Large-scale seismic signal acquisition equipment consistency detection method, device and system

Abstract

The invention discloses a consistency detection method, device and system for large-scale seismic signal acquisition equipment, and relates to the technical field of geophysical exploration. The method is executed by computer equipment in a large-scale seismic signal acquisition equipment consistency detection system, and after background noise original data continuously and synchronously acquired by a plurality of seismic signal acquisition equipment is received, an accumulated power spectrum variance value of each acquisition equipment is obtained through preprocessing and multi-step calculation in sequence; and finally, according to the accumulated power spectrum variance value of each acquisition device and the distance from each acquisition device to the layout center position, determining the acquisition device with an abnormal relationship in the relationship dimension of the accumulated power spectrum variance and the acquisition time or the relative distance. The purpose of efficient, reliable and automatic consistency detection on large-scale seismic signal acquisition equipment can be achieved, and then the detection result can accurately reflect the performance state of a seismic instrument.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical exploration, and particularly relates to a method, device and system for detecting the consistency of large-scale seismic signal acquisition devices. Background Art

[0002] The nodal seismograph is an existing instrument used for seismic monitoring and research, and its principle and application are of great significance in modern seismology. The nodal seismograph realizes the acquisition and transmission of seismic wave signals by arranging multiple seismic sensor nodes in the earth's crust, so as to realize the real-time monitoring and analysis of seismic activities.

[0003] Compared with traditional cabled instruments, the nodal seismograph exhibits revolutionary advantages and characteristics, such as a simple system architecture, no need to connect cables between acquisition nodes, no channel number limit and no layout limit, etc., making the acquisition efficiency of exploration operation data higher, and it is more suitable for high-density seismic acquisition applications. It is the development direction of the new generation of geophysical exploration acquisition instruments. The nodal seismograph is widely applicable to seismic exploration with active and passive sources, and shows an accelerating development trend, especially in the fields of oil / gas resource exploration, mineral resource exploration and urban underground space survey, etc.

[0004] The performance of seismic signal acquisition devices such as nodal seismographs is crucial for the entire exploration operation. The quality of its data acquisition directly determines the quality of seismic data, and further affects the efficiency of seismic exploration operations and the accuracy of data processing results. When using nodal seismographs on a large scale, it is necessary to attach great importance to the performance detection and maintenance of the devices. For example, through regular performance detection, potential faulty nodes can be discovered and eliminated in time to ensure the data acquisition accuracy and consistency of each nodal seismograph, thereby ensuring the reliability and accuracy of the entire acquisition system. Therefore, it is very necessary to carry out research on the performance detection scheme of nodal seismographs.

[0005] Currently, the performance detection scheme for geophones in seismic signal acquisition equipment (which is an electromechanical conversion device that converts seismic waves transmitted to the ground or water into electrical signals and is a key component for field data acquisition of seismographs) covers two aspects: indoor precision testing and on-site verification. In terms of indoor precision testing, existing detection schemes mainly focus on the detailed evaluation of the basic electrical performance and sensor characteristics of geophones and their acquisition systems. That is, by simulating or generating standard excitation signals (such as sine waves or pulse waves, etc.) to detect electrical performance indicators such as the sensitivity, dynamic range, frequency response, phase characteristics of the sensor, and the signal-to-noise ratio and linearity of the acquisition channel. Then, by comparing the detection results with the established technical parameter standards, geophones with abnormal performance or non-compliance requirements can be effectively screened out, providing quality assurance for subsequent on-site deployment. In terms of indoor precision testing, the existing patent CN115542424A provides a method for testing geophones based on the seismic wavelet principle, the existing patent CN117891001A provides a digital geophone testing system, and the existing patent CN109283598A provides a vibration excitation system and method for a geophone tester. In terms of on-site verification, existing detection schemes focus on performance verification in a real seismic monitoring environment, mainly including the following technical means: (1) Using active source records to achieve the performance detection of geophones. For example, arranging seismic signal acquisition equipment in a concentric circle manner in the test area and exciting an active seismic source (such as hammering or through a vibroseis vehicle, etc.) at the center position, collecting and analyzing the active source event records to detect whether there is a short circuit or open circuit in the sensor 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 the active source event records to calibrate the sensitivity and tolerance parameters of the geophones; (2) Using natural seismic source records to analyze the consistency of the equipment, that is, clustering the deployment of seismic signal acquisition equipment and performing noise power density analysis on the collected data to evaluate the consistency between geophones and their response ability to environmental noise; the aforementioned noise power density analysis is particularly applicable to the performance verification of broadband or short-period seismographs, which is particularly important for evaluating the pedestal noise characteristics of seismic stations and conducting research such as self-noise testing of seismographs. In terms of on-site verification, the existing patent CN108549113A provides a method and device for detecting the performance of geophones, the existing patent CN112485737A provides a method and system for detecting the performance of geophones, and the existing patent CN107153224A provides a comprehensive test and evaluation method for the dynamic performance of geophones. In addition, some researchers have established a unified geophone performance evaluation system, which is applicable to all types of geophones and can be compared with the performance of ideal geophones.

[0006] To meet the requirements of high-resolution and high-density exploration, large-scale seismic signal acquisition systems with tens of thousands or even over a hundred thousand channels will be widely used in seismic exploration. However, the current indoor detection solutions face significant limitations. Limited by detection equipment and site capacity, they can often only handle the detection tasks of a small number of geophones and are difficult to efficiently and accurately evaluate the acquisition performance of large-scale geophones. The commonly used concentric circle layout method in the field can, to a certain extent, qualitatively evaluate geophones, but its evaluation depth and accuracy are limited. As for the box wave method, it is currently more applied to the comparison and evaluation of the performance of geophones in a small range / small scale, and both methods rely on the excitation of active seismic sources, which may increase operational complexity and costs in practical applications.

