Method, device and system for detecting consistency of large-scale seismic signal acquisition equipment

Abstract

The application discloses a large-scale seismic signal acquisition equipment consistency detection method, device and system, and relates to the technical field of geophysical exploration. The method is executed by a computer device in a large-scale seismic signal acquisition equipment consistency detection system, and after receiving background noise original data continuously and synchronously collected by a plurality of seismic signal acquisition equipment, the cumulative power spectrum variance value of each acquisition equipment is obtained through pretreatment and multi-step calculation in sequence, and finally, according to the cumulative power spectrum variance value of each acquisition equipment and the distance from each acquisition equipment to the center position, the acquisition equipment with abnormal relationship in the relationship dimension of the cumulative power spectrum variance and the collection time or the relative distance is determined. Through the noise power density analysis of the background noise data, the consistency detection of the large-scale seismic signal acquisition equipment can be realized efficiently, reliably and automatically, and the detection result can accurately reflect the performance state of the seismograph.

Description

Technical Field

[0001] This invention belongs to the field of geophysical exploration technology, specifically relating to a method, device, and system for consistency testing of large-scale seismic signal acquisition equipment. Background Technology

[0002] Nodal seismographs are existing instruments used for earthquake monitoring and research, and their principles and applications are of great significance in modern seismology. Nodal seismographs acquire and transmit seismic wave signals by deploying multiple seismic sensor nodes in the Earth's crust, thereby enabling real-time monitoring and analysis of seismic activity.

[0003] Compared to traditional wired instruments, nodal seismographs exhibit revolutionary advantages and characteristics, featuring a simple system architecture, no need for connecting cables between acquisition nodes, no limitations on the number of channels, and no deployment restrictions. This results in higher data acquisition efficiency for exploration operations and makes them more suitable for high-density seismic acquisition applications, representing the future direction of geophysical acquisition instruments. Nodal seismographs are widely applicable in both active and passive source seismic exploration and are showing an accelerated development trend, particularly in fields such as oil / gas resource exploration, mineral resource exploration, and urban underground space surveying.

[0004] The performance of seismic signal acquisition equipment, such as nodal seismographs, is crucial to the entire exploration operation. The quality of their data acquisition directly determines the quality of seismic data, thus affecting the efficiency of seismic exploration and the accuracy of data processing results. When using nodal seismographs on a large scale, it is essential to pay close attention to equipment performance testing and maintenance. For example, regular performance testing can promptly identify and eliminate potential faulty nodes, ensuring the data acquisition accuracy and consistency of each nodal seismograph, thereby guaranteeing the reliability and accuracy of the entire acquisition system. Therefore, research on performance testing schemes for nodal seismographs is highly necessary.

[0005] Currently, performance testing solutions for seismic detectors (electromechanical conversion devices that convert seismic waves transmitted to the ground or water into electrical signals, and are key components for seismograph field data acquisition) in seismic signal acquisition equipment cover both indoor precision testing and field verification. In terms of indoor precision testing, existing solutions primarily focus on a detailed evaluation of the basic electrical performance and sensor characteristics of the seismic detector and its acquisition system. This involves simulating or generating standard excitation signals (such as sine waves or pulse waves) to test the sensor's sensitivity, dynamic range, frequency response, phase characteristics, and the signal-to-noise ratio and linearity of the acquisition channel. By comparing the test results with established technical parameter standards, detectors with performance abnormalities or that do not meet requirements can be effectively screened out, providing quality assurance for subsequent field deployment. In the field of indoor precision testing, existing patent CN115542424A provides a method for testing seismic detectors based on the seismic wavelet principle, existing patent CN117891001A provides a digital detector testing system, and existing patent CN109283598A provides a vibration excitation system and method for a detector tester. In terms of on-site verification, existing testing schemes focus on performance verification in real earthquake monitoring environments, mainly including the following technical means: (1) Using active source records to detect the performance of geophones, for example, deploying seismic signal acquisition equipment in concentric circles in the test area, and exciting active sources at the center of the circle (e.g., hammering or using a controllable source vehicle), collecting and analyzing active source event records to detect whether the sensor has a short circuit or open circuit and to judge the consistency of waveform polarity; or using the box wave test method, placing multiple geophones in a very small space, and using active source event records to calibrate the sensitivity and tolerance parameters of the geophones; (2) Using natural earthquake source records to analyze the consistency of equipment, that is, deploying seismic signal acquisition equipment in clusters and performing noise power density analysis on the acquired data, which can evaluate the consistency between geophones and their response to environmental noise; the aforementioned noise power density analysis is particularly suitable for the performance verification of broadband or short-period seismographs, which is especially important for evaluating the noise characteristics of seismic stations and conducting seismograph self-noise tests. Regarding field verification, existing patents CN108549113A provides a method and apparatus for detecting detector performance, CN112485737A provides a method and system for detecting detector performance, and CN107153224A provides a comprehensive test and evaluation method for detector dynamic performance. Furthermore, researchers have established a unified detector performance evaluation system applicable to all types of detectors and capable of comparing performance with that of an ideal detector.

[0006] To meet the demands of high-resolution and high-density exploration, large-scale seismic signal acquisition systems with tens of thousands or even hundreds of thousands of channels will be widely used in seismic exploration. However, current indoor detection schemes face significant limitations. Constrained by detection equipment and site capacity, they can often only handle the detection tasks of a small number of detectors, making it difficult to efficiently and accurately evaluate the acquisition performance of large-scale detectors. The concentric circle deployment method commonly used in the field, while capable of qualitative evaluation of detectors to some extent, has limited depth and accuracy. As for the box wave method, it is currently more used for comparing and evaluating the performance of small-scale / small-range detectors, and both rely on the excitation of active sources, which may increase operational complexity and cost in practical applications.

