seismic waves to be processed; A first P-wave arrival time acquisition module for acquiring the first P-wave arrival time of the seismic waves to be processed by using the fuzzy clustering method; A second P-wave arrival time acquisition module for selecting a time window centered on the first P-wave arrival time and performing arrival time picking on the seismic waves within the time window by using the AIC method, and the obtained arrival time is the second P-wave arrival time; An accurate P-wave arrival time determination module for obtaining the average value of the first P-wave arrival time and the second P-wave arrival time and determining the average value as the accurate P-wave arrival time of the seismic waves to be processed; Among them, the first P-wave arrival time acquisition module further includes: A first P-wave arrival time judgment sub-module for judging whether the first P-wave arrival time is accurate by using the random forest method; The second P-wave arrival time acquisition module is used for, when the judgment result is accurate, selecting a time window centered on the first P-wave arrival time and performing arrival time picking on the seismic waves within the time window by using the AIC method, and the obtained arrival time is the second P-wave arrival time; The first P-wave arrival time judgment sub-module specifically includes: A second sample data acquisition sub-module for acquiring a plurality of positive example data and negative example data as sample data, each sample data including a corresponding label, the label being 0 or 1, 0 representing negative example data, i.e., not an arrival time, and 1 representing positive example data, i.e., an arrival time; An eigenvalue calculation sub-module for, with the positive example data as the center, selecting a first time window and calculating the average value of each eigenvalue within the first time window; using the points far from the positive example data as negative example data, with the positive example data as the center, selecting a second time window and calculating the average value of each eigenvalue within the second time window; the lengths of the first time window and the second time window are equal; A prediction module for predicting with the average value of each eigenvalue to obtain a prediction result; Among them, the eigenvalues include the following 4: Classical STA / LTA: Recursive STA / LTA: Kalman STA / LTA: Delayed STA / LTA: In the above formula, sta is the short-window mean, lta is the long-window mean, nsta is the short-window length, nlta is the long-window length, i represents the i th point, x i represents the data of the i th point; star is the average value of the differences between the data points within the short window and sta ; ltar is the average value of the differences between the data points within the long window and lta ; ratio and quiet are preset parameters.

6. The device according to claim 5, characterized in that The device further includes: A first data acquisition module for acquiring the first data at the maximum amplitude position of the seismic waves to be processed; A second data acquisition module for calculating the average value of the first data and the first P-wave arrival time data and marking it as the second data; An S-wave arrival time acquisition module for performing arrival time picking within the time window of the first data and the second data by using the AIC method, and the obtained arrival time is the S-wave arrival time.

7. An electronic device, characterized in that, It includes: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-4.

8. A computer-readable storage medium storing computer instructions therein, the computer instructions, when executed by a processor, implementing the method according to any one of claims 1-4.

Images