Seismic data processing method and device

Abstract

The embodiment of the invention provides a seismic data processing method and device, and the method comprises the steps: obtaining original seismic data, carrying out the three-stage data processing of the original seismic data, so as to obtain compressed seismic data, and outputting the compressed seismic data. Through three-stage data processing, the compressed seismic data corresponding to the effective seismic data in the original seismic data can be obtained, the effective seismic data and other data can be effectively distinguished, the data transmission delay can be reduced through compression transmission, and finally the seismic data processing efficiency and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of marine seismic monitoring, and in particular to a seismic data processing method and device. BACKGROUND

[0002] With the rapid development of marine economy and the rapid growth of demand for marine resource development, the acquisition and processing of submarine seismic data have become an important technology for marine geological activity monitoring and scientific analysis.

[0003] However, due to the complexity of the submarine situation, noise data generated by ocean currents, biological activities and equipment interference will affect the seismic signal, and the related technology lacks sufficient means to process noise data, resulting in a large amount of invalid or low-quality noise data affecting the processing of seismic data, and it is difficult to ensure low delay during data transmission for large-scale data, ultimately resulting in low processing efficiency and accuracy of seismic data.

[0004] To provide a method that can improve the processing efficiency and accuracy of seismic data, which is an urgent problem to be solved in the field. SUMMARY

[0005] The embodiments of the present application provide a seismic data processing method and device, which can improve the processing efficiency and accuracy of seismic data.

[0006] In a first aspect, the embodiments of the present application provide a seismic data processing method, comprising:

[0007] obtaining original seismic data;

[0008] performing three-level data processing on the original seismic data to obtain compressed seismic data;

[0009] outputting the compressed seismic data.

[0010] In a possible implementation, the obtaining of the original seismic data comprises:

[0011] extracting seismic data from at least one preset site in a multi-thread parallel processing manner to obtain the original seismic data, wherein the site comprises at least one station, and the thread and the station correspond one by one.

[0012] In a possible implementation, after the obtaining of the original seismic data, the method further comprises:

[0013] dividing the seismic data corresponding to each station to obtain one or more data blocks corresponding to each station;

[0014] parsing the data blocks to obtain attribute information corresponding to the data blocks;

[0015] The attribute information corresponding to the data block is packaged to obtain a data packet in a preset format.

[0016] In a possible implementation, the three-level data processing on the original seismic data to obtain compressed seismic data comprises:

[0017] determining data packets corresponding to each station;

[0018] performing first-level processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval in the data packets;

[0019] performing second-level processing of spatial double-station mutual recognition on the data packets corresponding to any two stations to determine a type of the data packets, wherein the type of the data packets comprises effective seismic event data and environmental noise data;

[0020] performing third-level processing of lossless compression processing on the effective seismic event data to obtain compressed seismic data.

[0021] In a possible implementation, the method further comprises:

[0022] performing lossy compression processing on the environmental noise data to obtain compressed environmental noise data;

[0023] outputting the compressed environmental noise data.

[0024] In a possible implementation, the attribute information comprises an extraction timestamp.

[0025] Correspondingly, the first-level processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval in the data packets comprises:

[0026] dividing the data packets corresponding to each station by a preset time window to obtain time sequence data corresponding to each station under each time window;

[0027] under each time window, determining one of the time sequence data as first time sequence data and determining each of the time sequence data other than the first time sequence data as second time sequence data;

[0028] aligning each second time sequence data with the first time sequence data to obtain an extraction timestamp repetition rate corresponding to each second time sequence data;

[0029] determining, as the data in the low confidence interval, the second time sequence data corresponding to an extraction timestamp repetition rate less than a first preset value.

[0030] In a possible implementation, the second level processing of the data packet corresponding to any two stations by spatial two-station mutual authentication is configured to determine the type of the data packet, including:

[0031] acquiring the actual time delay of the seismic wave in the data packet between the two stations;

[0032] determining the spatial distance between the two stations and the wave velocity of the seismic first arrival wave;

[0033] determining the time delay range of the seismic wave between the two stations according to the spatial distance between the two stations and the wave velocity of the seismic first arrival wave;

[0034] if the actual time delay is within a preset proportion of the time delay range, the seismic wave in the data packet is determined as a valid seismic wave signal, otherwise, the seismic wave in the data packet is determined as an invalid seismic wave signal;

[0035] determining the signal energy ratio between the two stations;

[0036] if the signal energy ratio is within a preset range, the seismic wave in the data packet is determined as a valid seismic event, otherwise, the seismic event corresponding to the data packet is determined as an invalid seismic event;

[0037] if the seismic wave in the data packet is a valid seismic wave signal and the corresponding seismic event is a valid seismic event, the data packet is determined as valid seismic event data;

[0038] if the seismic wave in the data packet is an invalid seismic wave signal and the corresponding seismic event is an invalid seismic event, the data packet is determined as environmental noise data.