[0007] As crucial information continuously acquired by seismic signal acquisition equipment, background noise data is increasingly important in the performance evaluation of seismic instruments. In-depth analysis of far-field signals, utilizing the high consistency of signals between adjacent detectors, can effectively detect and evaluate the performance status of detectors, and promptly identify detectors with potential performance anomalies. However, near-field interference noise, prevalent in background noise, is a major problem currently facing detection technology. To mitigate the impact of near-field interference noise, existing detection schemes tend to employ measures such as dense instrument deployment and careful selection of low-noise environments; however, these measures have certain limitations in implementation, and their effectiveness is easily affected by the environment. Furthermore, for noise power density analysis, there is currently a lack of a systematic quantitative evaluation index, which limits the ability to deeply extract seismic instrument performance information from noise data.

[0008] In summary, how to further explore the potential of background noise data and provide a more reliable, efficient, and comprehensive solution for the performance testing of large-scale seismic signal acquisition equipment, so as to accurately reflect the performance status of seismic instruments and provide strong support for the smooth progress of seismic exploration work, is a topic that urgently needs to be studied by those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to provide a method, apparatus, system, computer equipment, computer-readable storage medium, and computer program product for consistency detection of large-scale seismic signal acquisition equipment. This is to achieve efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition equipment by analyzing the noise power density of background noise data, so as to accurately detect abnormal equipment and provide strong support for the smooth progress of seismic exploration work.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] In a first aspect, a method for testing the consistency of large-scale seismic signal acquisition equipment is provided, which is executed by a computer device in a large-scale seismic signal acquisition equipment consistency testing system. The large-scale seismic signal acquisition equipment consistency testing system further includes multiple seismic signal acquisition devices that are uniformly distributed in the experimental site and are respectively communicatively connected to the computer device.

[0012] The consistency detection method for large-scale seismic signal acquisition equipment includes:

[0013] Receive raw background noise data continuously and synchronously acquired by the multiple seismic signal acquisition devices;

[0014] The original background noise data is preprocessed to obtain corresponding new background noise data;

[0015] For each of the multiple seismic signal acquisition devices, the noise power spectral density corresponding to the corresponding device and each acquisition time period is calculated based on the new background noise data corresponding to the corresponding device and each acquisition time period.

[0016] For each of the acquisition devices, the cumulative power spectrum variance value of the corresponding device is extracted based on the noise power spectral density of the corresponding device and the acquisition time period of each unit.

[0017] Based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, the acquisition devices that exhibit anomalies in the relationship between cumulative power spectrum variance and acquisition time or relative distance are identified from the plurality of seismic signal acquisition devices, wherein the distance is calculated based on the known deployment locations of the plurality of seismic signal acquisition devices.

[0018] Based on the above-mentioned invention, a novel scheme for on-site verification and testing of large-scale seismic signal acquisition equipment based on background noise data is provided. This scheme is executed by a computer device within a large-scale seismic signal acquisition equipment consistency detection system. After receiving raw background noise data continuously and synchronously acquired by multiple seismic signal acquisition devices, the system first performs preprocessing and multi-step calculations to obtain the cumulative power spectrum variance value of each acquisition device. Finally, based on the cumulative power spectrum variance value of each acquisition device and the distance from each acquisition device to the deployment center, acquisition devices exhibiting abnormal relationships in the dimension of the relationship between cumulative power spectrum variance and acquisition time or relative distance are identified. Through noise power density analysis of the background noise data, efficient, reliable, and automated consistency testing of large-scale seismic signal acquisition equipment can be achieved. This allows the test results to accurately reflect the performance status of the seismic instruments, facilitating the accurate detection of abnormal equipment and providing strong support for the smooth progress of seismic exploration work, thus enabling practical application and promotion.

[0019] In one possible design, the raw background noise data is preprocessed to obtain corresponding new background noise data, including:

[0020] For each of the multiple seismic signal acquisition devices, determine whether the corresponding raw background noise data has missing data files or abnormal data records. If so, directly determine the corresponding device as an abnormal device and remove the corresponding device from the multiple seismic signal acquisition devices.

[0021] And / or, calculate the average value of the raw background noise data in each unit collection period, and for each unit collection period, subtract the corresponding average value from the raw background noise data collected in the corresponding period.

[0022] And / or, use trend analysis algorithms to remove long-term trend components from the original background noise data;

[0023] And / or, use a filter to remove noise components in the original background noise data that are not in the target frequency band and / or perform downsampling processing on the filtered data.

[0024] In one possible design, for each of the plurality of seismic signal acquisition devices, based on the new background noise data corresponding to the corresponding device and each unit acquisition time period, the noise power spectral density corresponding to the corresponding device and each unit acquisition time period is calculated, including:

[0025] For a specific acquisition device among the plurality of seismic signal acquisition devices, the corresponding background noise new data for each acquisition time period is segmented from the corresponding background noise new data;

[0026] The new background noise data corresponding to a certain acquisition device and a certain unit acquisition time period is windowed to obtain time-domain signals in multiple time windows;

[0027] For each of the multiple time windows, the time-domain signal in the corresponding window is converted to the frequency domain to obtain the corresponding spectrum;

[0028] Based on the multiple spectra corresponding one-to-one with the multiple time windows, the noise power spectral density PSD(f) of a certain acquisition device and corresponding to a certain unit acquisition period is calculated according to the following formula:

[0029]

[0030] In the formula, f represents the frequency, K represents the total number of time windows, k represents a positive integer less than or equal to K, and P k (f) represents the power spectral density corresponding to the k-th time window among the plurality of time windows, N represents the time window and the window length measured by the number of sampling points, X k (f) represents the spectrum corresponding to the k-th time window.

