Node seismograph data quality monitoring method and device, electronic equipment and medium
By performing thinning, autocorrelation analysis, and ellipsoidal volume calculation on nodal seismograph data, the problem of difficult data quality control of nodal seismographs was solved, achieving efficient and accurate data quality monitoring and reducing manpower and equipment investment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-24
- Publication Date
- 2026-03-24
AI Technical Summary
Nodal seismographs cannot acquire single-shot records in real time during full-node acquisition, resulting in long data synthesis cycles and difficulties in quality control. Traditional amplitude and frequency statistical methods cannot effectively identify weak amplitudes and high-frequency background noise, requiring a large investment of manpower and equipment.
By reading the shot gather seismic data, performing thinning and preliminary quality control, generating a three-dimensional scatter plot, conducting autocorrelation analysis, calculating T0 and A0 values, calculating anomalous receiver points using ellipsoidal volume, and generating a three-dimensional scatter plot for shot-by-shot and trace-by-track quality control.
It enables shot-by-shot and track-by-track quality control of nodal seismograph data, improves data analysis efficiency and accuracy, reduces manpower and equipment investment, and ensures the quality of synthetic data.
Smart Images
Figure CN121721701A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of petroleum seismic exploration technology and relates to data quality monitoring methods, specifically a nodal seismograph data quality monitoring method, device, electronic equipment and medium. Background Technology
[0002] Nodal seismographs are widely used in seismic exploration due to their self-collection and self-storage capabilities. However, there are also some problems in the application of nodal seismographs, such as the inability to obtain single-shot records in real time during full-node acquisition, the inability to monitor the acquired data in a timely manner, and the long data synthesis cycle, which makes quality control difficult during the synthesis process and makes it impossible to guarantee the accuracy of the synthesized data.
[0003] For some issues, such as the loss of individual node data, quality control can be performed by detecting zero amplitude values. However, for other issues, such as the inclusion of background noise with weak amplitude values and high dominant frequencies into the seismic data by nodal seismographs, which is scattered throughout the shot collection, traditional amplitude and frequency statistical methods, such as seismic data spectrum analysis and frequency sub-scanning, cannot perform shot-by-shot and channel-by-channel quality control of the seismic data, select outlier data, and improve the data quality of nodal seismographs. At the same time, traditional amplitude and frequency statistical methods require a significant investment of manpower and equipment. Summary of the Invention
[0004] To address the aforementioned shortcomings in the existing technology, this invention aims to provide a method, device, electronic equipment, and medium for monitoring the data quality of nodal seismographs, which can perform detailed analysis of seismic data shot by shot and trace by trace, thereby improving the data quality of nodal seismographs.
[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0006] A method for monitoring the data quality of nodal seismographs includes the following steps in sequence:
[0007] S1: Read shot gather seismic data, perform thinning and preliminary quality control.
[0008] The shot gather seismic data is thinned and then read; the shot gather seismic data includes seismic traces and trace head information.
[0009] A three-dimensional scatter plot I is generated with the east coordinate of the detector point as the horizontal axis X, the north coordinate as the vertical axis Y, and the absolute values of the average apparent dominant frequency and amplitude of the detector point as the Z axis.
[0010] Select receiver points with small amplitude, high frequency and abnormal color in the three-dimensional scatter plot I, extract common receiver point gathers, and compare them with the spectrum analysis to obtain receiver points whose spectrum characteristics are different from the seismic data and normal data. These are the initial control anomaly receiver points, and the preliminary quality control is completed.
[0011] S2, Perform autocorrelation analysis on shot gathering seismic data.
[0012] Autocorrelation analysis was performed on the mid-to-deep time window of the shot gathering seismic data to obtain the delay time T0 when the autocorrelation function first crosses the zero point, and the maximum value A0 of the autocorrelation function in the interval [0,T0].
[0013] Based on the T0 values of normal and initial control anomalous receivers in the shot gather seismic data after autocorrelation analysis, a T0 value threshold is set, and the dominant frequency amplitude file of the receivers after autocorrelation analysis is output.
[0014] The T0 threshold value is less than or equal to the T0 value of the initial control anomaly detector point;
[0015] The detector point dominant frequency amplitude file includes the field file number, detector point number, detector point east coordinate, detector point north coordinate, T0 value, and A0 value.
[0016] S3, normalize the dominant frequency amplitude file of the receiver point and calculate the ellipsoidal volume value.