[0007] As important information continuously collected by seismic signal acquisition equipment, background noise data is becoming increasingly prominent in the performance evaluation of seismic instruments. By deeply analyzing far-field signals, that is, using the high consistency of signals between adjacent geophones, the performance state of geophones can be effectively detected and evaluated, and potential performance-abnormal geophones can be timely discovered and identified. However, the near-field interference noise commonly present in background noise is the main problem faced by current detection technologies. To weaken the influence of near-field interference noise, existing detection solutions tend to take measures such as closely arranging instruments and carefully selecting low-noise environments, but these measures have certain limitations in implementation and their effects are easily affected by the environment. In addition, there is currently a lack of a systematic set of quantitative evaluation indicators for noise power density analysis, which limits the ability to deeply extract performance information of seismic instruments from noise data.

[0008] In summary, how to deeply explore the potential of background noise data, provide a more reliable, efficient, and comprehensive solution for the performance detection of large-scale seismic signal acquisition equipment, accurately reflect the performance state of seismic instruments, and provide strong guarantee for the smooth progress of seismic exploration work is an urgent research topic for those skilled in the art. Summary of the Invention

[0009] The objective of the present invention is to provide a method, device, system, computer device, computer-readable storage medium, and computer program product for the consistency detection of large-scale seismic signal acquisition equipment, so as to achieve the purpose of efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition equipment through noise power density analysis of background noise data, accurately discover abnormal equipment, and provide strong guarantee for the smooth progress of seismic exploration work.

[0010] To achieve the above objective, the present invention adopts the following technical solutions:

[0011] In a first aspect, a method for detecting the consistency of large-scale seismic signal acquisition devices is provided, which is executed by a computer device in a large-scale seismic signal acquisition device consistency detection system. Among them, the large-scale seismic signal acquisition device consistency detection system further includes a plurality of seismic signal acquisition devices that are uniformly arranged in an experimental site and are respectively communicatively connected to the computer device;

[0012] The method for detecting the consistency of large-scale seismic signal acquisition devices includes:

[0013] Receiving the background noise raw data continuously and synchronously collected by the plurality of seismic signal acquisition devices;

[0014] Preprocessing the background noise raw data to obtain corresponding new background noise data;

[0015] For each acquisition device among the plurality of seismic signal acquisition devices, according to the new background noise data corresponding to the corresponding device and each unit acquisition period, calculate the noise power spectral density corresponding to the corresponding device and each unit acquisition period;

[0016] For each of the acquisition devices, according to the noise power spectral density corresponding to the corresponding device and each unit acquisition period, extract the cumulative power spectral variance value corresponding to the corresponding device;

[0017] According to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the layout center position of the plurality of seismic signal acquisition devices, determine, from the plurality of seismic signal acquisition devices, the acquisition devices that have abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance, where the distances are calculated based on the known layout positions of the plurality of seismic signal acquisition devices.

[0018] Based on the above invention content, a new solution for on-site verification and detection of large-scale seismic signal acquisition devices based on background noise data is provided. That is, it is executed by a computer device in a large-scale seismic signal acquisition device consistency detection system. After receiving the original background noise data continuously and synchronously collected by multiple seismic signal acquisition devices, it first sequentially performs preprocessing and multiple-step calculations to obtain the cumulative power spectrum variance values of each acquisition device. Finally, based on the cumulative power spectrum variance values of each acquisition device and the distances from each acquisition device to the layout center position, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance are determined. In this way, through the noise power density analysis of the background noise data, efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition devices can be achieved. Furthermore, the detection results can accurately reflect the performance status of seismic instruments, facilitate the accurate discovery of abnormal devices, and provide strong guarantees for the smooth progress of seismic exploration work, which is convenient for practical application and promotion.

[0019] In a possible design, preprocessing the original background noise data to obtain corresponding new background noise data includes:

[0020] For each acquisition device among the multiple seismic signal acquisition devices, determine whether there is a situation of missing data files or abnormal data records in the corresponding original background noise data. If so, directly determine the corresponding device as an abnormal device and exclude the corresponding device from the multiple seismic signal acquisition devices;

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

[0022] And / or, use a trend analysis algorithm to remove the long-term trend component in the original background noise data;

[0023] And / or, use a filter to remove the noise components in the original background noise data that are in non-target frequency bands and perform downsampling processing on the filtered data.

[0024] In a possible design, for each acquisition device among the multiple seismic signal acquisition devices, calculate the noise power spectral density corresponding to the corresponding device and each unit acquisition period according to the new background noise data corresponding to the corresponding device and each unit acquisition period, including:

[0025] For a certain acquisition device among the multiple seismic signal acquisition devices, segment the corresponding new background noise data from the corresponding new background noise data to obtain the new background noise data corresponding to the corresponding device and each unit acquisition period;

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

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

[0028] According to the multiple spectra corresponding one by one to the multiple time windows, calculate the noise power spectral density PSD(f) corresponding to a certain acquisition device and a certain unit acquisition period according to the following formula:

[0029]

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

[0031] In a possible design, for each acquisition device, according to the noise power spectral density corresponding to the device and each unit acquisition period, extract the cumulative power spectral variance value corresponding to the device, including:

[0032] For each acquisition device, calculate the first cumulative power spectral variance value corresponding to the device and each unit acquisition period according to the noise power spectral density corresponding to the device and each unit acquisition period according to the following formula:

[0033]

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

[0035] And / or, for each of the acquisition devices, according to the noise power spectral density corresponding to the corresponding device and each unit acquisition period, calculate the second cumulative power spectral variance value corresponding to the corresponding device and the entire acquisition period 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' respectively represent positive integers less than or equal to M, l represents the serial number of the unit acquisition period, L represents the total number of the unit acquisition periods in the entire acquisition period, VFA m represents the second cumulative power spectral variance value corresponding to the m-th device in the multiple seismic signal acquisition devices and the entire acquisition period, f represents frequency, represents the first cumulative power spectral variance value corresponding to the m-th device and the l-th unit acquisition period, represents the noise power spectral density corresponding to the m-th device and the l-th unit acquisition period, represents the noise power spectral density corresponding to the m'-th device in the multiple seismic signal acquisition devices and the l-th unit acquisition period.