[0039] In a possible implementation, each station includes at least one channel, and the attribute information further includes a station number and a channel number;

[0040] Correspondingly, the method further includes:

[0041] creating a subject queue corresponding to each channel of each station according to the station number and the channel number;

[0042] distributing the compressed seismic data or the compressed environmental noise data to the subject queue of the corresponding channel of the corresponding station according to the corresponding station number and the channel number;

[0043] In a possible implementation, outputting the compressed seismic data or the compressed environmental noise data includes:

[0044] outputting the compressed seismic data or the compressed environmental noise data according to the corresponding subject queue.

[0045] In a second aspect, the present application provides a seismic data processing device, comprising:

[0046] a data acquisition and analysis module configured to acquire raw seismic data;

[0047] a three-stage data processing module configured to perform three-stage data processing on the raw seismic data to obtain compressed seismic data;

[0048] a data buffering and distribution module configured to output the compressed seismic data.

[0049] In a third aspect, the present application provides a computer device, comprising: a memory, a processor;

[0050] the memory stores computer execution instructions;

[0051] the processor executes the computer execution instructions stored in the memory, so that the processor performs the method as described above.

[0052] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method as described above.

[0053] In a fifth aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the method as described above.

[0054] The seismic data processing method and device provided by the embodiments of the present application can effectively distinguish seismic data from other data, and can improve the processing efficiency and accuracy of seismic data by transmitting in a compressed form. BRIEF DESCRIPTION OF DRAWINGS

[0055] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0056] Figure 1 The scene schematic diagram provided by the present application;

[0057] Figure 2 The flowchart of the seismic data processing method provided by the present application Figure One ;

[0058] Figure 3 The flowchart of the seismic data processing method provided by the present application Figure Two ;

[0059] Figure 4 Flowchart of the method for processing seismic data provided by the present application Figure Three ;

[0060] Figure 5 Processing flow of the exemplary three-stage data processing module

[0061] Figure 6 Flowchart of the method for processing seismic data provided by the present application Figure Four ;

[0062] Figure 7 Flowchart of the method for processing seismic data provided by the present application Figure Five ;

[0063] Figure 8 Flowchart of the method for processing seismic data provided by the present application Figure Six ;

[0064] Figure 9 Flowchart of the method for processing seismic data provided by the present application Figure Seven ;

[0065] Figure 10 Structural diagram of the device for processing seismic data provided by the present application

[0066] Figure 11 Structural diagram of the computer device provided by the present application.

[0067] The specific embodiments of the present application have been shown in the above-mentioned drawings, and will be described in more detail hereinafter. These drawings and the written description are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0068] The exemplary embodiments will be described in detail herein with reference to the attached drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments are not meant to represent all implementations in which one consistent with the present application can be practiced. Rather, they are merely examples with which one consistent with the present application can be practiced.

[0069] Figure 1 Scenario diagram provided by the present application, such as Figure 1As shown, the scenario of the present application includes a submarine seismograph, a buoy, a network access device in a seismic station, a seismic data processing system, a server and a display terminal. Among them, the submarine seismograph is responsible for collecting seismic monitoring data of the seabed, and the seismograph contains a velocity meter, an accelerometer and a hydrophone, supporting synchronous collection of three-axis direction velocity, three-axis direction acceleration and underwater acoustic signal. Then it is transmitted to the buoy end on the sea through the dynamic cable, and the buoy end can choose to send the seismic monitoring data to the seismic station through the satellite communication mode or the mobile base station communication mode. The seismic station receives the seismic monitoring data through the network access device, which can be a virtual private network (English: Virtual Private Network, abbreviated as VPN) device, and then accesses the seismic data processing system. The seismic data processing system can be a digital seismic network information real-time processing and earthquake automatic reporting system (Jopens), including seismic data monitoring and archiving, earthquake warning and intensity reporting modules. The server obtains the seismic monitoring data from the Jopens system and processes the seismic monitoring data, and finally sends the processed data to the display terminal for display.

[0070] For the selection of server type, the server can be a physical server, a virtual server, a cloud server and a high-performance computing cluster server, etc. Among them, the physical server has the advantages of high performance and high reliability, is suitable for processing a large amount of real-time data, and is suitable for seismic data processing centers that require high computing power and low latency; the virtual server has high flexibility and can dynamically adjust resources according to demand, which is suitable for test environments or scenarios that require rapid expansion; the cloud server can provide elastic resources, can be globally distributed and has the advantages of high availability, which is suitable for seismic monitoring systems that require global data collection and analysis; the high-performance computing cluster server can handle complex computing tasks such as seismic simulation and big data analysis, which is suitable for scientific research institutions that need to perform complex seismic simulation and analysis. For the selection of display device type, the display device can be a desktop computer display screen, a cloud server display page, a television screen, a video wall, a notebook computer, a tablet computer, a smart phone, an augmented reality (Augmented Reality, abbreviated as AR) device and a virtual reality (Augmented Reality, abbreviated as VR) device, etc. The selection of appropriate servers and display terminals depends on specific application requirements, budget and technical capabilities. For a comprehensive seismic data processing system, multiple types of servers and terminals are usually combined to meet different functional requirements and usage scenarios.