[0031] In one possible design, for each acquisition device, based on the noise power spectral density corresponding to the device and the respective unit acquisition time period, the cumulative power spectral variance value of the corresponding device is extracted, including:

[0032] For each acquisition device, based on the noise power spectral density corresponding to the corresponding device and each acquisition time period, the first cumulative power spectral variance value corresponding to the corresponding device and each acquisition time period is calculated according to the following formula:

[0033]

[0034] In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, and f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period among the plurality of seismic signal acquisition devices. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th device among the plurality of seismic signal acquisition devices and corresponding to the l-th unit acquisition time period;

[0035] And / or, for each of the acquisition devices, based on the noise power spectral density corresponding to the corresponding device and the respective unit acquisition time period, the second cumulative power spectral variance value corresponding to the entire acquisition time period is calculated according to the following formula:

[0036]

[0037] In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, L represents the total number of the unit acquisition time periods in the entire acquisition period, and VFA m This represents the second cumulative power spectrum variance value of the m-th device among the plurality of seismic signal acquisition devices and corresponding to the entire acquisition period, where f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′th device among the plurality of seismic signal acquisition devices and corresponding to the lth unit acquisition time period.

[0038] In one possible design, based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, acquisition devices exhibiting anomalies in the relationship between cumulative power spectrum variance and acquisition time or relative distance are identified from the plurality of seismic signal acquisition devices, including:

[0039] When the first cumulative power spectrum variance value corresponding to each acquisition device and each unit acquisition time period is calculated, firstly, for each unit acquisition time period, based on each acquisition device and the corresponding first cumulative power spectrum variance value, the average value μ and variance value δ of the first cumulative power spectrum variance value are calculated, and the interval [μ-η×δ,μ+η×δ] is taken as the corresponding normal judgment range. Then, for each acquisition device and each unit acquisition time period, if the corresponding device and the corresponding first cumulative power spectrum variance value fall within the normal judgment range of the corresponding time period, the corresponding time period is determined to be the abnormal time period of the corresponding device. Finally, for each acquisition device, if the ratio of the duration of all corresponding abnormal time periods to the duration of the entire acquisition time period is greater than the first preset percentage threshold, the corresponding device is determined to have an abnormal relationship in the dimension of the relationship between cumulative power spectrum variance and acquisition time, where η represents a preset coefficient.

[0040] And / or, when the second cumulative power spectrum variance value corresponding to each acquisition device and the entire acquisition period is calculated, firstly, based on the second cumulative power spectrum variance value corresponding to each acquisition device and the distance from each acquisition device to the deployment center position of the multiple seismic signal acquisition devices, a function model is fitted based on a linear regression model, with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable. Then, for each acquisition device, the distance from the corresponding device to the deployment center position of the multiple seismic signal acquisition devices is imported into the function model, and the second cumulative power spectrum variance value corresponding to the corresponding device and the entire acquisition period is judged based on the output result and the confidence interval based on the second preset percentage threshold. If so, it is determined that the corresponding device has an abnormal relationship in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, wherein the distance is calculated based on the known deployment position of the multiple seismic signal acquisition devices.

[0041] Secondly, a consistency detection device for large-scale seismic signal acquisition equipment is provided, which is arranged in the computer equipment of a large-scale seismic signal acquisition equipment consistency detection system. The large-scale seismic signal acquisition equipment consistency detection system further includes multiple seismic signal acquisition devices that are uniformly arranged in the experimental site and are respectively communicatively connected to the computer equipment.

[0042] The consistency detection device for large-scale seismic signal acquisition equipment includes a raw data receiving unit, a data preprocessing unit, a power spectrum density calculation unit, a power spectrum variance calculation unit, and an abnormal equipment determination unit that are connected in sequence via communication.

[0043] The raw data receiving unit is used to receive raw background noise data continuously and synchronously acquired by the plurality of seismic signal acquisition devices.

[0044] The data preprocessing unit is used to preprocess the original background noise data to obtain corresponding new background noise data;

[0045] The power spectral density calculation unit is used to calculate the noise power spectral density corresponding to each of the multiple seismic signal acquisition devices, based on the new background noise data corresponding to each acquisition device and each acquisition time period.

[0046] The power spectrum variance calculation unit is used to extract the cumulative power spectrum variance value of each acquisition device based on the noise power spectral density of the corresponding device and the acquisition time period of each unit.

[0047] The abnormal device determination unit is used to determine, based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, the acquisition devices that exhibit abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance, wherein the distance is calculated based on the known deployment locations of the plurality of seismic signal acquisition devices.

[0048] Thirdly, the present invention provides a large-scale seismic signal acquisition equipment consistency detection system, including a computer device and multiple seismic signal acquisition devices, wherein the multiple seismic signal acquisition devices are evenly distributed in the experimental site and are respectively communicatively connected to the computer device;

[0049] The computer device is used to execute the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect or any possible design in the first aspect.

[0050] Fourthly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence for communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect or any possible design in the first aspect.

[0051] Fifthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the large-scale seismic signal acquisition device consistency detection method as described in the first aspect or any possible design in the first aspect.

[0052] In a sixth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implement the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect or any possible design in the first aspect.

[0053] The beneficial effects of the above scheme are:

[0054] (1) This invention creatively provides a new scheme for on-site verification and testing of large-scale seismic signal acquisition equipment based on background noise data. The scheme is executed by computer equipment in the consistency detection system of large-scale seismic signal acquisition equipment. After receiving the raw background noise data continuously and synchronously acquired by multiple seismic signal acquisition equipment, the cumulative power spectrum variance of each acquisition equipment is obtained through preprocessing and multi-step calculation. Finally, based on the cumulative power spectrum variance of each acquisition equipment and the distance of each acquisition equipment to the deployment center, the acquisition equipment with abnormal relationship in the dimension of cumulative power spectrum variance and acquisition time or relative distance is determined. In this way, through noise power density analysis of background noise data, the consistency detection of large-scale seismic signal acquisition equipment can be carried out efficiently, reliably and automatically. In this way, the detection results can accurately reflect the performance status of the seismic instrument, which is conducive to accurately discovering abnormal equipment and providing a strong guarantee for the smooth progress of seismic exploration work.