[0038] In a possible design, according to the cumulative power spectral variance values of each of the acquisition devices and the distances from each of the acquisition devices to the layout center position of the multiple seismic signal acquisition devices, determine the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance from the multiple seismic signal acquisition devices, including:

[0039] When the first cumulative power spectral variance values corresponding to each of the acquisition devices and each of the unit acquisition periods are calculated, first, for each of the unit acquisition periods, calculate the average value μ and variance value δ of the first cumulative power spectral variance values according to each of the acquisition devices, and use the interval [μ - η×δ, μ + η×δ] as the corresponding normal determination range. Then, for each of the acquisition devices and each of the unit acquisition periods, if the first cumulative power spectral variance value corresponding to the corresponding device falls within the normal determination range of the corresponding period, determine that the corresponding period is the abnormal period of the corresponding device. Finally, for each of the acquisition devices, if the ratio of the duration of all the corresponding abnormal periods to the duration of the entire acquisition period is greater than the first preset percentage threshold, determine that the corresponding device has an abnormal relationship in the dimension of the relationship between the cumulative power spectral variance and the acquisition time, where η represents a preset coefficient;

[0040] And / or, when calculating the second cumulative power spectrum variance values corresponding to each acquisition device and the full acquisition time period, first, based on the second cumulative power spectrum variance values corresponding to each acquisition device and the full acquisition time period and the distances from each acquisition device to the layout center position of the multiple seismic signal acquisition devices, a function model with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable is fitted based on a linear regression model. Then, for each acquisition device, the distance from the corresponding device to the layout center position of the multiple seismic signal acquisition devices is imported into the function model, and whether the second cumulative power spectrum variance value corresponding to the corresponding device and the full acquisition time period is an outlier is determined according to the output result and the confidence interval based on the second preset percentage threshold. If so, it is determined that there is a relationship anomaly for the corresponding device in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, where the distance is calculated based on the known layout positions of the multiple seismic signal acquisition devices.

[0041] In a second aspect, a large-scale seismic signal acquisition device consistency detection device is provided, which is arranged in a computer device of a large-scale seismic signal acquisition device consistency detection system. The large-scale seismic signal acquisition device consistency detection system further includes a plurality of seismic signal acquisition devices that are evenly arranged in an experimental site and are respectively communicatively connected to the computer device.

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

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

[0044] The data preprocessing unit is configured to preprocess the background noise raw data to obtain corresponding new background noise data.

[0045] The power spectral density calculation unit is configured to calculate, for each acquisition device among the plurality of seismic signal acquisition devices, the noise power spectral density corresponding to the corresponding device and each unit acquisition time period according to the new background noise data corresponding to the corresponding device and each unit acquisition time period.

[0046] The power spectrum variance calculation unit is configured to extract, for each acquisition device, the cumulative power spectrum variance value corresponding to the corresponding device according to the noise power spectral density corresponding to the corresponding device and each unit acquisition time period.

[0047] The abnormal device determination unit is configured to determine, from the multiple seismic signal acquisition devices, the acquisition devices that have an abnormal relationship in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or the relative distance, according to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the layout center position of the multiple seismic signal acquisition devices, where the distances are calculated based on the known layout positions of the multiple seismic signal acquisition devices.

[0048] In a third aspect, the present invention provides a large-scale seismic signal acquisition device consistency detection system, including a computer device and multiple seismic signal acquisition devices, where the multiple seismic signal acquisition devices are uniformly arranged in an experimental site and are respectively communicatively connected to the computer device;

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

[0050] In a fourth aspect, the present invention provides a computer device, including a memory, a processor, and a transceiver that are communicatively connected in sequence, where the memory is configured to store a computer program, the transceiver is configured to send and receive messages, and the processor is configured to read the computer program and execute the large-scale seismic signal acquisition device consistency detection method as described in the first aspect or any possible design in the first aspect.

[0051] In a fifth aspect, the present invention provides a computer-readable storage medium, on which instructions are stored, and when the instructions are run on a computer, the large-scale seismic signal acquisition device consistency detection method as described in the first aspect or any possible design in the first aspect is executed.

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

[0053] Beneficial effects of the above solution:

[0054] (1) The present invention creatively provides a new solution for on-site verification and detection of large-scale seismic signal acquisition devices based on background noise data. That is, it is executed by a computer device in a large-scale seismic signal acquisition device consistency detection system. After receiving the original background noise data continuously and synchronously collected by multiple seismic signal acquisition devices, it first passes through preprocessing and multiple steps of calculation in sequence 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 layout center position, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance are determined. In this way, through the noise power density analysis of the background noise data, efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition devices can be achieved. Furthermore, the detection results can accurately reflect the performance status of seismic instruments, facilitating the accurate discovery of abnormal devices and providing strong guarantees for the smooth progress of seismic exploration work;

[0055] (2) This solution has low requirements for the site and does not rely on active excitation sources, greatly simplifying the field operation process and reducing costs. This requirement for no seismic source excitation enhances its practicability and operability, enabling rapid deployment and effective detection in various complex environments;