[0071] However, due to the complexity of the seabed, noise data generated by ocean currents, biological activities, and equipment interference can affect seismic signals. The relevant technologies are insufficient in processing noise data, resulting in a large amount of invalid or low-quality noise data affecting the processing of seismic data, leading to low processing efficiency and accuracy.

[0072] The seismic data processing method provided in this application performs three-level data processing on the raw seismic data to obtain compressed seismic data and compressed environmental noise data, and then outputs the compressed seismic data. This application can effectively distinguish seismic data from other data, and by transmitting it in a compressed form, it can improve the processing efficiency and accuracy of seismic data.

[0073] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0074] Figure 2 Flowchart of the seismic data processing method provided in this application Figure One ,like Figure 2 As shown, it includes:

[0075] S201. Obtain raw seismic data.

[0076] Based on the scenario example, the executing entity of this application is Figure 1 The example server can be divided into a data acquisition and parsing module, a three-level data processing module, and a data buffering and distribution module. Earthquake monitoring data acquired from the Jopens system can be identified as raw earthquake data. Optionally, the server's acquisition and parsing module can obtain raw earthquake data based on the Jopens system's real-time waveform data stream service. Alternatively, it can use preset functions, such as the `requests.get` method, to call the Jopens system's real-time waveform data stream service to obtain raw earthquake data as a continuous data stream. The format of the raw earthquake data can be the Mini SEED (Mini Standard for Me Exchange of Earthquake Data).

[0077] S202. Perform three-level data processing on the raw seismic data to obtain compressed seismic data.

[0078] In combination with a scene example, the original seismic data includes interference data generated between devices, environmental noise data of the sea, and effective seismic event data. The three-level data processing includes time alignment processing, spatial double-station mutual recognition event identification processing, and data compression transmission processing. The time alignment processing is used to remove data with too large time sequence differences in the original seismic data. The data with too large time sequence differences can be determined as interference data. Removing the interference data can ensure the uniformity of the time sequence of the seismic data and the accuracy of subsequent analysis of the seismic data. The spatial double-station mutual recognition event identification processing can determine whether the seismic data is an effective seismic event, so as to divide the original seismic data into effective seismic event data and environmental noise data. The data compression transmission processing is used for compression and transmission of the effective seismic event data, which can speed up the transmission rate of the data. Optionally, the server can use a three-level data processing module to perform three-level data processing on the original seismic data.

[0079] S203, output the compressed seismic data.

[0080] In combination with a scene example, the data buffer distribution module of the server can output the compressed seismic data. Specifically, the compressed seismic data can be sent to a display terminal, so that the display terminal displays the compressed seismic data, and relevant personnel can monitor the seismic data. Specifically, the compressed seismic data can be displayed in a visual form on the display terminal, such as a waveform. Visual display refers to a method and technology of converting data into graphics or images and displaying them on a screen for interactive processing.

[0081] Based on the method provided in the example, compressed seismic data in the original seismic data can be obtained, effective seismic data and other data can be effectively distinguished, and the effective seismic data can be output in a compressed form. The delay of data transmission can be reduced, and finally the efficiency and accuracy of processing seismic data can be improved.

[0082] Optionally, S201 includes:

[0083] The multi-thread parallel processing mode is used to extract seismic data from at least one preset station to obtain the original seismic data. The station includes at least one station. The thread and the station are in one-to-one correspondence.

[0084] In combination with the scene example, the Jopens system can include at least one site, each site generally including two stations for storing strong earthquake data and seismic data in the corresponding site, and the example takes two sites as an example, that is, the Jopens system includes four stations. A plurality of threads can be created through the threading library, each thread corresponds to obtain the seismic data of one station, so that the data acquisition and analysis module can create four threads, and obtain the corresponding seismic data from the four stations in parallel through the four threads, and determine the seismic data obtained from the four stations as the original seismic data. Based on the method provided in the example, each thread is responsible for processing the seismic data obtained from one station, and the seismic data is obtained from a plurality of stations in a multi-thread parallel manner, which can improve the efficiency of obtaining the seismic data.

[0085] Optionally, Figure 3 Flowchart of the seismic data processing method provided in the present application Figure Two After S201, it further includes:

[0086] S301, the seismic data corresponding to each station is divided to obtain one or more data blocks corresponding to each station.

[0087] In combination with the scene example, the multi-thread parallel processing mode is still used, each thread is responsible for processing the seismic data of the corresponding station, and the seismic data can be processed in blocks to improve the processing efficiency of the seismic data. In the example, the iter-content method can be used to iterate the data stream in units of 512 bytes to obtain the segmented data blocks.

[0088] S302, the data block is parsed to obtain the attribute information corresponding to the data block.

[0089] In combination with the scene example, the read method of the obspy library can be used to parse the data block obtained above to obtain the attribute information corresponding to the data block. The attribute information of the data block includes: extraction timestamp, station number, channel number, sampling rate and vibration data, etc.

[0090] S303, the attribute information corresponding to the data block is packaged to obtain a data packet in a preset format.