[0055] (2) This solution has low site requirements and does not rely on active excitation sources, which greatly simplifies the field operation process and reduces costs; this source-free excitation requirement enhances its practicality and operability, enabling rapid deployment and effective detection in various complex environments.

[0056] (3) This scheme constructs a quantitative evaluation index system for background noise power spectral density, which can comprehensively, efficiently and with evidence evaluate the performance of large-scale nodal seismograph equipment. This system not only provides quantitative standards for equipment performance, but also ensures the objectivity and comparability of evaluation results, and provides strong data support for the maintenance of equipment throughout its entire life cycle.

[0057] (4) The entire detection process of this solution includes data acquisition, preprocessing, calculation, evaluation model construction and performance testing and evaluation, etc. It has automation and adaptive capabilities, which not only greatly reduces the intervention of manual operation, but also significantly improves detection efficiency and consistency of results.

[0058] (5) This solution can demonstrate high flexibility and scalability. It is not only suitable for the performance testing of large-scale nodal seismograph equipment, but can also be adjusted for performance evaluation of other types of seismic exploration instruments or similar signal acquisition equipment, and has broad application prospects. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating the consistency detection method for large-scale seismic signal acquisition equipment provided in an embodiment of this application.

[0061] Figure 2 This is an example diagram illustrating the deployment of a large-scale seismic signal acquisition device in a square matrix configuration, as provided in an embodiment of this application. Figure 2 Figure (a) shows an example diagram of a square matrix design for a large-scale seismic signal acquisition system. Figure 2 (b) shows a real-world example of a square matrix of a large-scale seismic signal acquisition device.

[0062] Figure 3 This is a design example diagram of a large-scale seismic signal acquisition device deployed in a circular array, provided as an embodiment of this application.

[0063] Figure 4 Example graph showing the noise power spectral density curves of multiple seismic signal acquisition devices provided in the embodiments of this application, corresponding to a unit acquisition period of 10 minutes.

[0064] Figure 5 An example diagram showing the calculation results of the first cumulative power spectrum variance value corresponding to a certain unit acquisition period with a duration of 10 minutes, provided by multiple seismic signal acquisition devices in the embodiments of this application.

[0065] Figure 6 An abnormal state timing diagram of multiple seismic signal acquisition devices provided in the embodiments of this application.

[0066] Figure 7 This is a diagram showing the positional relationship between the second cumulative power spectrum variance and the confidence interval of multiple seismic signal acquisition devices provided in the embodiments of this application and the entire acquisition period.

[0067] Figure 8 This is a schematic diagram of the structure of the consistency detection device for large-scale seismic signal acquisition equipment provided in the embodiments of this application.

[0068] Figure 9 This is a schematic diagram of the structure of a large-scale seismic signal acquisition equipment consistency detection system provided in an embodiment of this application.

[0069] Figure 10 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these embodiments without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0071] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0072] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0073] Example:

[0074] like Figure 1 As shown, the large-scale seismic signal acquisition equipment consistency detection method provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources in a large-scale seismic signal acquisition equipment consistency detection system. The large-scale seismic signal acquisition equipment consistency detection system further includes, but is not limited to, multiple seismic signal acquisition devices uniformly deployed at the experimental site and respectively communicatively connected to the computer device, such as... Figure 9 As shown; specifically, the seismic signal acquisition equipment may be, but is not limited to, nodal seismographs or other types of seismic exploration instruments; the multiple seismic signal acquisition devices are preferably of the same model and have the same operating parameters (e.g., suitable sampling rate and gain parameters), and are preferably arranged closely and evenly in the experimental site. The specific arrangement can be as follows: Figure 2The square matrix arrangement shown (i.e., when the multiple seismic signal acquisition devices include 100 single-component seismic detectors with a natural frequency of 10Hz, arranged in a 10×10 square array, with each single-component seismic detector having a corresponding unique number: #1~#100) can also be as follows: Figure 3 The circular array shown, etc.; the experimental site is preferably a site with stable geological conditions, a flat ground, and good detector coupling effect. For example... Figure 1 As shown, the consistency detection method for large-scale seismic signal acquisition equipment may include, but is not limited to, the following steps S1 to S5.

[0075] S1. Receive raw background noise data continuously and synchronously acquired by the plurality of seismic signal acquisition devices.

[0076] In step S1, the raw background noise data is the electrical signal continuously acquired by the corresponding seismic signal acquisition equipment, which needs to have a certain continuous duration (e.g., not less than 24 hours) to ensure the reliability of subsequent detection results. Furthermore, the raw background noise data can be, but is not limited to, acquired based on the following operating parameters: a sampling rate of 1000Hz, a gain of 0dB, and a total acquisition duration of 66 hours.

[0077] S2. Preprocess the original background noise data to obtain corresponding new background noise data.

[0078] In step S2, the purpose of the preprocessing is to provide a data foundation for subsequent analysis. Specifically, it includes, but is not limited to, steps such as data file retrieval, data anomaly analysis, mean removal, trend removal, filtering, and / or downsampling. In other words, the original background noise data is preprocessed to obtain corresponding new background noise data, including, but not limited to, any one or any combination of the following processing methods (A) to (D).

[0079] (A) For each of the multiple seismic signal acquisition devices, determine whether the corresponding raw background noise data has missing data files or abnormal data records. If so, directly determine the corresponding device as an abnormal device and remove it from the multiple seismic signal acquisition devices. The aforementioned determination process may include, but is not limited to, determining whether there are missing data files through conventional data file retrieval steps, or determining whether there are abnormal data records through conventional data anomaly analysis steps.

[0080] (B) Calculate the average value of the raw background noise data for each unit collection period, and for each unit collection period, subtract the corresponding average value from the raw background noise data collected in the corresponding period. Since the raw background noise data is mainly time-domain signal sampled values, the average value is also the time-domain signal sampled average value, and the result of the subtraction is the time-domain signal sampled difference.

