Quality control method for node acquisition data

Abstract

The invention provides a quality control method for node acquisition data. The quality control method comprises the following steps: loading observation system file data; checking data integrity of a detection point and a shot point; carrying out detection point data empty channel recovery rate statistics; carrying out detection point domain interference and multi-attribute statistics; carrying out detection point domain sparse inspection quality control statistics; carrying out shot domain data basic information attribute statistics; carrying out shot domain data interference and multi-attribute statistics; and performing shot domain first arrival fitting intelligent analysis and statistics. Finally, a set of complete quality control method for node acquisition data is formed.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas geophysical seismic data processing, and in particular to a quality control method for node acquisition data. Background Art

[0002] Data quality is a crucial foundation for scientific research and decision-making. The complexity and uncontrollability of field environments pose significant challenges to data quality. In seismic exploration, cabled and cableless methods are two common data acquisition methods. Cableless methods require a large number of nodes to collect seismic data. Their advantages include suitability for a wide range of terrain conditions and the ability to achieve high-density, continuous data acquisition, making geophysical exploration more efficient, accurate, and economical. However, the requirement to ensure that nodes receive data for a long period of time during the acquisition process can lead to issues such as immobility during construction. This has led to the development of node-based data quality control technology in seismic exploration. The core concept of node-based data quality control technology is to continuously monitor the target area using a sufficient number of nodes, thereby obtaining more detailed data. Compared to traditional geophysical exploration, node-based data quality control technology offers higher sampling density and more comprehensive data coverage. However, the data obtained using node-based data quality control technology is not necessarily accurate or reliable. Therefore, ensuring the quality and accuracy of node-based data is a key challenge facing this technology. To solve this problem, it is necessary to develop a complete standardized quality control system for node collection data, which will be conducive to improving the efficient analysis and application research of collected data.

[0003] In view of the characteristics of field data quality control, domestic and foreign scholars have proposed a large number of quality control methods. The current quality control methods for collected data mainly include:

[0004] 1. Multi-attribute data domain quality control method: Single shots in field construction undergo multi-attribute inspection and quality control. First, the monitoring attribute values ​​of the single shot are calculated. The difference between the monitored attribute values ​​of the collected single shot and the reference value of the monitored attribute is compared. The reference value of the monitored attribute is calculated by averaging the monitored attribute values ​​of multiple test shots in the work area. If the monitored attribute value of the single shot is greater than or equal to the reference value, the monitored attribute of the single shot is determined to be qualified. Otherwise, the monitored attribute of the single shot is determined to be unqualified. Healthy reference value parameters that quantify the acquisition quality can be summarized as signal-to-noise ratio, resolution, fidelity, and comprehensive parameters.

[0005] 2. Multi-attribute acquisition domain quality control method: Vibroseis scanning quality control includes parameter inspection and analysis such as excitation TB time, GPS status, COG combination center, and vibroseis attribute information to ensure that the excitation parameters are in line with the construction design. Node instrument parameter quality control uses a handheld terminal for field monitoring. Indoors, the GPS time, XYZ coordinates, and other information of the node are read to determine whether there is time drift and leap second correction. The GPS positioning data is compared with the layout, and any deviation is promptly re-measured to ensure node positioning accuracy. By extracting the GPS positioning data of the node and comparing it with the measured layout coordinates, the error between the two is calculated and controlled within 5m according to technical standards. During production, it can be found that some nodes have errors due to problems with their own positioning accuracy. Some nodes are artificially moved and deviate from the original design theoretical point. This method can promptly detect problems and ensure the quality of node positioning data.

[0006] 3. Patent CN109031418A proposes a method and apparatus for analyzing seismic acquisition quality. This method includes calculating a monitoring attribute value for a single shot in a work area, where a monitoring attribute reference value is calculated by averaging the monitoring attribute values ​​of multiple test shots in the work area. If the monitoring attribute value of the single shot is greater than or equal to the monitoring attribute reference value, the monitoring attribute of the single shot is determined to be qualified; otherwise, the monitoring attribute of the single shot is determined to be unqualified. This method provides a method for analyzing seismic acquisition quality.