[0091] In combination with the scene example, JavaScipt Obiect Notation (JSON) is a lightweight data exchange format, easy to read and write by people, and easy to parse and generate by machines, so optionally, the attribute information can be packaged into a JSON format data packet.

[0092] Based on the method provided in the example, the original seismic data can be segmented into multiple data blocks, and the attribute information of each data block is packaged into a JSON format data packet, which can facilitate subsequent analysis of the seismic data.

[0093] Optionally, Figure 4 The flowchart of the seismic data processing method provided in the application is shown in Figure Three As Figure 4 shown, S202 includes:

[0094] S401, determining the data packet corresponding to each site.

[0095] In combination with the scene example, in combination with the above content, taking two sites as an example, each site includes two stations, so the data packets of the seismic data corresponding to the four stations in the two sites can be obtained respectively.

[0096] S402, performing first-level processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval of the data packet.

[0097] In combination with the scene example, Figure 5 The processing flow of the three-level data processing module of the example is shown in Figure 5 The three-level data processing module first obtains the data packets of the seismic data corresponding to the four stations, and then the three-level data processing module performs time sequence alignment processing on the data packets of the four stations to obtain low confidence interval data. The low confidence interval data refers to a data packet whose time sequence difference with other three data packets is too large, so the data packet can be removed to ensure the uniformity of the data time sequence. The data with a time sequence difference that is too large can be determined as interference data, and removing the interference data can ensure the accuracy of subsequent analysis of the seismic data.

[0098] S403, for any two sites corresponding to the data packets, performing second-level processing of spatial double-station mutual recognition on the data packets to determine the type of the data packets, wherein the type of the data packets includes effective seismic event data and environmental noise data.

[0099] In combination with the scene example, in combination with Figure 5 After the three-level data processing module completes the time sequence alignment processing of each data packet, it determines whether the data packet type is effective seismic event data by spatial double-station mutual recognition. Spatial double-station mutual recognition refers to mutual recognition screening of the data packets in two different sites according to seismic wave signals to determine whether the data packets are effective seismic event data or environmental noise data. For example, there are site A and site B, data packet 1 belongs to site A, and data packet 2 belongs to site B. The data packet 1 and the data packet 2 can be mutually recognized and screened to determine the types of the data packet 1 and the data packet 2.

[0100] S404, performing third-level processing of lossless compression on the effective seismic event data to obtain compressed seismic data.

[0101] In combination with the scene example, in combination Figure 5 , after the first-level processing and the second-level processing, the third-level data processing module can divide each data packet into effective seismic event data and environmental noise data, and the third-level processing refers to compressing the effective seismic event data and the environmental noise data separately.

[0102] First, for the effective seismic event data, the Zstandard algorithm is used for lossless compression to obtain compressed seismic data. Specifically, the information is compressed by entropy coding, the probability distribution of the symbol stream is modeled, and a compact code is generated. Let the symbol set be , the probability of each symbol appearing is , which satisfies , and the calculation formula of the entropy is represented by the following first formula:

[0103] First formula:

[0104] In actual operation, a compression function is used, the data block representing the vibration data part is input , and the compression level is determined , and the compression function is as follows:

[0105] .

[0106] Wherein the compression level is higher, indicating that the compression ratio is larger, and for the effective seismic event data, level 3 can be used to balance the speed and compression rate.

[0107] Based on the method provided in the example, three-level data processing can be performed on the seismic data to effectively distinguish the effective seismic data and the environmental noise data in the seismic data, thereby improving the efficiency and accuracy of processing the seismic data. And lossless compression processing is performed on the effective seismic data, which can further improve the efficiency of data transmission.

[0108] Optionally, Figure 6 The flowchart of the seismic data processing method provided in the present application Figure Four , as shown in Figure 6 , further comprises:

[0109] S601, performing lossy compression processing on the environmental noise data to obtain compressed environmental noise data.

[0110] In combination with the scene example, in combination Figure 5 , the third-level processing further comprises compressing the environmental noise data to obtain compressed environmental noise data;

[0111] For the environmental noise data, lossy compression can be used, and the sampling points in the original unit time can be reduced by 50% through downsampling. The data block after downsampling is processed by using an LZ4 compression algorithm, and the compression function is as follows:

[0112]

[0113] The compression level is 9 to maximize the data compression rate.

[0114] S602, output the compressed environmental noise data.

[0115] In combination with a scene example, the data buffering and distribution module of the server can send the compressed environmental noise data and the compressed seismic data to the display terminal at the same time, so that the display terminal displays the compressed seismic data and the compressed environmental noise data in a differentiated manner. The display of the compressed environmental noise data can also use a visual display manner.

[0116] Based on the method provided in the example, for the environmental noise data, lossy compression with a high compression rate is used to significantly reduce the bandwidth occupation, improve the data transmission efficiency, and display the compressed seismic data and the compressed environmental noise data in a differentiated manner, which can effectively distinguish the seismic data and the environmental noise data.