[0081] (C) Use a trend analysis algorithm to remove the long-term trend component from the original background noise data. The aforementioned trend analysis algorithm can be implemented conventionally using, but is not limited to, linear fitting or multinomial fitting methods. The removed long-term trend term is the change term related to the independent variable (specifically, time).

[0082] (D) Use filters to remove noise components in the original background noise data that are not in the target frequency band, and downsample the filtered data. The aforementioned filters may specifically be, but are not limited to, digital filters such as bandpass filters and / or high-pass filters, to preserve the seismic signal in the target frequency band (e.g., 0.01–60 Hz). The purpose of the aforementioned downsampling process (e.g., reducing the sampling rate from 1000 Hz to 100 Hz) is to reduce the amount of data as needed for subsequent analysis, thereby improving processing efficiency.

[0083] S3. For each of the plurality of seismic signal acquisition devices, calculate the noise power spectral density corresponding to the corresponding device and the corresponding acquisition time period based on the new background noise data of the corresponding device and the acquisition time period of each unit.

[0084] In step S3, the specific unit acquisition time period can be predetermined according to the analysis needs, and is generally required to be no less than 5 minutes. Specifically, for each acquisition device among the multiple seismic signal acquisition devices, the noise power spectral density corresponding to the corresponding device and each unit acquisition time period is calculated based on the new background noise data corresponding to the corresponding device and each unit acquisition time period, including but not limited to the following steps S31 to S34.

[0085] S31. For a specific acquisition device among the plurality of seismic signal acquisition devices, segment the corresponding background noise data from the corresponding new background noise data to obtain the new background noise data corresponding to the device and each acquisition time period.

[0086] In step S31, the duration of the unit collection period can be, for example, 10 minutes.

[0087] S32. Window the new background noise data corresponding to a certain acquisition device and a certain unit acquisition time period to obtain time domain signals in multiple time windows.

[0088] In step S32, the windowing process can be conventionally implemented using existing sliding window technology. For example, multiple time windows with a window length of 8192 sampling points (i.e., the window length is measured by the number of sampling points) and an overlap rate of one-eighth can be obtained.

[0089] S33. For each of the multiple time windows, the time-domain signal of the corresponding window is converted to the frequency domain to obtain the corresponding spectrum.

[0090] In step S33, the specific method for converting the time-domain signal to the frequency domain can be, but is not limited to, using the existing Fast Fourier Transform algorithm or similar algorithms. The specific transformation formula will not be elaborated here.

[0091] S34. Based on the multiple spectra corresponding one-to-one with the multiple time windows, the noise power spectral density PSD(f) corresponding to a certain acquisition device and a certain unit acquisition time period is calculated according to the following formula:

[0092]

[0093] In the formula, f represents the frequency, K represents the total number of time windows, k represents a positive integer less than or equal to K, and P k (f) represents the power spectral density corresponding to the k-th time window among the plurality of time windows, N represents the time window and the window length measured by the number of sampling points, X k (f) represents the spectrum corresponding to the k-th time window.

[0094] In step S34, averaging the power spectral density over all time windows aims to reduce the impact of random noise. Furthermore, based on... Figure 2 The multiple seismic signal acquisition devices shown are illustrated with curves for a specific acquisition period of 10 minutes, and the corresponding noise power spectral density (PSD(f)) is given below. Figure 4 As shown.

[0095] S4. For each acquisition device, extract the cumulative power spectrum variance value of the corresponding device based on the noise power spectral density of the corresponding device and the acquisition time period of each unit.

[0096] In step S4, the cumulative power spectral variance is used as a key feature parameter, specifically including but not limited to the first cumulative power spectral variance value corresponding to each acquisition device and each unit acquisition time period, and / or the second cumulative power spectral variance value corresponding to each acquisition device and the entire acquisition time period. Specifically, for each acquisition device, the cumulative power spectral variance value of the corresponding device is extracted based on the noise power spectral density corresponding to each unit acquisition time period. This includes, but is not limited to: for each acquisition device, the first cumulative power spectral variance value corresponding to each unit acquisition time period is calculated according to the following formula based on the noise power spectral density corresponding to the corresponding device and each unit acquisition time period:

[0097]

[0098] In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, and f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period among the plurality of seismic signal acquisition devices. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th seismic signal acquisition device among the plurality of acquisition devices, corresponding to the l-th unit acquisition time period; and / or, for each acquisition device, the second cumulative power spectral variance value corresponding to the entire acquisition time period is calculated according to the following formula based on the noise power spectral density of the corresponding device and the respective unit acquisition time period:

[0099]

[0100] In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, L represents the total number of the unit acquisition time periods in the entire acquisition period, and VFA m This represents the second cumulative power spectrum variance value of the m-th device among the plurality of seismic signal acquisition devices and corresponding to the entire acquisition period, where f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th seismic signal acquisition device among the plurality of seismic signal acquisition devices, corresponding to the l-th unit acquisition time period. Based on Figure 2 Examples of the calculation results of the first cumulative power spectrum variance value corresponding to the multiple seismic signal acquisition devices shown are as follows: Figure 5 As shown.

[0101] S5. Based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, identify the acquisition devices from the plurality of seismic signal acquisition devices that have an abnormal relationship in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance, wherein the distance is calculated based on the known deployment location of the plurality of seismic signal acquisition devices.