[0017] Normalize the dominant frequency amplitude file of the detector points, calculate the average T0 value, and count the number of detector points whose average T0 value is lower than the T0 value threshold.
[0018] The ellipsoidal volume value of each receiver point is calculated using the ellipsoidal volume calculation formula. The receiver point number, the east coordinate of the receiver point, the north coordinate of the receiver point, and the ellipsoidal volume value are output as a quality control result text file.
[0019] S4: Read the quality control result text file, select abnormal detector points, and complete quality monitoring.
[0020] Read the quality control result text file and generate a three-dimensional scatter plot II with the east coordinate of the detector point as the horizontal axis (X), the north coordinate as the vertical axis (Y), and the ellipsoidal volume of the detector point as the Z axis.
[0021] When the ratio of the ellipsoidal volume values of any two detector points is ≥100, the detector point with the larger ellipsoidal volume is marked as detector point I.
[0022] Select detector point I with color anomalies in the three-dimensional scatter plot II and mark it as detector point II;
[0023] Set an ellipsoidal volume threshold, wherein the ellipsoidal volume threshold is less than or equal to the ellipsoidal volume of detector point II;
[0024] Select the receiver points in the quality control result text file whose ellipsoidal volume is greater than or equal to the ellipsoidal volume threshold. These are the abnormal receiver points, thus completing the nodal seismograph data quality monitoring.
[0025] As a limitation of this invention, the trackhead information includes the field file number of the shot point, the station number and coordinate information of the shot point and the receiver point;
[0026] When any piece of trackhead information is missing, the missing information is added to the trackhead through the observation system.
[0027] As a further limitation of the present invention, thinning refers to first uniformly thinning the shot line, and then uniformly thinning the shot points; after thinning is completed, the number of shot sets in the seismic data is reduced by 90%.
[0028] As a further limitation of the present invention, the absolute value of the detector amplitude is obtained by selecting a mid-deep time window and calculating it using the root mean square method;
[0029] The average apparent dominant frequency of the detector point is determined by selecting a mid-to-deep time window using the formula: The calculation yields , where MF is the average apparent dominant frequency of the detector point, M is the amplitude sample point, i is the channel number, and T is the main lobe half-width of the autocorrelation function.
[0030] As a further limitation of the present invention, the autocorrelation analysis includes the following steps:
[0031] By selecting the mid-to-deep time window of the shot gather seismic data, and using the Wiener-Khinchin theorem, the autocorrelation function is calculated through fast Fourier transform and inverse fast Fourier transform. The dominant frequency and amplitude are then calculated using the autocorrelation function.
[0032] The autocorrelation function is: Rxx(τ)=E[x(t)x(t+τ)], where t is time, τ is the time-shifted variable, x(t) is the amplitude value of the sampling point at time t, x(t+τ) is the amplitude value of the time-shifted variable τ at time t, and E[x(t)x(t+τ)] is the expected value of the product of x(t) and x(t+τ) over the entire time.
[0033] The value of T0 is the time shift variable τ0 when Rxx(τ) first crosses the zero point;
[0034] The value of A0 is the maximum value of Rxx(τ) on the interval [0,T0].
[0035] As a further limitation of the present invention, the detector point main frequency amplitude file is an ASCII format text file.
[0036] As a further limitation of the present invention, the method for calculating the volume of the ellipsoid includes the following steps:
[0037] Calculate the average of the normalized A0 values, and label it as a;
[0038] The ratio of the number of times the average T0 value is lower than the T0 threshold to the total number of times the detector point appears is denoted as b.
[0039] Calculate the reciprocal of the normalized average value T0, denoted as c;
[0040] Through the formula: The volume of the ellipsoid is calculated.
[0041] The present invention also provides a nodal seismograph data quality monitoring device based on the nodal seismograph data quality monitoring method described in the above technical solution, comprising:
[0042] The acquisition module is used to acquire shot gather seismic data collected by nodal seismic instruments;
[0043] The read module is used to read and thin the shot gather seismic data.
[0044] The preliminary quality control module performs preliminary quality control based on shot gather seismic data to obtain initial control anomaly detector points;
[0045] The autocorrelation analysis module performs autocorrelation analysis based on shot gather seismic data to obtain the dominant frequency amplitude file of the receiver points;
[0046] The normalization module performs normalization processing based on the dominant frequency amplitude file of the detector points to obtain the average T0 value and the number of detector points with an average T0 value lower than the T0 value threshold.