[0117] Optionally, Figure 7 The flowchart of the seismic data processing method provided in the present application is shown in Figure Five The attribute information includes an extraction timestamp.

[0118] Correspondingly, as shown in Figure 7 S402 includes:

[0119] S701, divide the data packets corresponding to each station in a preset time window to obtain time series data corresponding to each station in each time window.

[0120] In combination with a scene example, two stations are taken as an example, each station includes two stations, and the time window can be determined according to actual conditions. In the example, 5S is selected as a time window, specifically, 5S event window aggregation is performed to obtain four time series, and then according to the attribute information of the extraction timestamp corresponding to the data packet, a timestamp set corresponding to each time series in each window period is determined.

[0121] S702, in each time window, one of the time series data is determined as first time series data, and each time series data except the first time series data is determined as second time series data.

[0122] Based on the scenario example, any one of the four time series can be selected as the base time series. The base time series can be determined as the first time series data, and then the other three time series can be determined as the second time series data.

[0123] S703. Align each second time series data with the first time series data to obtain the extraction timestamp repetition rate corresponding to each second time series data.

[0124] With a scenario example, the Dynamic Time Warping (DTW) algorithm can be used to align each second time series data point with the first time series data point to calculate the timestamp repetition rate between each second time series data point and the first time series data point.

[0125] Specifically, if the first time series data is defined as X, and the second time series data is defined as Y, then... , DTW-based calculation and The distance between them is shown in the second formula below:

[0126] Second formula:

[0127] in, for and The distance between them , , .

[0128] Let the time interval of the first time series data be... The time interval of the second time series data is The timestamp overlap between the second time series data and the first time series data is shown in the third formula below:

[0129] Third formula:

[0130] in, This represents the timestamp overlap between the second and first time series data. The timestamp repetition rate is shown in the fourth formula below:

[0131] Fourth formula:

[0132] Where r is the timestamp repetition rate between the second time series data and the first time series data.

[0133] S704, determine the second time sequence data with a corresponding extraction timestamp repetition rate less than the first preset value as the data in the low confidence interval.

[0134] In combination with a scene example, the first preset value can be determined according to actual conditions. In this example, the first preset value can be determined as 90%. According to the second to fourth formulas, the timestamp repetition rate between each second time sequence data and the first time sequence data can be calculated, and the second time sequence data with a timestamp repetition rate less than 90% with the first time sequence data can be determined as the data in the low confidence interval.

[0135] Based on the method provided in this example, the data in the low confidence interval in the data packet corresponding to the seismic data can be determined, so as to accurately remove the data in the low confidence interval, and to ensure the uniformity of the seismic data in time sequence.

[0136] Optionally, Figure 8 The flowchart of the seismic data processing method provided in this application is shown in Figure Six As shown in Figure 8 S403 includes:

[0137] S801, obtain the actual time delay of the seismic wave between the two stations in the data packet.

[0138] In combination with a scene example, the actual time delay of the actual seismic wave arriving at the two stations can be determined according to the extraction timestamp in the data packet corresponding to the two stations.

[0139] S802, determine the spatial distance between the two stations and the wave velocity of the seismic first arrival wave.

[0140] In combination with a scene example, the distance between the two stations can be obtained, which can be denoted as D. According to the sea area geological model, the P wave, that is, the wave velocity of the first arrival wave, can be set, which can be denoted as V. According to the wave velocity of the first arrival wave, the maximum and minimum values of the wave velocity can be estimated, which can be denoted as Vmax and Vmin, respectively.

[0141] S803, determine the time delay range of the seismic wave between the two stations by the spatial distance between the two stations and the wave velocity of the seismic first arrival wave.

[0142] In combination with a scene example, the time delay range can be determined by the spatial distance D between the two stations and Vmax and Vmin. Specifically, if the time delay range is denoted as Δt, the maximum value of Δt is the ratio between D and Vmin, and the minimum value of Δt is the ratio between D and Vmax.

[0143] S804, if the actual time delay is within a preset proportion of the time delay range, the seismic wave in the data packet is determined as a valid seismic wave signal, otherwise, the seismic wave in the data packet is determined as an invalid seismic wave signal.

[0144] In combination with a scene example, the preset proportion can be determined according to actual conditions, for example, the preset proportion is determined as ±20% in this example, and it is needed to judge whether the actual time delay is within ±20% of the time delay range. If the actual time delay is recorded as Δta, it is judged whether the following fifth formula is established:

[0145] Fifth formula:

[0146] If the actual time delay Δta of the seismic wave in the data packet between the two stations satisfies the above fifth formula, the seismic wave is determined as a valid seismic wave signal, and if the actual time delay Δta of the seismic wave in the data packet between the two stations does not satisfy the above fifth formula, the seismic wave is determined as an invalid seismic wave signal.

[0147] S805, the signal energy ratio between the two stations is determined.