[0007] 4. Patent CN109655938 A proposes a method and system for evaluating the quality of shot record acquisition. The method includes: 1) capturing reflector layer information on a seismic stack profile, where the reflection points contained in the reflector layer form reflector layer mapping points; 2) determining the corresponding trace in the shot record based on the common center point of the reflector layer mapping points, and generating a time-distance curve; 3) combining the strike of the layers surrounding the reflection point captured on the seismic stack profile with the time-distance curve generated in step 2) to determine whether the shot record is acceptable or a geological rejection. Based on seismic stack profiles, this invention analyzes the distribution of time-distance curves across the shot gather, organically combining stack profiles with shot records for comparative analysis to examine the reflection wave conditions of the target layer. This allows for a comprehensive analysis of the shot record acquisition quality, providing an effective and objective method for determining whether field data is acceptable or geologically rejectable.

[0008] 5. Patent CN1837859A proposes a 3D seismic data processing quality monitoring technique. This method involves selecting a time window and analysis frequency, performing a fast Fourier transform on each seismic trace of a particular shot within the time window, and obtaining the frequency domain transform of the trace data. Frequency domain median filtering is performed on all seismic traces of the same shot to obtain the statistical excitation energy or noise interference of the shot. The calculated results of all shots are plotted on a plane diagram of each shot point to monitor the 3D excitation energy or noise interference. Normalized autocorrelation is performed on each seismic trace of a particular shot. The autocorrelation statistics of all seismic traces of the same shot are statistically summed to obtain the statistical autocorrelation of the shot. The zero crossing of the statistical autocorrelation of the shot is then found and the zero crossing of all shots is plotted on the plane position of each shot point to complete the excitation wavelet monitoring.

[0009] Current conventional approaches to data acquisition quality control: Existing quality control approaches for acquired data primarily focus on quality inspections of acquisition equipment and received data. Acquisition equipment quality control includes quality control of source excitation, instrument status, and source parameters. Received data quality control relies on relatively simple measures such as energy, signal-to-noise ratio, and frequency. With the widespread use of wireless node devices, conventional inspection techniques are no longer adequate for comprehensive inspections of nodes with wide azimuths, large offsets, full-time reception, and large data volumes. Consequently, quality control techniques for wireless node data struggle to effectively detect and eliminate abnormal data, ensuring the accuracy and validity of acquired data.

[0010] Currently, conventional quality control methods suffer from two major shortcomings. First, traditional quality control methods primarily focus on checking various data attributes. While these methods can be effective, they are ineffective in identifying and resolving issues arising from data collection, lacking systematic quality control methods for collected data. Second, conventional quality control methods primarily rely on qualitative analysis and evaluation, which are subject to numerous human factors and lack objectivity, accuracy, and impartiality, making it difficult to promptly identify problems in data collection. Manual evaluation of the extremely large amounts of data required for high-coverage, high-density, and wide-area node collection is no longer applicable. A set of technical methods for quantifying and evaluating node-collected data is needed. Summary of the Invention

[0011] In view of the above problems, the present invention is proposed to provide a quality control method for node collection data that overcomes the above problems or at least partially solves the above problems.

[0012] According to one aspect of the present invention, a quality control method for node collection data is provided, the quality control method comprising:

[0013] Load observation system file data;

[0014] Check the integrity of detection point and shot point data;

[0015] Statistics of empty channel recovery rate of detection point data;

[0016] Detection point domain interference, multi-attribute statistics;

[0017] Sparse inspection quality control statistics of detection point domain;

[0018] Statistics of basic information attributes of artillery area data;

[0019] Artillery data interference and multi-attribute statistics;

[0020] Intelligent analysis and statistics of the first arrival fitting of the artillery domain.

[0021] Optionally, the loading of observation system file data specifically includes:

[0022] Import the gun and receiver point positions and relationship files, define the observation system file, and perform a preliminary check of the correctness of the observation system file through linear dynamic correction.

[0023] Optionally, the data integrity check of the detection points and shot points specifically includes:

[0024] After loading the detection point data into the observation system, the detection point data and the text defining the detection point are matched and analyzed to check whether there are any detection point data without defined coordinate geographic information to ensure the integrity of the detection point data;

[0025] After loading the detection point data into the observation system, check whether the minimum and maximum file numbers on the same detection line are consistent. If there is a difference, match and analyze the detection point data and the file defining the relationship between the shot detection points to ensure the integrity of the shot point data.