[0047] The calculation module calculates the volume of the ellipsoid based on the data obtained through normalization.
[0048] The output module outputs the detector point number, the east coordinate of the detector point, the north coordinate of the detector point, and the ellipsoid volume value as a text file of quality control results;
[0049] The quality control module selects abnormal receiver points based on the quality control result text file and completes the monitoring of nodal seismograph data quality.
[0050] The present invention also provides an electronic device, the electronic device comprising:
[0051] Memory, which stores executable instructions;
[0052] A processor that executes the executable instructions in the memory to implement the nodal seismograph data quality monitoring method described in any of the above technical solutions.
[0053] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the nodal seismograph data quality monitoring method described in any of the above technical solutions.
[0054] By adopting the above-described technical solution, the beneficial effects achieved by this invention compared to the prior art are as follows:
[0055] The present invention provides a nodal seismograph data quality monitoring method that thins out shot gather data, reads the data, establishes a three-dimensional scatter plot I, identifies receivers with anomalous dominant frequency and amplitude from the three-dimensional scatter plot I, examines the receiver gather data, confirms the spectrum and appearance of the initially controlled anomalous receivers, performs preliminary quality control, and obtains the initially controlled anomalous receivers. This method reduces the amount of data read and improves the efficiency of preliminary quality control.
[0056] Autocorrelation analysis was performed on the shot gather seismic data to obtain T0 and A0 values. Based on the T0 values of normal receivers and initial control anomaly receivers in the shot gather seismic data after autocorrelation analysis, a T0 value threshold was set. The dominant frequency amplitude file of the receivers was output, which included the field file number, receiver number, X and Y coordinates of the receivers, and T0 and A0 values of the receivers. The autocorrelation analysis used Fast Fourier Transform (FFT) to improve the efficiency of data analysis.
[0057] The frequency of receivers with values below the T0 threshold is counted, the ellipsoidal volume is calculated, and the output is a quality control result text file. A 3D scatter plot II is generated. A large ellipsoidal volume indicates a problem with the receiver, which may be an anomalous receiver. This is confirmed by combining the color of the receivers in 3D scatter plot II. Anomalous receivers are displayed with a color that is significantly different from that of normal receivers in 3D scatter plot II. Based on the ellipsoidal volume of the receivers and the receiver colors in 3D scatter plot II, an ellipsoidal volume threshold is set. In the quality control result text file, receivers with values above the ellipsoidal volume threshold are judged as anomalous receivers, and receivers with values below the ellipsoidal volume threshold are judged as normal receivers. Anomalous receivers are selected to complete the nodal seismograph data quality monitoring. The ellipsoidal volume calculation method can comprehensively utilize the information of receivers in three dimensions: amplitude, frequency, and anomaly rate, and can more accurately and comprehensively determine whether a receiver is anomalous.
[0058] The nodal seismograph data quality monitoring method of this invention can perform shot-by-shot and channel-by-channel quality control on the synthesized data of nodal seismographs, while reducing manpower and equipment investment, and achieving the goal of high data acquisition quality. Compared with the traditional amplitude and frequency statistics method, this invention realizes the accuracy of shot-by-shot and channel-by-channel analysis and statistics of the synthesized data of nodal seismographs, and adopts more effective statistical methods to improve the accuracy of quality control and thus improve the data quality of nodal seismographs. At the same time, this method minimizes human-computer interaction, thereby reducing manpower and time costs.
[0059] This invention is applicable to the quality monitoring of data acquired by nodal seismographs and is used in seismic exploration technology. Attached Figure Description
[0060] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0061] Figure 1 This is a flowchart of the nodal seismograph data quality monitoring method in Embodiment 1 of the present invention;
[0062] Figure 2 This is a shot gather seismic data profile after autocorrelation analysis in Embodiment 1 of the present invention. The black dashed box in the figure is a line graph of T0 value.
[0063] Figure 3 This is a schematic diagram of the three-dimensional scatter plot II in Embodiment 1 of the present invention. The detector points within the red dashed box in the figure are the detector points with large ellipsoidal volumes, as shown by their color in the three-dimensional scatter plot II. Detailed Implementation
[0064] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the described embodiments are only used to explain the present invention and do not limit the present invention.