[0148] In combination with a scene example, each station includes multiple channels, the signal energy corresponding to each channel in the two stations can be calculated first, then the signal energies of all channels in the station are superimposed to obtain the total signal energy of the station, and then the total signal energies of the two stations are compared to obtain the signal energy ratio between the two stations. For example, for channel i, the seismic data of channel i is The signal energy corresponding to channel i can be calculated by the following sixth formula:

[0149] Sixth formula:

[0150] Through the above sixth formula, the signal energy corresponding to each channel in the station can be obtained, then the total signal energies of the two stations can be obtained by superimposing the signal energies corresponding to each channel in the station, and the signal energy ratio between the two stations is the ratio between E1 and E2.

[0151] S806, if the signal energy ratio is within a preset range, the seismic wave in the data packet is determined as a valid seismic event, otherwise, the seismic event corresponding to the data packet is determined as an invalid seismic event.

[0152] In combination with the scene example, the signal energy ratio in the preset range can be determined according to the actual situation. In the example, the preset range is determined as 0.3-2.8. When the signal energy ratio between the two stations is in the range of 0.3-2.8, it can be determined that the seismic wave in the data packet is determined as an effective seismic event. Otherwise, if the signal energy ratio between the two stations is not in the range of 0.3-2.8, it can be determined that the seismic wave in the data packet is determined as an invalid seismic event.

[0153] S807, if the seismic wave in the data packet is an effective seismic wave signal and the corresponding seismic event is an effective seismic event, the data packet is determined as effective seismic event data.

[0154] In combination with the scene example, according to the above two judgment conditions, when the seismic wave in the data packet meets the conditions of being an effective seismic wave signal and being an effective seismic event at the same time, the seismic wave in the data packet can be determined as effective seismic event data.

[0155] S808, if the seismic wave in the data packet is an invalid seismic wave signal and the corresponding seismic event is an invalid seismic event, the data packet is determined as noise data.

[0156] In combination with the scene example, the data in the data packet other than the effective seismic event data can be determined as noise data.

[0157] Based on the method provided in the example, the effective seismic event data and the noise data can be accurately distinguished.

[0158] Figure 9 Flowchart of the seismic data processing method provided in the application Figure Seven Each station includes at least one channel, and the attribute information further includes a station number and a channel number;

[0159] Correspondingly, as shown in Figure 9 , it further includes:

[0160] S901, according to the station number and the channel number, a subject queue corresponding to each channel under each station is created.

[0161] In combination with a scene example, the data buffering and distribution module can store the data after three-stage data processing in a message middleware for buffering, and the message middleware can be Kafka. Kafka adopts efficient disk sequential reading and writing and zero-copy technology, can process a large amount of message data, has a single-machine ten-thousand-level high throughput, and the high throughput characteristic can ensure fast reading and writing of the seismic monitoring data and can avoid data accumulation, thereby ensuring real-time performance of the seismic monitoring data aggregation. The data buffered in Kafka can be distributed to different topics according to different data types. In this example, corresponding topics can be created according to station numbers and channel numbers, and an independent topic is created for each channel corresponding to each station in each site.

[0162] S902, distribute the compressed seismic data or the compressed environmental noise data to the topic queue of the corresponding channel of the corresponding station according to the corresponding station number and the channel number.

[0163] In combination with a scene example, the data buffering and distribution module is first initialized, the Kafka address is configured, and then the seismic data of each channel is distributed to the topic corresponding to the channel in Kafka to distinguish the seismic data. In addition, each topic can be further divided into multiple partitions. The partition is a physical storage unit, the messages in the partition are ordered, and each message has a unique offset. The existence of the partition enables Kafka to realize horizontal expansion, improve the throughput and concurrent processing capacity of the system.

[0164] Based on the method provided in this example, Kafka is used as the message middleware, the high throughput characteristic of Kafka is used to ensure fast reading and writing of the seismic monitoring data, data accumulation is avoided, and real-time performance of the seismic monitoring data aggregation is ensured.

[0165] Optionally, outputting the compressed seismic data or the compressed environmental noise data includes:

[0166] Displaying the compressed seismic data or the compressed environmental noise data according to the corresponding topic queue.

[0167] In combination with a scene example, the data buffering and distribution module creates a waveform display channel for each channel of each station on the display terminal, and uses the first-in first-out feature of the queue to realize updating of the seismic waveform. The data buffering and distribution module can distribute data of different topics to the waveform display channel corresponding to the topic, convert the data into a line waveform in combination with the Echarts component library, and when the maximum length of the channel queue is reached, pop out the earliest part of the data to make the latest data enter the display, thereby realizing updating of the seismic waveform and finally realizing aggregation and visual display of the multi-site multi-source seismic monitoring data on the terminal.

[0168] Figure 10 A structural schematic diagram of a seismic data processing device provided in the present application is shown in FIG. 1, which comprises: Figure 10

[0169] A data acquisition and analysis module 101 is configured to acquire original seismic data.

[0170] A three-stage data processing module 102 is configured to perform three-stage data processing on the original seismic data to obtain compressed seismic data.

[0171] A data buffering and distribution module 103 is configured to output the compressed seismic data.