[0056] (3) This solution constructs a quantitative evaluation index system for background noise power spectral density, which can comprehensively, efficiently, and reasonably evaluate the performance of large-scale nodal seismic instruments. This system not only provides a quantitative standard for device performance but also ensures the objectivity and comparability of evaluation results, providing strong data support for the maintenance of the entire life cycle of the device;

[0057] (4) The entire detection process of this solution includes data acquisition, preprocessing, calculation, evaluation model construction, and performance detection and evaluation, etc. It has automation and adaptability capabilities, not only greatly reducing the intervention of manual operations but also significantly improving the detection efficiency and result consistency;

[0058] (5) This solution can demonstrate extremely high flexibility and scalability. It is not only suitable for the performance detection of large-scale nodal seismic instruments but can also be adjusted and applied to the performance evaluation of other types of seismic exploration instruments or similar signal acquisition devices, with broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0060] Figure 1 It is a schematic flowchart of the method for detecting the consistency of large-scale seismic signal acquisition devices provided by the embodiments of the present application.

[0061] Figure 2 It is an example diagram of arranging large-scale seismic signal acquisition devices in a square matrix provided by the embodiments of the present application, where Figure 2 Figure (a) shows an example design diagram of the square matrix of large-scale seismic signal acquisition devices. Figure 2 Figure (b) shows an example actual scene diagram of the square matrix of large-scale seismic signal acquisition devices.

[0062] Figure 3 It is an example design diagram of arranging large-scale seismic signal acquisition devices in a circular array provided by the embodiments of the present application.

[0063] Figure 4 It is an example diagram of the curve of the noise power spectral density corresponding to multiple seismic signal acquisition devices and a certain unit acquisition period with a duration of 10 minutes provided by the embodiments of the present application.

[0064] Figure 5 It is an example diagram of the calculation result of the first cumulative power spectral variance value corresponding to multiple seismic signal acquisition devices and a certain unit acquisition period with a duration of 10 minutes provided by the embodiments of the present application.

[0065] Figure 6 It is a time sequence diagram of the abnormal states of multiple seismic signal acquisition devices provided by the embodiments of the present application.

[0066] Figure 7 It is a position relationship diagram between the second cumulative power spectral variance value and the confidence interval corresponding to multiple seismic signal acquisition devices and the whole acquisition period provided by the embodiments of the present application.

[0067] Figure 8 It is a schematic structural diagram of the device for detecting the consistency of large-scale seismic signal acquisition devices provided by the embodiments of the present application.

[0068] Figure 9 It is a schematic structural diagram of the system for detecting the consistency of large-scale seismic signal acquisition devices provided by the embodiments of the present application.

[0069] Figure 10 It is a schematic structural diagram of the computer device provided by the embodiments of the present application. Detailed implementation manners

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the accompanying drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can be obtained based on these embodiments. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation to the present invention.

[0071] It should be understood that although terms such as 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 can be called the second object, and similarly the second object can be called the first object, without departing from the scope of the exemplary embodiments of the present invention.

[0072] It should be understood that for the term "and / or" that may appear in this text, it is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously; another example, A, B and / or C can mean any one of A, B and C or any combination of them; for the term " / and" that may appear in this text, it is a description of another association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone or A and B exist simultaneously; in addition, for the character " / " that may appear in this text, generally it means that the front and rear associated objects are an "or" relationship.

[0073] Embodiment:

[0074] As Figure 1 shown, the method for detecting the consistency of the large-scale seismic signal acquisition devices provided in the first aspect of this embodiment can be, but is not limited to, executed by a computer device in the large-scale seismic signal acquisition device consistency detection system that has certain computing resources. Among them, the large-scale seismic signal acquisition device consistency detection system further includes, but is not limited to, a plurality of seismic signal acquisition devices that are evenly arranged in the experimental site and are respectively communicatively connected to the computer device, as Figure 9 shown; specifically, the seismic signal acquisition device can be, but is not limited to, a nodal seismograph or other types of seismic exploration instruments, etc.; the plurality of seismic signal acquisition devices are preferably of the same model and set with the same working parameters (such as appropriate sampling rate and gain and other parameters), and are preferably closely and evenly arranged in the experimental site. The specific arrangement method can be as Figure 2The square matrix arrangement shown (i.e., when the multiple seismic signal acquisition devices include 100 single-component geophones with a natural frequency of 10 Hz, they are arranged in a 10×10 square matrix, and each single-component geophone has a corresponding unique number: #1 to #100), or it can be the circular array arrangement as shown in Figure 3 etc.; The experimental site preferably has stable geological conditions, a flat ground, and good coupling effect of the geophones. As shown in Figure 1 As shown, the method for detecting the consistency of the large-scale seismic signal acquisition devices may but is not limited to including the following steps S1 to S5.

[0075] S1. Receive the background noise raw data continuously and synchronously collected by the multiple seismic signal acquisition devices.

[0076] In step S1, the background noise raw data is the electrical signal continuously collected by the corresponding seismic signal acquisition device, which needs to have a certain continuous duration (for example, not less than 24 hours) to ensure the reliability of the subsequent detection results. In addition, the background noise raw data may but is not limited to be collected based on the following working parameters: the sampling rate is 1000 Hz, the gain is 0 dB, and the continuous duration of the entire acquisition period is 66 hours.

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

[0078] In step S2, the purpose of the preprocessing is to provide a data basis for subsequent analysis, specifically including but not limited to steps such as data file retrieval, data anomaly analysis, de-meaning, detrending, filtering, and / or downsampling. That is, specifically, preprocess the background noise raw data to obtain the corresponding new background noise data, including but not limited to any one of the following processing methods (A) to (D) or any combination of them.