[0065] Example 1: A method for monitoring the data quality of nodal seismographs
[0066] This embodiment uses shot gather seismic data acquired by nodal seismic instruments in a 3D seismic field as an example to demonstrate a method for monitoring the data quality of nodal seismic instruments. Figure 1 As shown, the shot gather seismic data acquired by the nodal seismic instrument is imported into the database. The shot gather seismic data includes seismic trace and trace head information. The trace head information includes the field file number of the shot point, the station number of the shot point, and the coordinate information. The trace head information is missing the station number and coordinate information of the receiver point. The missing information is inserted into the trace head through the observation system to make the trace head information complete. Then, the following steps are performed in the database in sequence:
[0067] S1: Read shot gather seismic data, perform thinning and preliminary quality control.
[0068] The seismic data from shot gathers is thinned out. First, the shot lines are thinned out uniformly, and then the shot points are thinned out uniformly. Thinning is stopped when the number of shot gathers is reduced by 90%. The track head information and sampled value information are read to obtain the receiver line number, point number, east coordinate, north coordinate and amplitude. The receiver line number and point number are combined to form the receiver point number.
[0069] By selecting a mid-to-deep time window from the shot gather seismic data, the absolute value of the receiver amplitude was calculated using the root mean square (RMS) method.
[0070] Selecting the mid-to-deep time window from shot gather seismic data using the formula: The average apparent dominant frequency of the detector point is calculated, where MF is the average apparent dominant frequency of the detector point, M is the amplitude sample point, i is the channel number, and T is the main lobe half-width of the autocorrelation function.
[0071] Write the obtained detector point number, east and north coordinates of the detector point, absolute value of the detector point amplitude, and average apparent dominant frequency of the detector point into the text file required for the preliminary quality control to generate the scatter plot.
[0072] Read the text file and generate a three-dimensional scatter plot I with the east coordinate of the receiver point as the x-axis, the north coordinate as the y-axis, and the absolute values of the average apparent dominant frequency and amplitude of the receiver point as the z-axis. Select receiver points in the three-dimensional scatter plot I that have abnormal color and small amplitude and high frequency, extract common receiver point gathers, and compare them with the spectrum analysis. Receiver points whose spectrum characteristics are different from the seismic data and normal data are identified as initial control anomaly receiver points, thus completing the preliminary quality control.
[0073] S2, Perform autocorrelation analysis on shot gathering seismic data.
[0074] Autocorrelation analysis was performed on the mid-to-deep time window of the shot gather seismic data. The autocorrelation function was calculated using the Wiener-Khinchin theorem through Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT): Rxx(τ)=E[x(t)x(t+τ)].
[0075] When Rxx(τ) first crosses the zero point, the value of T0 is obtained as τ0; using the formula: The value of A0 is calculated.
[0076] Seismic data from shot gathers after autocorrelation analysis, such as Figure 2 As shown, Figure 2 Within the black dashed box, the initial control anomaly receivers have lower T0 values, while the normal receivers have higher T0 values. By plotting the T0 values of all receivers within a shot collection as a line graph, the anomaly receivers can be clearly identified. Based on the T0 values of the normal receivers and the initial control anomaly receivers, a T0 threshold value of 6 is set.
[0077] Output the dominant frequency amplitude file of the receiver point after autocorrelation analysis. This receiver point dominant frequency amplitude file is an ASCII format text file, including the field file number, receiver point number, receiver point east coordinate, receiver point north coordinate, T0 value, and A0 value.
[0078] S3, normalize the dominant frequency amplitude file of the receiver point and calculate the ellipsoidal volume value.
[0079] The dominant frequency amplitude file of the detector points is normalized, the average value T0 is calculated, and the number of detector points with an average T0 value lower than the T0 threshold is counted.
[0080] Calculate the average of the normalized A0 values, denoted as a; calculate the ratio of the number of detectors with an average T0 value below the T0 threshold to the total number of such detectors, denoted as b; calculate the reciprocal of the normalized average T0 value, denoted as c; using the formula: The volume of the ellipsoid is calculated.
[0081] Output the detector point number, the east coordinate of the detector point, the north coordinate of the detector point, and the ellipsoid volume value as a text file of quality control results.
[0082] S4: Read the quality control result text file, select abnormal detector points, and complete quality monitoring.
[0083] Read the quality control result text file, and generate a three-dimensional scatter plot II with the east coordinate of the detector point as the x-axis, the north coordinate as the y-axis, and the detector point ellipsoid volume value as the z-axis. Figure 3 As shown.