[0102] In step S5, the known deployment locations of the multiple seismic signal acquisition devices can be accurately measured during deployment using GPS (Global Positioning System) or other positioning technologies; for example, if the known deployment location of the m-th device is (x m ,y m If the distance D from the m-th device to the center of the deployment of the plurality of seismic signal acquisition devices is given, then... m It is expressed as follows:

[0103]

[0104] In the formula, (x0, y0) represents the center location of the deployment of the multiple seismic signal acquisition devices, which can be obtained by averaging the known deployment locations of the multiple seismic signal acquisition devices. Since the specific content of the cumulative power spectrum variance value differs, the specific determination process for the aforementioned relationship anomalies will also differ. Specifically, based on the cumulative power spectrum variance value of each acquisition device and the distance from each acquisition device to the center location of the multiple seismic signal acquisition devices, acquisition devices exhibiting relationship anomalies in the dimension of the relationship between the cumulative power spectrum variance and acquisition time or relative distance are identified from the multiple seismic signal acquisition devices. This includes, but is not limited to, the following methods (E) and / or method (F).

[0105] (E) When the first cumulative power spectrum variance value corresponding to each acquisition device and each unit acquisition time period is calculated, firstly, for each unit acquisition time period, based on the first cumulative power spectrum variance value of each acquisition device, the average value μ and variance value δ of the first cumulative power spectrum variance value are calculated, and the interval [μ-η×δ, μ+η×δ] is taken as the corresponding normal judgment range. Then, for each acquisition device and each unit acquisition time period, if the corresponding device and the corresponding first cumulative power spectrum variance value fall within the normal judgment range of the corresponding time period, the corresponding time period is determined to be an abnormal time period of the corresponding device. Finally, for each acquisition device, if the ratio of the duration of all corresponding abnormal time periods to the duration of the entire acquisition time period is greater than the first preset percentage threshold, the corresponding device is determined to have an abnormal relationship in the dimension of the relationship between cumulative power spectrum variance and acquisition time, where η represents a preset coefficient. The aforementioned preset coefficient η can be preset based on historical experience, for example, a value of 2; the aforementioned first preset percentage threshold can also be preset based on historical experience, for example, a value of 50%. Figure 2 The abnormal state time sequence diagram of the multiple seismic signal acquisition devices shown is as follows: Figure 6 As shown, the three seismic signal acquisition devices numbered #17, #58, and #85 exhibit anomalies in the relationship between cumulative power spectrum variance and acquisition time. Furthermore, according to... Figure 5 Anomalies were also found in three seismic signal acquisition devices numbered #17, #58, and #85.

[0106] (F) When the second cumulative power spectrum variance value corresponding to the entire acquisition period for each acquisition device is calculated, firstly, based on the second cumulative power spectrum variance value corresponding to the entire acquisition period for each acquisition device and the distance from each acquisition device to the deployment center position of the multiple seismic signal acquisition devices, a function model is fitted based on a linear regression model, with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable. Then, for each acquisition device, the distance from the corresponding device to the deployment center position of the multiple seismic signal acquisition devices is imported into the function model, and the output result and a confidence interval based on a second preset percentage threshold are used to determine whether the second cumulative power spectrum variance value corresponding to the entire acquisition period for the corresponding device is an outlier. If so, it is determined that the corresponding device has an abnormal relationship in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, wherein the distance is calculated based on the known deployment positions of the multiple seismic signal acquisition devices. The aforementioned linear regression model is exemplified, but not limited to, as follows:

[0107] VFA m =a×(D m ) b +c

[0108] In the formula, a, b, and c represent the coefficients that can be fitted and determined. The aforementioned second preset percentage threshold can also be preset based on historical experience, for example, a value of 90%. Figure 2 The diagram shown illustrates the positional relationship between the variance of the second cumulative power spectrum and the confidence interval corresponding to the entire acquisition period for the multiple seismic signal acquisition devices. Figure 7 As shown, it can also be found that the three seismic signal acquisition devices numbered #17, #58 and #85 have anomalies in the relationship between the cumulative power spectrum variance and the relative distance.

[0109] In step S5, the acquisition devices that exhibit anomalies in the relationship between the cumulative power spectrum variance and the acquisition time or relative distance are the results obtained from the consistency test of large-scale seismic signal acquisition devices. In other words, they are the faulty devices that are detected and have inconsistencies with other devices. Their numbers and / or known deployment locations can then be output so that maintenance personnel can quickly locate, repair, or replace them.

[0110] Therefore, based on the consistency detection method for large-scale seismic signal acquisition equipment described in steps S1 to S5 above, a new scheme for on-site verification and detection of large-scale seismic signal acquisition equipment based on background noise data is provided. This scheme is executed by a computer device in the large-scale seismic signal acquisition equipment consistency detection system. After receiving raw background noise data continuously and synchronously acquired by multiple seismic signal acquisition devices, the system first performs preprocessing and multi-step calculations to obtain the cumulative power spectrum variance value of each acquisition device. Finally, based on the cumulative power spectrum variance value of each acquisition device and the distance from each acquisition device to the deployment center, acquisition devices exhibiting abnormal relationships in the dimension of the relationship between cumulative power spectrum variance and acquisition time or relative distance are identified. Through noise power density analysis of background noise data, efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition equipment can be achieved. This allows the detection results to accurately reflect the performance status of the seismic instruments, facilitating the accurate detection of abnormal equipment and providing strong support for the smooth progress of seismic exploration work, thus enabling practical application and promotion.

[0111] like Figure 8 As shown, the second aspect of this embodiment provides a virtual device for implementing the large-scale seismic signal acquisition equipment consistency detection method described in the first aspect. The device is arranged in the computer equipment of the large-scale seismic signal acquisition equipment consistency detection system. The large-scale seismic signal acquisition equipment consistency detection system further includes multiple seismic signal acquisition devices that are uniformly arranged in the experimental site and are respectively communicatively connected to the computer equipment.

[0112] The consistency detection device for large-scale seismic signal acquisition equipment includes a raw data receiving unit, a data preprocessing unit, a power spectrum density calculation unit, a power spectrum variance calculation unit, and an abnormal equipment determination unit that are connected in sequence via communication.

[0113] The raw data receiving unit is used to receive raw background noise data continuously and synchronously acquired by the plurality of seismic signal acquisition devices.