[0079] (A) For each acquisition device among the multiple seismic signal acquisition devices, judge whether there is a data file missing situation or a data record anomaly situation in the corresponding background noise raw data. If so, directly determine the corresponding device as an abnormal device and exclude the corresponding device from the multiple seismic signal acquisition devices. The foregoing judgment process may but is not limited to specifically include: judging whether there is a data file missing situation through a conventional data file retrieval step, or judging whether there is a data record anomaly situation through a conventional data anomaly analysis step.

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

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

[0082] (D) Use a filter to remove the noise components in the original background noise data that are in non-target frequency bands / and perform downsampling processing on the filtered data. The aforementioned filter can specifically but not limited to be a digital filter such as a band-pass filter and / or a high-pass filter, etc., in order to retain the seismic signals in the target frequency band (for example, 0.01~60Hz). The purpose of the aforementioned downsampling processing (for example, the sampling rate is reduced from 1000Hz to 100Hz) is to reduce the data volume according to the subsequent analysis needs, so as to improve the processing efficiency.

[0083] S3. For each acquisition device among the multiple seismic signal acquisition devices, calculate the noise power spectral density corresponding to the device and corresponding to each unit acquisition period according to the new background noise data corresponding to the device and each unit acquisition period.

[0084] In the step S3, the unit acquisition period can be specifically determined in advance according to the analysis needs, and generally it is required to be not less than 5 minutes. Specifically, for each acquisition device among the multiple seismic signal acquisition devices, calculate the noise power spectral density corresponding to the device and corresponding to each unit acquisition period according to the new background noise data corresponding to the device and each unit acquisition period, including but not limited to the following steps S31~S34.

[0085] S31. For a certain acquisition device among the multiple seismic signal acquisition devices, segment the corresponding new background noise data from the corresponding new background noise data to obtain the new background noise data corresponding to the device and corresponding to each unit acquisition period.

[0086] In the step S31, the duration of the unit acquisition period can be exemplified as 10 minutes.

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

[0088] In the step S32, the windowing process can be conventionally implemented by using the existing sliding window technique. For example, the plurality of 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 plurality of time windows, convert the time-domain signal in the corresponding window to the frequency domain to obtain the corresponding frequency spectrum.

[0090] In the step S33, the specific manner of converting the time-domain signal to the frequency domain can be, but is not limited to, implementing it by using the existing fast Fourier transform algorithm or a similar algorithm. The specific transformation formula will not be elaborated here.

[0091] S34. According to the plurality of frequency spectra corresponding one-to-one to the plurality of time windows, calculate the noise power spectral density PSD(f) corresponding to the certain acquisition device and the certain unit acquisition period according to the following formula:

[0092]

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

[0094] In the step S34, the purpose of averaging the power spectral densities of all time windows is to reduce the influence of random noise. In addition, based on Figure 2 the noise power spectral density PSD(f) corresponding to the plurality of seismic signal acquisition devices shown and a certain unit acquisition period with a duration of 10 minutes are curves respectively and are exemplified as Figure 4 shown.

[0095] S4. For each of the acquisition devices, extract the cumulative power spectral variance value of the corresponding device according to the noise power spectral density corresponding to the device and each unit acquisition period.

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

[0097]

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

[0099]

[0100] In the formula, M represents the total number of the multiple seismic signal acquisition devices, m and m′ respectively represent positive integers less than or equal to M, l represents the serial number of the unit acquisition period, L represents the total number of the unit acquisition periods in the whole acquisition period, VFA m represents the second cumulative power spectrum variance value corresponding to the m-th device among the multiple seismic signal acquisition devices and the whole acquisition period, f represents the frequency, represents the first cumulative power spectrum variance value corresponding to the m-th device and the l-th unit acquisition period, represents the noise power spectral density corresponding to the m-th device and the l-th unit acquisition period, Denote the noise power spectral density corresponding to the m'-th device among the multiple seismic signal acquisition devices and corresponding to the l-th unit acquisition period. Based on Figure 2 The calculation result of the first cumulative power spectral variance value corresponding to the multiple seismic signal acquisition devices shown and corresponding to a certain unit acquisition period with a duration of 10 minutes is exemplified as Figure 5 shown.

[0101] S5. Determine, from the multiple seismic signal acquisition devices, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance according to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the layout center position of the multiple seismic signal acquisition devices, where the distances are calculated based on the known layout positions of the multiple seismic signal acquisition devices.

[0102] In step S5, the known layout positions of the multiple seismic signal acquisition devices can be accurately measured by GPS (Global Positioning System) positioning technology or other positioning technologies during layout; for example, if the known layout position of the m-th device is (x m , y m ), then the distance D m from the m-th device to the layout center position of the multiple seismic signal acquisition devices is expressed as follows:

[0103]

[0104] In the formula, (x0, y0) represents the layout center position of the multiple seismic signal acquisition devices, which can be obtained by averaging based on the known layout positions of the multiple seismic signal acquisition devices. Since the specific content of the cumulative power spectral variance values is different, the specific determination process of the foregoing abnormal relationships will also be different. Specifically, determining, from the multiple seismic signal acquisition devices, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance according to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the layout center position of the multiple seismic signal acquisition devices includes, but is not limited to, the following methods (E) and / or method (F).