[0026] Optionally, the statistics of the empty channel recovery rate of the detection point data specifically include:

[0027] Count the number of channels and amplitude values ​​for all geophone data points within the work area. Channels with an amplitude of 0 are considered empty. The node recovery rate is calculated as the percentage of normally functioning channels compared to the total number of collected channels, and must be greater than 99%. Also, the equipment availability rate is calculated as the percentage of unfailed node acquisition instruments used in the work area compared to all other equipment, and must be greater than 99%.

[0028] Optionally, the detection point domain interference and multi-attribute statistics include:

[0029] Calculate the amplitude value of the detection point domain data, calculate the detection point data information of 50H industrial electrical interference, analyze and count the amplitude abnormal point information, and the specific function formula is expressed as follows:

[0030] E=∑|A(t)| 2 (1)

[0031] Where A(t) is the amplitude value at time t;

[0032] Calculate the amplitude value of the detection point data after low-pass filtering, analyze and count the data exceeding the average amplitude as low-frequency abnormal interference information of the detection point. The specific function formula is expressed as:

[0033] E = ∑(s_filterd - s_oigingal) 2 (2)

[0034] Among them, s_filtered represents the data after low-pass filtering, and s_original is the original data.

[0035] Optionally, the detection point domain interference and multi-attribute statistics further include:

[0036] Calculate the energy ratio between the nearest single shot and the farthest single shot received by each detection point, form a distribution diagram, and analyze and count the near-far energy ratio information of the detection point;

[0037] Calculate the energy ratio of the data below the first arrival and the data above the first arrival of each detection point, form a distribution diagram, and analyze and count the energy ratio information of the first arrival of the detection point.

[0038] Optionally, the detection point domain sparse inspection quality control statistics specifically include:

[0039] Calculate the ambient noise amplitude and data amplitude of each detection point to form a distribution map, analyze and statistically analyze whether the detection point energy ratio is related to surface factors, and analyze abnormal information of the detection point.

[0040] Optionally, the detection point domain sparse inspection quality control statistics further include:

[0041] Sparsely recovered node data is used to control the quality of the collected data. The amplitude, frequency, signal-to-noise ratio attribute analysis and statistics of typical surface node data are regularly recovered and calculated to reflect the quality control issues of all data in the entire work area.

[0042] Optionally, the statistics of basic information attributes of the artillery area data specifically include:

[0043] Quantitatively analyze the problems existing in the exciting source by using the attribute diagrams of well depth, charge, coverage times, minimum and maximum offset distances of the statistical blast field data;

[0044] Check each node gun one by one to see if there are any abnormal channels, timing errors, or insufficient data duration issues.

[0045] Optionally, the statistics of basic information attributes of the artillery area data specifically include:

[0046] Calculate the amplitude, main frequency, and signal-to-noise ratio attribute maps of the shot domain data, and analyze and compile statistics on the single shot records corresponding to abnormal attributes;

[0047] Statistically check whether the number of permutations and channels corresponding to the gun area data is consistent with the designed observation system, and check whether the data is complete. The specific function formula is expressed as follows:

[0048] SNR=20*log10(A_signal / A_noise) (3)

[0049] Among them, A_signal represents the amplitude value of the seismic signal, and A_nosie represents the amplitude value of the noise.

[0050] Optionally, the artillery area data interference and multi-attribute statistics specifically include:

[0051] Calculate the ratio of the single shot amplitude value after high-pass filtering to the average amplitude of the single shot in the dominant frequency band, and analyze and count the single shot records with strong high-frequency interference;

[0052] Calculate the ratio of the single shot amplitude value after low-pass filtering to the average amplitude of the single shot in the dominant frequency band, and analyze and count the single shot records with strong low-frequency interference;

[0053] Calculate the amplitude of ambient noise above the first arrival of a single shot, and analyze and statistically analyze the relationship between ambient noise and the excitation source.

[0054] Optionally, the intelligent analysis statistics of the shot domain first arrival fitting specifically include:

[0055] After performing elevation static correction and linear dynamic correction on the entire area's gun range data, the first arrival time of the data within the near-offset range is selected;

[0056] The average value of the first arrival time of the shot offset in the horizontal arrangement direction is defined as the shot deviation standard along the arrangement direction;

[0057] The average first arrival time of the vertical alignment direction shot offset is defined as the vertical alignment direction shot deviation standard, and the shots whose first arrival time values ​​exceed the horizontal and vertical standards are analyzed and counted.