[0114] The data preprocessing unit is used to preprocess the original background noise data to obtain corresponding new background noise data;

[0115] The power spectral density calculation unit is used to calculate the noise power spectral density corresponding to each of the multiple seismic signal acquisition devices, based on the new background noise data corresponding to each acquisition device and each acquisition time period.

[0116] The power spectrum variance calculation unit is used to extract the cumulative power spectrum variance value of each acquisition device based on the noise power spectral density of the corresponding device and the acquisition time period of each unit.

[0117] The abnormal device determination unit is used to determine, based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, the acquisition devices that exhibit abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance, wherein the distance is calculated based on the known deployment locations of the plurality of seismic signal acquisition devices.

[0118] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the consistency detection method for large-scale seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0119] like Figure 9 As shown, the third aspect of this embodiment provides a physical system for implementing the large-scale seismic signal acquisition equipment consistency detection method described in the first aspect, including a computer device and multiple seismic signal acquisition devices, wherein the multiple seismic signal acquisition devices are evenly distributed in the experimental site and are respectively communicatively connected to the computer device;

[0120] The computer device is used to execute the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect. The working process, details, and technical effects of the aforementioned system provided in the third aspect of this embodiment can be found in the consistency detection method for large-scale seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0121] like Figure 10 As shown, the fourth aspect of this embodiment provides a computer device for executing the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect. The device includes a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program to execute the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0122] The working process, working details and technical effects of the aforementioned computer equipment provided in the fourth aspect of this embodiment can be found in the consistency detection method for large-scale seismic signal acquisition equipment described in the first aspect, and will not be repeated here.

[0123] This fifth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising the large-scale seismic signal acquisition equipment consistency detection method as described in the first aspect. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the large-scale seismic signal acquisition equipment consistency detection method as described in the first aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0124] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fifth aspect of this embodiment can be found in the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect, and will not be repeated here.

[0125] The sixth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the consistency detection method for large-scale seismic signal acquisition equipment as described in the first aspect. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0126] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for consistency detection of large-scale seismic signal acquisition equipment, characterized in that, The process is performed by computer equipment in a large-scale seismic signal acquisition equipment consistency testing system, wherein the large-scale seismic signal acquisition equipment consistency testing system also includes multiple seismic signal acquisition devices that are uniformly distributed on the experimental site and are respectively communicatively connected to the computer equipment; The consistency detection method for large-scale seismic signal acquisition equipment includes: Receive raw background noise data continuously and synchronously acquired by the multiple seismic signal acquisition devices; The original background noise data is preprocessed to obtain corresponding new background noise data; For each of the multiple seismic signal acquisition devices, the noise power spectral density of the corresponding device and corresponding to each acquisition time period is calculated based on the new background noise data of the corresponding device and corresponding to each acquisition time period. For each acquisition device, based on the noise power spectral density of the corresponding device and corresponding to each unit acquisition time period, the cumulative power spectral variance value of the corresponding device is extracted. Specifically, this includes: for each acquisition device, based on the noise power spectral density of the corresponding device and corresponding to each unit acquisition time period, the first cumulative power spectral variance value of the corresponding device and corresponding to each unit acquisition time period is calculated according to the following formula: In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, and f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period among the plurality of seismic signal acquisition devices. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th seismic signal acquisition device among the plurality of acquisition devices, corresponding to the l-th unit acquisition time period; and / or, for each acquisition device, the second cumulative power spectral variance value corresponding to the entire acquisition time period is calculated according to the following formula based on the noise power spectral density of the corresponding device and corresponding to each unit acquisition time period: In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, L represents the total number of the unit acquisition time periods in the entire acquisition period, and VFA m This represents the second cumulative power spectrum variance value of the m-th device among the plurality of seismic signal acquisition devices and corresponding to the entire acquisition period, where f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th device among the plurality of seismic signal acquisition devices and corresponding to the l-th unit acquisition time period; Based on the cumulative power spectrum variance of each acquisition device or based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, the acquisition devices that exhibit an abnormal relationship in the dimension of the relationship between cumulative power spectrum variance and acquisition time or relative distance are identified from the plurality of seismic signal acquisition devices, wherein the distance is calculated based on the known deployment locations of the plurality of seismic signal acquisition devices.

2. The consistency detection method for large-scale seismic signal acquisition equipment according to claim 1, characterized in that, The original background noise data is preprocessed to obtain corresponding new background noise data, including: For each of the multiple seismic signal acquisition devices, determine whether the corresponding raw background noise data has missing data files or abnormal data records. If so, directly determine the corresponding device as an abnormal device and remove the corresponding device from the multiple seismic signal acquisition devices. And / or, calculate the average value of the raw background noise data in each unit collection period, and for each unit collection period, subtract the corresponding average value from the raw background noise data collected in the corresponding period. And / or, use trend analysis algorithms to remove long-term trend components from the original background noise data; And / or, use a filter to remove noise components in the original background noise data that are not in the target frequency band, or use a filter to remove noise components in the original background noise data that are not in the target frequency band and downsample the filtered data.

3. The consistency detection method for large-scale seismic signal acquisition equipment according to claim 1, characterized in that, For each of the plurality of seismic signal acquisition devices, based on the new background noise data corresponding to the corresponding device and each acquisition time period, the noise power spectral density corresponding to the corresponding device and each acquisition time period is calculated, including: For a specific acquisition device among the plurality of seismic signal acquisition devices, the corresponding background noise new data for that device and corresponding to each unit acquisition time period is obtained from the corresponding background noise new data. The new background noise data corresponding to a certain acquisition device and a certain unit acquisition time period is windowed to obtain time-domain signals in multiple time windows; For each of the multiple time windows, the time-domain signal in the corresponding window is converted to the frequency domain to obtain the corresponding spectrum; Based on the multiple spectra corresponding one-to-one with the multiple time windows, the noise power spectral density PSD(f) of a certain acquisition device and corresponding to a certain unit acquisition period is calculated according to the following formula: In the formula, f represents the frequency, K represents the total number of time windows, k represents a positive integer less than or equal to K, and P k (f) represents the power spectral density corresponding to the k-th time window among the plurality of time windows, N represents the time window and the window length measured by the number of sampling points, X k (f) represents the spectrum corresponding to the k-th time window.