[0084] When the ratio of the ellipsoidal volume values of any two detector points is ≥100, the detector point with the larger ellipsoidal volume is marked as detector point I; the detector point I with the color anomaly in the three-dimensional scatter plot II is selected and marked as detector point II. Figure 3 Within the red dashed box, the ellipsoidal volume of the warm-colored receiver is 10, and the ellipsoidal volume of the blue receiver is 0.1. The threshold value of the ellipsoidal volume is set to 10. Receivers with an ellipsoidal volume ≥ 10 in the quality control result text file are selected as abnormal receivers, thus completing the nodal seismograph data quality monitoring.
[0085] Example 2: A nodal seismograph data quality monitoring device
[0086] This embodiment provides a nodal seismograph data quality monitoring device based on the nodal seismograph data quality monitoring method in Embodiment 1. The device includes:
[0087] The acquisition module is used to acquire shot gather seismic data collected by nodal seismic instruments;
[0088] The read module is used to read and thin the shot gather seismic data.
[0089] The preliminary quality control module performs preliminary quality control based on shot gather seismic data to obtain initial control anomaly detector points;
[0090] The autocorrelation analysis module performs autocorrelation analysis based on shot gather seismic data to obtain the dominant frequency amplitude file of the receiver points;
[0091] The normalization module performs normalization processing based on the dominant frequency amplitude file of the detector points to obtain the average T0 value and the number of detector points with an average T0 value lower than the T0 value threshold.
[0092] The calculation module calculates the volume of the ellipsoid based on the data obtained through normalization.
[0093] The output module outputs the detector point number, the east coordinate of the detector point, the north coordinate of the detector point, and the ellipsoid volume value as a text file of quality control results;
[0094] The quality control module selects abnormal receiver points based on the quality control result text file and completes the monitoring of nodal seismograph data quality.
[0095] Example 3: An electronic device
[0096] This embodiment provides an electronic device, which includes: a memory storing executable instructions; and a processor that runs the executable instructions in the memory to implement the nodal seismograph data quality monitoring method in Embodiment 1.
[0097] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0098] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment disclosed in this application, the processor is used to execute computer-readable instructions stored in the memory.
[0099] Example 4: A computer-readable storage medium
[0100] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the nodal seismograph data quality monitoring method of Embodiment 1.
[0101] The computer-readable storage medium stores non-transitory computer-readable instructions thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods of the foregoing embodiments are performed.
[0102] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
[0103] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or make equivalent substitutions for some of the technical features. 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 claims of the present invention.
Claims
1. A method for monitoring the data quality of nodal seismographs, characterized in that, This includes performing the following steps in sequence: S1: Read the shot gather seismic data, perform thinning and preliminary quality control. The shot gather seismic data is thinned and then read; the shot gather seismic data includes seismic traces and trace head information. A three-dimensional scatter plot I is generated with the east coordinate of the detector point as the horizontal axis X, the north coordinate as the vertical axis Y, and the absolute values of the average apparent dominant frequency and amplitude of the detector point as the Z axis. Select receiver points with small amplitude, high frequency and abnormal color in the three-dimensional scatter plot I, extract common receiver point gathers, and compare them with the spectrum analysis to obtain receiver points whose spectrum characteristics are different from the seismic data and normal data. These are the initial control anomaly receiver points, and the preliminary quality control is completed. S2, Perform autocorrelation analysis on shot gathering seismic data. Autocorrelation analysis was performed on the mid-to-deep time window of the shot gathering seismic data to obtain the delay time T0 when the autocorrelation function first crosses the zero point, and the maximum value A0 of the autocorrelation function in the interval [0, T0]. Based on the T0 values of normal and initial control anomalous receivers in the shot gather seismic data after autocorrelation analysis, a T0 value threshold is set, and the dominant frequency amplitude file of the receivers after autocorrelation analysis is output. The T0 threshold value is less than or equal to the T0 value of the initial control anomaly detector point; The detector point dominant frequency amplitude file includes the field file number, detector point number, detector point east coordinate, detector point north coordinate, T0 value, and A0 value. S3, normalize the dominant frequency amplitude file of the receiver point and calculate the ellipsoidal volume value. Normalize the dominant frequency amplitude file of the detector points, calculate the average T0 value, and count the number of detector points whose average T0 value is lower than the T0 value threshold. The ellipsoidal volume value of each receiver point is calculated using the ellipsoidal volume calculation formula. The receiver point number, the east coordinate of the receiver point, the north coordinate of the receiver point, and the ellipsoidal volume value are output as a quality control result text file. S4: Read the quality control result text file, select abnormal detector points, and complete quality monitoring. Read the quality control result text file and generate a three-dimensional scatter plot II with the east coordinate of the detector point as the horizontal axis (X), the north coordinate as the vertical axis (Y), and the ellipsoidal volume of the detector point as the Z axis. When the ratio of the ellipsoidal volume values of any two detector points is ≥100, the detector point with the larger ellipsoidal volume is marked as detector point I. Select detector point I with color anomalies in the three-dimensional scatter plot II and mark it as detector point II; Set an ellipsoidal volume threshold, wherein the ellipsoidal volume threshold is less than or equal to the ellipsoidal volume of detector point II; Select the receiver points in the quality control result text file whose ellipsoidal volume is greater than or equal to the ellipsoidal volume threshold. These are the abnormal receiver points, thus completing the nodal seismograph data quality monitoring.