[0058] This invention provides a quality control method for node-collected data. The method includes: loading observation system file data; checking the integrity of geophone and shot point data; performing empty channel recovery rate statistics for geophone data; performing geophone domain interference and multi-attribute statistics; performing geophone domain sparsity check quality control statistics; performing shot domain data basic information attribute statistics; performing shot domain data interference and multi-attribute statistics; and performing shot domain first-break fitting intelligent analysis statistics. Ultimately, this method forms a complete set of quality control methods for node-collected data.

[0059] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is a flow chart of a quality control method for node collection data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0063] The terms "comprises" and "comprising" and any variations thereof in the description, embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.

[0064] The technical solution of the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0065] like Figure 1 As shown, the present invention is a quality control method for node collection data. The processing flow includes the following steps:

[0066] Step 1: Import the gun and receiver position and relationship files, define the observation system file, and perform a preliminary check of the correctness of the observation system file through linear dynamic correction.

[0067] Step 2: After loading the detection point data into the observation system, the detection point data and the text defining the detection point are matched and analyzed to check whether there are any detection point data without defined coordinates and other geographic information to ensure the integrity of the detection point data.

[0068] Step 3: After loading the detection point data into the observation system, check whether the minimum and maximum file numbers on the same detection line are consistent. If there is a difference, match and analyze the detection point data and the file defining the relationship between the shot detection points to ensure the integrity of the shot point data.

[0069] Step 4: Count the number of channels and amplitude values ​​for all geophone data points within the work area. Channels with an amplitude of 0 are considered empty. The node recovery rate is calculated as the percentage of normally functioning channels compared to the total number of channels collected, and must be greater than 99%. Also, the equipment availability rate is calculated as the percentage of all non-failed node acquisition instruments used within the work area, and must be greater than 99%.

[0070] Step 5: Calculate the amplitude value of the detection point domain data, calculate the detection point data information of 50H industrial electrical interference, analyze and count the amplitude abnormal point information, and the specific function formula is expressed as follows:

[0071] E=∑|A(t)| 2 (1)

[0072] Where A(t) is the amplitude value at time t.

[0073] Step 6: Calculate the amplitude of the detection point data after low-pass filtering, analyze and count the data exceeding the average amplitude as low-frequency abnormal interference information of the detection point. The specific function formula is expressed as:

[0074] E = ∑(s_filterd - s_oigingal) 2 (2)

[0075] Among them, s_filtered represents the data after low-pass filtering, and s_original is the original data.

[0076] Step 7: Calculate the energy ratio between the nearest single shot and the farthest single shot received by each detection point, form a distribution diagram, and analyze and count information such as the near-far energy ratio of the detection point.

[0077] Step 8: Calculate the energy ratio of the data below the first arrival and the data above the first arrival of each detection point, form a distribution diagram, and analyze and count the information such as the energy ratio of the first arrival of the detection point.

[0078] Step 9: Calculate the ambient noise amplitude and data amplitude of each detection point to form a distribution diagram, analyze and statistically analyze whether the detection point energy ratio is related to surface factors, and analyze the abnormal information of the detection point.

[0079] Step 10: Use sparse recovered node data to perform quality control on the collected data. Regularly recover and calculate the amplitude, frequency, signal-to-noise ratio and other attribute analysis and statistics of typical surface node data, which can reflect the quality control issues of all data in the entire work area to a certain extent.

[0080] Step 11: Statistical analysis of the shot data attributes, such as well depth, charge volume, coverage times, minimum and maximum offsets, can be used to quantitatively analyze the problems with the excitation source. Check each shot individually to see if there are any abnormal channels, timing errors, or insufficient data duration.

[0081] Step 12: Calculate the amplitude, main frequency, signal-to-noise ratio and other attribute graphs of the shot domain data, analyze and count the single shot records corresponding to abnormal attributes. Count whether the number of permutations and channels corresponding to the shot domain data are consistent with the designed observation system, and check whether the data is complete. The specific function formula is expressed as:

[0082] SNR=20*log10(A_signal / A_noise) (3)

[0083] Among them, A_signal represents the amplitude value of the seismic signal, and A_nosie represents the amplitude value of the noise.