4. The consistency detection method for large-scale seismic signal acquisition equipment according to claim 1, characterized in that, Based on the cumulative power spectrum variance of each acquisition device, or based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, identify acquisition devices from the plurality of seismic signal acquisition devices that exhibit anomalies in the relationship between cumulative power spectrum variance and acquisition time or relative distance, including: When the first cumulative power spectrum variance value corresponding to each acquisition device and each unit acquisition time period is calculated, firstly, for each unit acquisition time period, based on each acquisition device and the corresponding first cumulative power spectrum variance value, the average value μ and variance value δ of the first cumulative power spectrum variance value are calculated, and the interval [μ-η×δ,μ+η×δ] is taken as the corresponding normal judgment range. Then, for each acquisition device and each unit acquisition time period, if the corresponding device and the corresponding first cumulative power spectrum variance value fall within the normal judgment range of the corresponding time period, the corresponding time period is determined to be the abnormal time period of the corresponding device. Finally, for each acquisition device, if the ratio of the duration of all corresponding abnormal time periods to the duration of the entire acquisition time period is greater than the first preset percentage threshold, the corresponding device is determined to have an abnormal relationship in the dimension of the relationship between cumulative power spectrum variance and acquisition time, where η represents a preset coefficient. And / or, when the second cumulative power spectrum variance value corresponding to each acquisition device and the entire acquisition period is calculated, firstly, based on the second cumulative power spectrum variance value corresponding to each acquisition device and the distance from each acquisition device to the deployment center position of the multiple seismic signal acquisition devices, a function model is fitted based on a linear regression model, with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable. Then, for each acquisition device, the distance from the corresponding device to the deployment center position of the multiple seismic signal acquisition devices is imported into the function model, and the second cumulative power spectrum variance value corresponding to the corresponding device and the entire acquisition period is judged based on the output result and the confidence interval based on the second preset percentage threshold. If so, it is determined that the corresponding device has an abnormal relationship in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, wherein the distance is calculated based on the known deployment position of the multiple seismic signal acquisition devices.

5. A consistency detection device for large-scale seismic signal acquisition equipment, characterized in that, The computer equipment is arranged in the consistency testing system for large-scale seismic signal acquisition equipment, wherein the consistency testing system for large-scale seismic signal acquisition equipment also includes multiple seismic signal acquisition devices that are uniformly distributed in the experimental site and are respectively communicatively connected to the computer equipment; The consistency detection device for large-scale seismic signal acquisition equipment includes a raw data receiving unit, a data preprocessing unit, a power spectrum density calculation unit, a power spectrum variance calculation unit, and an abnormal equipment determination unit that are connected in sequence via communication. The raw data receiving unit is used to receive raw background noise data continuously and synchronously acquired by the plurality of seismic signal acquisition devices. The data preprocessing unit is used to preprocess the original background noise data to obtain corresponding new background noise data; The power spectral density calculation unit is used to calculate the noise power spectral density of each of the multiple seismic signal acquisition devices, based on the new background noise data of the corresponding device and corresponding to each unit acquisition time period. The power spectrum variance calculation unit is used to extract the cumulative power spectrum variance value of each acquisition device based on the noise power spectral density of the corresponding device and the corresponding acquisition time period. Specifically, it includes: for each acquisition device, calculating the first cumulative power spectrum variance value corresponding to each acquisition time period based on the noise power spectral density of the corresponding device and the corresponding acquisition time period according to the following formula: In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, and f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period among the plurality of seismic signal acquisition devices. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th seismic signal acquisition device among the plurality of acquisition devices, corresponding to the l-th unit acquisition time period; and / or, for each acquisition device, the second cumulative power spectral variance value corresponding to the entire acquisition time period is calculated according to the following formula based on the noise power spectral density of the corresponding device and corresponding to each unit acquisition time period: In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ represent positive integers less than or equal to M, l represents the sequence number of the unit acquisition time period, L represents the total number of the unit acquisition time periods in the entire acquisition period, and VFA m This represents the second cumulative power spectrum variance value of the m-th device among the plurality of seismic signal acquisition devices and corresponding to the entire acquisition period, where f represents the frequency. This represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition time period. This represents the noise power spectral density of the m-th device and the noise power spectral density corresponding to the l-th unit acquisition time period. This represents the noise power spectral density of the m′-th device among the plurality of seismic signal acquisition devices and corresponding to the l-th unit acquisition time period; The abnormal device determination unit is used to determine, based on the cumulative power spectrum variance of each acquisition device or based on the cumulative power spectrum variance of each acquisition device and the distance from each acquisition device to the deployment center of the plurality of seismic signal acquisition devices, the acquisition device that exhibits an abnormal relationship in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance, wherein the distance is calculated based on the known deployment location of the plurality of seismic signal acquisition devices.

6. A consistency detection system for large-scale seismic signal acquisition equipment, characterized in that, It includes computer equipment and multiple seismic signal acquisition devices, wherein the multiple seismic signal acquisition devices are evenly distributed in the experimental site and are respectively communicatively connected to the computer equipment; The computer device is used to execute the consistency detection method for large-scale seismic signal acquisition equipment as described in any one of claims 1 to 4.

7. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver connected in sequence for communication. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the consistency detection method for large-scale seismic signal acquisition equipment as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the consistency detection method for large-scale seismic signal acquisition equipment as described in any one of claims 1 to 4.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the consistency detection method for large-scale seismic signal acquisition equipment as described in any one of claims 1 to 4.

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