[0105] (E) When calculating the first cumulative power spectrum variance values corresponding to each of the acquisition devices and each unit acquisition period, first, for each unit acquisition period, based on each of the acquisition devices and the corresponding first cumulative power spectrum variance values, calculate the average value μ and variance value δ of the first cumulative power spectrum variance value, and take the interval [μ - η×δ, μ + η×δ] as the corresponding normal determination range. Then, for each of the acquisition devices and each unit acquisition period, if the first cumulative power spectrum variance value of the corresponding device falls within the normal determination range of the corresponding period, determine that the corresponding period is an abnormal period of the corresponding device. Finally, for each of the acquisition devices, if the ratio of the duration of all the corresponding abnormal periods to the duration of the full-process acquisition period is greater than the first preset percentage threshold, determine that there is a relationship anomaly for the corresponding device in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time, where η represents a preset coefficient. The aforementioned preset coefficient η can be preset according to historical experience, for example, taking a value of 2; the aforementioned first preset percentage threshold can also be preset according to historical experience, for example, taking a value of 50%. Based on Figure 2 An example of the abnormal state time series diagram of the multiple seismic signal acquisition devices shown in Figure 6 is as shown. It can be seen that there is a relationship anomaly for the three seismic signal acquisition devices numbered #17, #58, and #85 in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time. In addition, according to Figure 5 it can also be found that the three seismic signal acquisition devices numbered #17, #58, and #85 are abnormal.

[0106] (F) When calculating the second cumulative power spectrum variance values corresponding to each of the acquisition devices and the full-process acquisition period, first, based on the second cumulative power spectrum variance values corresponding to each of the acquisition devices and the full-process acquisition period and the distances from each of the acquisition devices to the layout center position of the multiple seismic signal acquisition devices, fit a function model with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable based on a linear regression model. Then, for each of the acquisition devices, import the distance from the corresponding device to the layout center position of the multiple seismic signal acquisition devices into the function model, and determine whether the second cumulative power spectrum variance value corresponding to the corresponding device and the full-process acquisition period is an outlier according to the output result and the confidence interval based on the second preset percentage threshold. If so, determine that there is a relationship anomaly for the corresponding device in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, where the distance is calculated based on the known layout positions of the multiple seismic signal acquisition devices. The aforementioned linear regression model is exemplified but not limited to the following expression:

[0107] VFA m = a×(D m ) b + c

[0108] In the formula, a, b, and c respectively represent coefficients that can be determined by fitting. The aforementioned second preset percentage threshold can also be preset according to historical experience. For example, it can take a value of 90%. Based on Figure 2 The relationship diagram between the second cumulative power spectrum variance value corresponding to the multiple seismic signal acquisition devices shown and the confidence interval in the full acquisition period is exemplified as Figure 7 shown. It can also be found that there are abnormal relationships among the three seismic signal acquisition devices numbered #17, #58, and #85 in the dimension of the relationship between the cumulative power spectrum variance and the relative distance.

[0109] In step S5, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance are the results obtained through the consistency detection of large-scale seismic signal acquisition devices, that is, the faulty devices detected and inconsistent with other devices. Then, their numbers and / or known deployment positions can be output so that maintenance personnel can quickly locate and repair or replace them.

[0110] Based on the large-scale seismic signal acquisition device consistency detection method described in the foregoing steps S1 to S5, a new solution for on-site verification and detection of large-scale seismic signal acquisition devices based on background noise data is provided. That is, it is executed by the computer device in the large-scale seismic signal acquisition device consistency detection system. After receiving the original background noise data continuously and synchronously collected by multiple seismic signal acquisition devices, it first obtains the cumulative power spectrum variance values of each acquisition device through preprocessing and multi-step calculations in sequence. Finally, based on the cumulative power spectrum variance values of each acquisition device and the distances from each acquisition device to the deployment center position, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or relative distance are determined. In this way, through the noise power density analysis of the background noise data, the purpose of efficient, reliable, and automated consistency detection of large-scale seismic signal acquisition devices can be achieved. Furthermore, the detection results can accurately reflect the performance status of seismic instruments, facilitate the accurate discovery of abnormal devices, and provide strong guarantee for the smooth progress of seismic exploration work, which is convenient for practical application and promotion.

[0111] As Figure 8 shown, in the second aspect of this embodiment, a virtual device for implementing the large-scale seismic signal acquisition device consistency detection method described in the first aspect is provided, which is arranged in the computer device of the large-scale seismic signal acquisition device consistency detection system. Among them, the large-scale seismic signal acquisition device consistency detection system further includes a plurality of seismic signal acquisition devices evenly deployed in the experimental site and respectively communicatively connected to the computer device;

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

[0113] The raw data receiving unit is used to receive the background noise raw data continuously and synchronously collected by the multiple seismic signal acquisition devices;

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

[0115] The power spectral density calculation unit is used to calculate, for each acquisition device among the multiple seismic signal acquisition devices, the noise power spectral density corresponding to the device and corresponding to each unit acquisition period according to the new background noise data corresponding to the device and each unit acquisition period;

[0116] The power spectral variance calculation unit is used to extract, for each acquisition device, the cumulative power spectral variance value corresponding to the device according to the noise power spectral density corresponding to the device and corresponding to each unit acquisition period;

[0117] The abnormal equipment determination unit is used to determine, from the multiple seismic signal acquisition devices, the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance according to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the layout center position of the multiple seismic signal acquisition devices, where the distances are calculated based on the known layout positions of the multiple seismic signal acquisition devices.

[0118] For the working process, working details, and technical effects of the foregoing device provided in the second aspect of this embodiment, reference may be made to the large-scale seismic signal acquisition equipment consistency detection method described in the first aspect, which will not be elaborated herein.

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

[0120] The computer device is used to execute the large-scale seismic signal acquisition equipment consistency detection method described in the first aspect. For the working process, working details, and technical effects of the foregoing system provided in the third aspect of this embodiment, reference may be made to the large-scale seismic signal acquisition equipment consistency detection method described in the first aspect, which will not be elaborated herein.

[0121] As Figure 10 shown, the fourth aspect of this embodiment provides a computer device for implementing the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect, including a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect. Specifically, for example, the memory may but is not limited to include random access memory (RAM), read-only memory (ROM), flash memory, first input first output (FIFO), and / or first input last output (FILO), etc.; the processor may but is not limited to use a microprocessor of the STM32F105 series. In addition, the computer device may also but is not limited to include a power module, a display screen, and other necessary components.