[0084] Step 13: Calculate the ratio of the high-pass filtered single-shot amplitude to the average single-shot amplitude within the dominant frequency band, and analyze and compile statistics for single-shot records with strong high-frequency interference. Calculate the ratio of the low-pass filtered single-shot amplitude to the average single-shot amplitude within the dominant frequency band, and analyze and compile statistics for single-shot records with strong low-frequency interference. Calculate the amplitude of the ambient noise above the first arrival of the single shot, and analyze and compile statistics for the relationship between the ambient noise and the excitation source.

[0085] Step 14: After performing elevation static correction and linear dynamic correction on the entire area’s gun area data, select the data in the near-offset range and pick up the first arrival time. Calculate the average first arrival time of the horizontal offset in the horizontal arrangement direction and define it as the standard for gun deviation along the arrangement direction. Calculate the average first arrival time of the vertical offset in the vertical arrangement direction and define it as the standard for gun deviation in the vertical arrangement direction. Analyze and count the guns whose first arrival time exceeds the horizontal and vertical standards.

[0086] A quality control method for node collection data,

[0087] First, we use comprehensive geographic information acquisition quality analysis technology. We use shot-receiver point relationship files to perform a detailed matching analysis of collected node data, checking for missing shots, missing alignments, or missing traces in the common receiver point gathers. We also calculate the total number of normal operating traces for all collected node data and the total number of traces designed during construction. We then calculate the percentage of the two to determine whether the node recovery rate meets requirements.

[0088] Secondly, we use quantitative analysis technology for quality control at the detection point domain. This includes statistical analysis of abnormally strong energy interference, 50Hz industrial electrical interference, strong low-frequency interference, the energy ratio between the nearest and farthest single shots received at each detection point, the energy ratio between data below the first arrival and data above the first arrival at each detection point, environmental noise analysis at each detection point, and energy statistics for each detection point based on the characteristic of high energy at close offsets and low energy at far offsets. We perform quality control on node data collected through fixed-point sparse recovery, using regular sampling to perform quantitative comparative analysis of signal-to-noise ratio, energy, and dominant frequency attributes.

[0089] Finally, quantitative analysis technology for shot-point domain quality control is employed. This includes statistical analysis of shot-point depth attribute maps, shot-point elevation maps, charge attribute maps, work-area coverage times, minimum and maximum offset attribute maps, and other shot-domain attributes, including energy, frequency, signal-to-noise ratio, high-frequency interference, low-frequency interference, and single-shot background noise. Based on this, shot-point elevation static correction and linear dynamic correction are performed. First arrival times are selected for data within the near-offset range, and the difference between the first arrival averages of positive and negative offsets in the vertical and horizontal directions is calculated to estimate shot deviations in the vertical and horizontal directions. Shots with differences exceeding the average values ​​undergo manual analysis of the cause and deviation. Implementing these techniques ultimately forms a complete quality control method for node-collected data.

[0090] Beneficial effects:

[0091] On the one hand, the comparative analysis technology of gun inspection point files and acquisition node data was systematically sorted out to ensure that the node data recovery rate meets the acquisition design requirements.

[0092] Furthermore, a series of analytical quality control techniques for node data in both the geophone and shot domains have been developed, addressing key issues in seismic acquisition quality control caused by the significant lag in data collection across all nodes. Ultimately, a comprehensive set of quality control methods for node-based data has been developed.

[0093] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quality control method for node collection data, characterized in that: The quality control method includes: Load observation system file data; Check the integrity of detection point and shot point data; Statistics of empty channel recovery rate of detection point data; Detection point domain interference, multi-attribute statistics; Sparse inspection quality control statistics of detection point domain; Statistics of basic information attributes of artillery area data; Artillery data interference and multi-attribute statistics; Intelligent analysis and statistics of the first arrival fitting of the artillery domain.

2. A quality control method for node collection data according to claim 1, characterized in that: The loading of observation system file data specifically includes: Import the gun and receiver point positions and relationship files, define the observation system file, and perform a preliminary check of the correctness of the observation system file through linear dynamic correction.

3. A quality control method for node collection data according to claim 1, characterized in that: The data integrity check of the detection points and shot points specifically includes: After loading the detection point data into the observation system, the detection point data and the text defining the detection point are matched and analyzed to check whether there are any detection point data without defined coordinate geographic information to ensure the integrity of the detection point data; After loading the detection point data into the observation system, check whether the minimum and maximum file numbers on the same detection line are consistent. If there is a difference, match and analyze the detection point data and the file defining the relationship between the shot detection points to ensure the integrity of the shot point data.