[0172] Optionally, the data acquisition and analysis module 101 is specifically configured to extract seismic data from at least one preset site in a multi-thread parallel processing manner to obtain the original seismic data, wherein the site comprises at least one station, and the threads and the stations are in one-to-one correspondence.

[0173] Optionally, the three-stage data processing module 102 is further configured to divide seismic data corresponding to each station to obtain one or more data blocks corresponding to each station.

[0174] The three-stage data processing module 102 is further configured to analyze the data blocks to obtain attribute information corresponding to the data blocks.

[0175] The three-stage data processing module 102 is further configured to pack the attribute information corresponding to the data blocks to obtain data packets in a preset format.

[0176] Optionally, the three-stage data processing module 102 is specifically configured to determine data packets corresponding to each site.

[0177] The three-stage data processing module 102 is further configured to perform first-stage processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval in the data packets.

[0178] The three-stage data processing module 102 is further configured to perform second-stage processing of spatial double-station mutual recognition on the data packets corresponding to any two sites to determine a type of the data packets, wherein the type of the data packets comprises effective seismic event data and environmental noise data.

[0179] The three-stage data processing module 102 is further configured to perform third-stage processing of lossless compression on the effective seismic event data to obtain compressed seismic data.

[0180] The three-stage data processing module 102 is further configured to perform lossy compression on the environmental noise data to obtain compressed environmental noise data.​

[0181] The data buffering and distribution module 103 is further configured to output the compressed ambient noise data.

[0182] Optionally, the attribute information comprises an extraction timestamp.

[0183] Correspondingly, the tertiary data processing module 102 is further configured to divide the data packets corresponding to each station in a preset time window to obtain time series data corresponding to each station in each time window.

[0184] The tertiary data processing module 102 is further configured to determine one of the time series data as first time series data and determine each time series data other than the first time series data as second time series data in each time window.

[0185] The tertiary data processing module 102 is further configured to align each second time series data with the first time series data to obtain an extraction timestamp repetition rate corresponding to each second time series data.

[0186] The tertiary data processing module 102 is further configured to determine the second time series data corresponding to an extraction timestamp repetition rate less than a first preset value as the data in the low confidence interval.

[0187] Optionally, the tertiary data processing module 102 is further configured to obtain an actual time delay of the seismic wave between the two stations in the data packet.

[0188] The tertiary data processing module 102 is further configured to determine a spatial distance between the two stations and a wave velocity of the seismic first arrival wave.

[0189] The tertiary data processing module 102 is further configured to determine a time delay range corresponding to the two stations by the spatial distance between the two stations and the wave velocity of the seismic first arrival wave.

[0190] The tertiary data processing module 102 is further configured to determine the seismic wave in the data packet as an effective seismic wave signal if the actual time delay is within a preset proportion of the time delay range, and otherwise, determine the seismic wave in the data packet as an invalid seismic wave signal.

[0191] The tertiary data processing module 102 is further configured to determine a signal energy ratio between the two stations.

[0192] The tertiary data processing module 102 is further configured to determine the seismic wave in the data packet as an effective seismic event if the signal energy ratio is within a preset range, and otherwise, determine the seismic event corresponding to the data packet as an invalid seismic event.

[0193] The third data processing module 102 is further configured to determine the data packet as valid seismic event data if the seismic wave in the data packet is a valid seismic wave signal and the corresponding seismic event is a valid seismic event.

[0194] The third data processing module 102 is further configured to determine the data packet as environmental noise data if the seismic wave in the data packet is an invalid seismic wave signal and the corresponding seismic event is an invalid seismic event.

[0195] Optionally, each station includes at least one channel, and the attribute information further includes a station number and a channel number.

[0196] Correspondingly, the third data processing module 102 is further configured to create a subject queue corresponding to each channel of each station according to the station number and the channel number.

[0197] The third data processing module 102 is further configured to distribute the compressed seismic data or the compressed environmental noise data to the subject queue of the corresponding channel of the corresponding station according to the corresponding station number and channel number.

[0198] Optionally, the third data processing module 102 is further configured to obtain the corresponding compressed seismic data or compressed environmental noise data from different subject queues, and display the compressed seismic data or compressed environmental noise data according to the corresponding subject queue.

[0199] The seismic data processing apparatus provided in the embodiment can execute the method provided in the method embodiment, and has similar implementation principles and technical effects, which will not be described here.

[0200] Figure 11 A structural schematic diagram of a computer device provided in the present application is shown in FIG. 1. As shown in FIG. 1, the computer device 50 provided in the embodiment includes at least one processor 501 and a memory 502. Optionally, the computer device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected through a bus. Figure 11

[0201] In the specific implementation process, the at least one processor 501 executes the computer execution instructions stored in the memory 502, so that the at least one processor 501 executes the method described above.

[0202] The specific implementation process of the processor 501 can refer to the method embodiment described above, and has similar implementation principles and technical effects, which will not be described here.

[0203] ​In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0204] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0205] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0206] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above method.

[0207] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when a processor executes the computer execution instructions, the above method is implemented.