[0122] For the working process, working details, and technical effects of the foregoing computer device provided in the fourth aspect of this embodiment, reference may be made to the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect, and details will not be elaborated here.

[0123] The fifth aspect of this embodiment provides a computer-readable storage medium storing instructions including the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect, that is, instructions are stored on the computer-readable storage medium, and when the instructions run on a computer, they execute the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect. Among them, the computer-readable storage medium refers to a carrier for storing data, and may but is not limited to include computer-readable storage media such as floppy disks, optical discs, hard disks, flash memories, 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] For the working process, working details, and technical effects of the foregoing computer-readable storage medium provided in the fifth aspect of this embodiment, reference may be made to the consistency detection method of large-scale seismic signal acquisition devices as described in the first aspect, and details will not be elaborated here.

[0125] In a sixth aspect of this embodiment, a computer program product is provided, including a computer program or instructions, and when the computer program or the instructions are executed by a computer, the method for detecting the consistency of large-scale seismic signal acquisition devices as described in the first aspect is implemented. Among them, the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0126] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the consistency of large-scale seismic signal acquisition devices, characterized in that It is executed by a computer device in a large-scale seismic signal acquisition device consistency detection system, wherein the large-scale seismic signal acquisition device consistency detection system further includes a plurality of seismic signal acquisition devices that are uniformly arranged in an experimental site and are respectively communicatively connected to the computer device; The large-scale seismic signal acquisition device consistency detection method includes: Receiving background noise raw data continuously and synchronously collected by the plurality of seismic signal acquisition devices; Preprocessing the background noise raw data to obtain corresponding new background noise data; For each acquisition device among the plurality of seismic signal acquisition devices, calculating the noise power spectral density corresponding to the device and corresponding to each unit acquisition period according to the new background noise data corresponding to the device and corresponding to each unit acquisition period; For each of the acquisition devices, extracting the cumulative power spectral variance value corresponding to the device according to the noise power spectral density corresponding to the device and corresponding to each unit acquisition period; Determining, from the plurality of seismic signal acquisition devices, acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance according to the cumulative power spectral variance values of the respective acquisition devices / and the distances from the respective acquisition devices to the central position of the layout of the plurality of seismic signal acquisition devices, wherein the distances are calculated based on the known layout positions of the plurality of seismic signal acquisition devices.

2. The consistency detection method for large-scale seismic signal acquisition devices according to claim 1, characterized in that Preprocessing the background noise raw data to obtain corresponding new background noise data, including: For each acquisition device among the plurality of seismic signal acquisition devices, determining whether there is a data file missing situation or a data recording abnormal situation in the corresponding background noise raw data. If so, directly determining the corresponding device as an abnormal device and removing the corresponding device from the plurality of seismic signal acquisition devices; And / or, calculating the data average value of the background noise raw data in each unit acquisition period, and for each unit acquisition period, subtracting the corresponding data average value from the background noise raw data collected in the corresponding period; And / or, using a trend analysis algorithm to remove the long-term trend component in the background noise raw data; And / or, using a filter to remove the noise components in the background noise raw data that are in non-target frequency bands / and performing downsampling processing on the filtered data.

3. The method for detecting the consistency of large-scale seismic signal acquisition devices according to claim 1, characterized in that For each acquisition device among the plurality of seismic signal acquisition devices, calculating the noise power spectral density corresponding to the device and corresponding to each unit acquisition period according to the new background noise data corresponding to the device and corresponding to each unit acquisition period, including: For a certain acquisition device among the plurality of seismic signal acquisition devices, splitting the corresponding new background noise data to obtain the new background noise data corresponding to the device and corresponding to each unit acquisition period; Performing windowing processing on the new background noise data corresponding to the certain acquisition device and corresponding to a certain unit acquisition period to obtain time-domain signals in a plurality of time windows; For each of the multiple time windows, convert the time-domain signal in the corresponding window to the frequency domain to obtain the corresponding spectrum; According to the multiple spectra corresponding one-to-one to the multiple time windows, calculate the noise power spectral density PSD(f) corresponding to the certain acquisition device and the certain unit acquisition period according to the following formula: where f represents the frequency, K represents the total number of windows of the multiple 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 multiple time windows, N represents the time window and the window length measured by the number of sampling points, and X k (f) represents the frequency spectrum corresponding to the k-th time window.

4. The method for detecting the consistency of large-scale seismic signal acquisition devices according to claim 1, characterized in that, For each of the acquisition devices, according to the noise power spectral density corresponding to the corresponding device and each unit acquisition period, extract the cumulative power spectral variance value of the corresponding device, including: For each of the acquisition devices, according to the noise power spectral density corresponding to the corresponding device and each unit acquisition period, calculate the first cumulative power spectral variance value corresponding to the corresponding device and each unit acquisition period according to the following formula: Wherein, M represents the total number of the plurality of seismic signal acquisition devices, m and m' respectively represent positive integers less than or equal to M, l represents the serial number of the unit acquisition period, f represents the frequency, represents the first cumulative power spectral variance value corresponding to the m-th device among the plurality of seismic signal acquisition devices and the l-th unit acquisition period, represents the noise power spectral density corresponding to the m-th device and the l-th unit acquisition period, represents the noise power spectral density corresponding to the m'-th device among the plurality of seismic signal acquisition devices and the l-th unit acquisition period; And / or, for each of the acquisition devices, according to the noise power spectral density corresponding to the corresponding device and each unit acquisition period, calculate the second cumulative power spectral variance value corresponding to the corresponding device and the full acquisition period according to the following formula: Wherein, M represents the total number of the multiple seismic signal acquisition devices, m and m' respectively represent positive integers less than or equal to M, l represents the serial number of the unit acquisition period, L represents the total number of the unit acquisition periods in the whole acquisition period, and VFA m represents the second cumulative power spectral variance value corresponding to the m-th device among the multiple seismic signal acquisition devices and corresponding to the whole acquisition period, f represents frequency, represents the first cumulative power spectral variance value corresponding to the m-th device and corresponding to the l-th unit acquisition period, represents the noise power spectral density corresponding to the m-th device and corresponding to the l-th unit acquisition period, represents the noise power spectral density corresponding to the m'-th device among the multiple seismic signal acquisition devices and corresponding to the l-th unit acquisition period.