2. The nodal seismograph data quality monitoring method according to claim 1, characterized in that, The path information includes the field file number of the shot point, the station number and coordinate information of the shot point and the receiver point; When any item of trackhead information is missing, the missing information is added to the trackhead through the observation system.
3. The nodal seismograph data quality monitoring method according to claim 2, characterized in that, Thinning involves first uniformly thinning the shot lines, and then uniformly thinning the shot points; after thinning, the number of shot collections in the seismic data is reduced by 90%.
4. The nodal seismograph data quality monitoring method according to claim 3, characterized in that, The absolute value of the detector amplitude is calculated by selecting a mid-deep time window and using the root mean square method. The average apparent dominant frequency of the detector point is determined by selecting a mid-to-deep time window using the formula: The calculation yields , where MF is the average apparent dominant frequency of the detector point, M is the amplitude sample point, i is the channel number, and T is the main lobe half-width of the autocorrelation function.
5. The nodal seismograph data quality monitoring method according to claim 4, characterized in that, The autocorrelation analysis includes the following steps: By selecting the mid-to-deep time window of the shot gather seismic data, and using the Wiener-Khinchin theorem, the autocorrelation function is calculated through fast Fourier transform and inverse fast Fourier transform. The dominant frequency and amplitude are then calculated using the autocorrelation function. The autocorrelation function is: Rxx(τ)=E[x(t)x(t+τ)], where t is time, τ is the time-shifted variable, x(t) is the amplitude value of the sampling point at time t, x(t+τ) is the amplitude value of the time-shifted variable τ at time t, and E[x(t)x(t+τ)] is the expected value of the product of x(t) and x(t+τ) over the entire time. The value of T0 is the time shift variable τ0 when Rxx(τ) first crosses the zero point; The value of A0 is the maximum value of Rxx(τ) on the interval [0, T0].
6. The nodal seismograph data quality monitoring method according to claim 5, characterized in that, The detector point main frequency amplitude file is an ASCII format text file.
7. The nodal seismograph data quality monitoring method according to claim 6, characterized in that, The method for calculating the volume of the ellipsoid includes the following steps: Calculate the average of the normalized A0 values, and label it as a; The ratio of the number of times the average T0 value is lower than the T0 threshold to the total number of times the detector point appears is denoted as b. Calculate the reciprocal of the normalized average value T0, denoted as c; Through the formula: The volume of the ellipsoid is calculated.
8. A nodal seismograph data quality monitoring device based on the nodal seismograph data quality monitoring method according to any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire shot gather seismic data collected by nodal seismic instruments; The read module is used to read and thin the shot gather seismic data. The preliminary quality control module performs preliminary quality control based on shot gather seismic data to obtain initial control anomaly detector points; The autocorrelation analysis module performs autocorrelation analysis based on shot gather seismic data to obtain the dominant frequency amplitude file of the receiver points; The normalization module performs normalization processing based on the dominant frequency amplitude file of the detector points to obtain the average T0 value and the number of detector points with an average T0 value lower than the T0 value threshold. The calculation module calculates the volume of the ellipsoid based on the data obtained through normalization. The output module outputs the detector point number, the east coordinate of the detector point, the north coordinate of the detector point, and the ellipsoid volume value as a text file of quality control results; The quality control module selects abnormal receiver points based on the quality control result text file and completes the monitoring of nodal seismograph data quality.
9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the nodal seismograph data quality monitoring method according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the nodal seismograph data quality monitoring method according to any one of claims 1-7.