[0208] The above readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0209] An example readable storage medium is coupled to the processor such that the processor can read information from the readable storage medium and can write information to the readable storage medium. Of course, the readable storage medium can also be a part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0210] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0211] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0212] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0213] If the functions are realized in the form of software functional units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0214] Those skilled in the art can understand that all or part of the steps of the foregoing method embodiments can be completed by a program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the foregoing method embodiments when executed; and the foregoing storage medium includes various media capable of storing program codes, such as ROM, RAM, magnetic disk, or optical disk.

[0215] Finally, it should be noted that other embodiments of the present application will readily occur to those skilled in the art upon consideration of the specification and practice of the present application disclosed herein. The present application is intended to include all such variations and modifications in keeping with the principles of the present application and including such steps in addition to those which are presently preferred, to the full extent of the prior art, to the practice of the application disclosed herein, and to the practice of the application which are adapted to the particular specifications and conditions known as art to the present application. The scope of the application is to be limited only by the claims.

Claims

1. A method of seismic data processing, characterized by, The method comprises the following steps: acquiring original seismic data; performing three-level data processing on the original seismic data to obtain compressed seismic data; outputting the compressed seismic data.

2. The method of claim 1, wherein, The step of acquiring original seismic data comprises the following steps: extracting seismic data from at least one preset site to obtain the original seismic data by using a multi-thread parallel processing mode, wherein the site comprises at least one station, and the thread and the station are in one-to-one correspondence.

3. The method of claim 2, wherein, After the step of acquiring original seismic data, the method further comprises the following steps: dividing the seismic data corresponding to each station to obtain one or more data blocks corresponding to each station; parsing the data blocks to obtain attribute information corresponding to the data blocks; packing the attribute information corresponding to the data blocks to obtain data packets in a preset format.

4. The method of claim 3, wherein, The step of performing three-level data processing on the original seismic data to obtain compressed seismic data comprises the following steps: determining data packets corresponding to each site; performing first-level processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval in the data packets; performing second-level processing of spatial double-station mutual recognition on the data packets corresponding to any two sites to determine the type of the data packets, wherein the type of the data packets comprises valid seismic event data and environmental noise data; performing third-level processing of lossless compression processing on the valid seismic event data to obtain compressed seismic data.

5. The method of claim 4, wherein, The method further comprises the following steps: performing lossy compression processing on the environmental noise data to obtain compressed environmental noise data; outputting the compressed environmental noise data.

6. The method of claim 4, wherein, The attribute information comprises an extraction timestamp. Correspondingly, the step of performing first-level processing of time sequence alignment on the data packets corresponding to each station to remove data in a low confidence interval in the data packets comprises the following steps: dividing the data packets corresponding to each station by using a preset time window to obtain time sequence data corresponding to each station under each time window; determining one of the time sequence data as first time sequence data and determining each time sequence data other than the first time sequence data as second time sequence data under each time window; aligning each second time sequence data with the first time sequence data to obtain an extraction timestamp repetition rate corresponding to each second time sequence data; determining second time sequence data with a corresponding extraction timestamp repetition rate less than a first preset value as the data in the low confidence interval.

7. The method of claim 4, wherein, The step of performing second-level processing of spatial double-station mutual recognition on the data packets corresponding to any two sites to determine the type of the data packets comprises the following steps: acquiring an actual time delay of seismic waves in the data packets between the any two sites; determining a spatial distance between the two sites and a wave velocity of a seismic first arrival wave; determining a time delay range of seismic waves between the two sites through the spatial distance between the two sites and the wave velocity of the seismic first arrival wave; if the actual time delay is located within a preset proportion of the time delay range, determining the seismic waves in the data packets as valid seismic wave signals, otherwise, determining the seismic waves in the data packets as invalid seismic wave signals. determining a signal energy ratio between the two stations; if the signal energy ratio is within a preset range, determining the seismic wave in the data packet as a valid seismic event, otherwise, determining the seismic event corresponding to the data packet as an invalid seismic event; if the seismic wave in the data packet is a valid seismic wave signal and the corresponding seismic event is a valid seismic event, determining the data packet as valid seismic event data; if the seismic wave in the data packet is an invalid seismic wave signal and the corresponding seismic event is an invalid seismic event, determining the data packet as environmental noise data.

8. The method according to any one of claims 3-7, characterized in that, Each station includes at least one channel, and the attribute information further includes a station number and a channel number; Correspondingly, it further includes: According to the station number and the channel number, a corresponding topic queue of each channel under each station is created; The compressed seismic data or the compressed environmental noise data is distributed to the topic queue of the corresponding channel under the corresponding station according to the corresponding station number and the channel number.

9. The method of claim 8, wherein, Outputting the compressed seismic data or the compressed environmental noise data includes: Outputting the compressed seismic data or the compressed environmental noise data according to the corresponding topic queue.

10. A seismic data processing apparatus, characterized by comprising: It includes: A data acquisition and analysis module for acquiring original seismic data; A three-level data processing module for performing three-level data processing on the original seismic data to obtain compressed seismic data; A data buffering and distribution module for outputting the compressed seismic data.

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