5. The method for detecting the consistency of large-scale seismic signal acquisition devices according to claim 1, characterized in that According to the cumulative power spectral variance values of each of the acquisition devices / and the distances from each of the acquisition devices to the layout center position of the multiple seismic signal acquisition devices, determine the acquisition devices with abnormal relationships in the dimension of the relationship between the cumulative power spectral variance and the acquisition time or relative distance from the multiple seismic signal acquisition devices, including: When calculating the first cumulative power spectral variance value corresponding to each of the acquisition devices and each unit acquisition period, first, for each unit acquisition period, calculate the average value μ and variance value δ of the first cumulative power spectral variance value according to each of the acquisition devices and the corresponding first cumulative power spectral variance value, and use the interval [μ - η×δ, μ + η×δ] as the corresponding normal determination range. Then, for each of the acquisition devices and each unit acquisition period, if the first cumulative power spectral variance value corresponding to the corresponding device falls within the normal determination range of the corresponding period, determine the corresponding period as the abnormal period of the corresponding device. Finally, for each of the acquisition devices, if the ratio of the duration of all the corresponding abnormal periods to the duration of the full acquisition period is greater than the first preset percentage threshold, determine that there is an abnormal relationship in the dimension of the relationship between the cumulative power spectral variance and the acquisition time of the corresponding device, where η represents a preset coefficient; And / or, when calculating the second cumulative power spectrum variance values corresponding to each acquisition device and the entire acquisition time period, first, based on the second cumulative power spectrum variance values corresponding to each acquisition device and the entire acquisition time period and the distances from each acquisition device to the layout center position of the multiple seismic signal acquisition devices, a function model with the distance as the independent variable and the second cumulative power spectrum variance value as the dependent variable is fitted based on a linear regression model. Then, for each acquisition device, the distance from the corresponding device to the layout center position of the multiple seismic signal acquisition devices is imported into the function model, and based on the output result and the confidence interval based on the second preset percentage threshold, it is determined whether the second cumulative power spectrum variance value corresponding to the corresponding device and the entire acquisition time period is an outlier. If so, it is determined that there is a relationship anomaly for the corresponding device in the dimension of the relationship between the cumulative power spectrum variance and the relative distance, where the distance is calculated based on the known layout positions of the multiple seismic signal acquisition devices.

6. A consistency detection device for large-scale seismic signal acquisition equipment, characterized in that, Arranged in a computer device of a large-scale seismic signal acquisition device consistency detection system, where the large-scale seismic signal acquisition device consistency detection system further includes a plurality of seismic signal acquisition devices evenly arranged in an experimental site and respectively communicatively connected to the computer device; The large-scale seismic signal acquisition device consistency detection device includes a raw data receiving unit, a data preprocessing unit, a power spectral density calculation unit, a power spectrum variance calculation unit, and an abnormal device determination unit that are communicatively connected in sequence; The raw data receiving unit is configured to receive the background noise raw data continuously and synchronously collected by the plurality of seismic signal acquisition devices; The data preprocessing unit is configured to preprocess the background noise raw data to obtain corresponding new background noise data; The power spectral density calculation unit is configured to, for each acquisition device among the plurality of seismic signal acquisition devices, calculate the noise power spectral density corresponding to the corresponding device and each unit acquisition time period according to the new background noise data corresponding to the corresponding device and each unit acquisition time period; The power spectrum variance calculation unit is configured to, for each acquisition device, extract the cumulative power spectrum variance value corresponding to the corresponding device according to the noise power spectral density corresponding to the corresponding device and each unit acquisition time period; The abnormal device determination unit is configured to determine, from the plurality of seismic signal acquisition devices, the acquisition devices that have a relationship anomaly in the dimension of the relationship between the cumulative power spectrum variance and the acquisition time or the relative distance according to the cumulative power spectrum variance values of each acquisition device / and the distances from each acquisition device to the layout center position of the multiple seismic signal acquisition devices, where the distance is calculated based on the known layout positions of the multiple seismic signal acquisition devices.

7. A consistency detection system for large-scale seismic signal acquisition devices, characterized in that, It includes a computer device and a plurality of seismic signal acquisition devices, where the plurality of seismic signal acquisition devices are evenly arranged in an experimental site and respectively communicatively connected to the computer device; The computer device is used to execute the consistency detection method for the large-scale seismic signal acquisition device according to any one of claims 1 to 5.

8. A computer device, characterized in that, It includes a memory, a processor, and a transceiver that are communicatively connected in sequence. Among them, the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the consistency detection method for the large-scale seismic signal acquisition device according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that Instructions are stored on the computer-readable storage medium. When the instructions run on the computer, the consistency detection method for the large-scale seismic signal acquisition device according to any one of claims 1 to 5 is executed.

10. A computer program product comprising a computer program or instructions, characterized in that, The computer program or the instructions, when executed by the computer, implement the consistency detection method for the large-scale seismic signal acquisition device according to any one of claims 1 to 5.

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