4. A quality control method for node collection data according to claim 1, characterized in that: The statistics of the empty channel recovery rate of the detection point data specifically include: Count the number of channels and amplitude values ​​for all geophone data points within the work area. Channels with an amplitude of 0 are considered empty. The node recovery rate is calculated as the percentage of normally functioning channels compared to the total number of collected channels, and must be greater than 99%. Also, the equipment availability rate is calculated as the percentage of unfailed node acquisition instruments used in the work area compared to all other equipment, and must be greater than 99%.

5. A quality control method for node collection data according to claim 1, characterized in that: The detection point domain interference and multi-attribute statistics include: Calculate the amplitude value of the detection point domain data, calculate the detection point data information of 50H industrial electrical interference, analyze and count the amplitude abnormal point information, and the specific function formula is expressed as follows: E=∑|A(t)| 2 (1) Where A(t) is the amplitude value at time t; Calculate the amplitude of the detection point data after low-pass filtering, analyze and count the data exceeding the average amplitude as low-frequency abnormal interference information of the detection point. The specific function formula is expressed as: E=∑(s_filterd-s_oigingal) 2 (2) Among them, s_filtered represents the data after low-pass filtering, and s_original is the original data.

6. A quality control method for node collection data according to claim 1, characterized in that: The detection point domain interference and multi-attribute statistics also include: Calculate the energy ratio between the nearest single shot and the farthest single shot received by each detection point, form a distribution diagram, and analyze and count the near-far energy ratio information of the detection point; Calculate the energy ratio of the data below the first arrival and the data above the first arrival of each detection point, form a distribution diagram, and analyze and count the energy ratio information of the first arrival of the detection point.

7. A quality control method for node collection data according to claim 1, characterized in that: The detection point domain sparse inspection quality control statistics specifically include: Calculate the ambient noise amplitude and data amplitude of each detection point to form a distribution map, analyze and statistically analyze whether the detection point energy ratio is related to surface factors, and analyze abnormal information of the detection point.

8. A quality control method for node collection data according to claim 1, characterized in that: The detection point domain sparse inspection quality control statistics also include: Sparsely recovered node data is used to control the quality of the collected data. The amplitude, frequency, signal-to-noise ratio attribute analysis and statistics of typical surface node data are regularly recovered and calculated to reflect the quality control issues of all data in the entire work area.

9. A quality control method for node collection data according to claim 1, characterized in that: The basic information attribute statistics of the artillery area data specifically include: Quantitatively analyze the problems existing in the exciting source by using the attribute diagrams of well depth, charge, coverage times, minimum and maximum offset distances of the statistical blast field data; Check each node gun one by one to see if there are any abnormal channels, timing errors, or insufficient data duration issues.

10. A quality control method for node collection data according to claim 1, characterized in that: The basic information attribute statistics of the artillery area data specifically include: Calculate the amplitude, main frequency, and signal-to-noise ratio attribute maps of the shot area data, and analyze and compile statistics on the single shot records corresponding to abnormal attributes; Statistically check whether the number of permutations and channels corresponding to the gun area data is consistent with the designed observation system, and check whether the data is complete. The specific function formula is expressed as follows: SNR=20*log10(A_signal / A_noise) (3) Among them, A_signal represents the amplitude value of the seismic signal, and A_nosie represents the amplitude value of the noise.

11. A quality control method for node collection data according to claim 1, characterized in that: The artillery area data interference and multi-attribute statistics specifically include: Calculate the ratio of the single shot amplitude value after high-pass filtering to the average amplitude of the single shot in the dominant frequency band, and analyze and count the single shot records with strong high-frequency interference; Calculate the ratio of the single shot amplitude value after low-pass filtering to the average amplitude of the single shot in the dominant frequency band, and analyze and count the single shot records with strong low-frequency interference; Calculate the amplitude of ambient noise above the first arrival of a single shot, and analyze and statistically analyze the relationship between ambient noise and the excitation source.

12. A quality control method for node collection data according to claim 1, characterized in that: The intelligent analysis statistics of the first arrival fitting in the artillery domain specifically include: After performing elevation static correction and linear dynamic correction on the entire area's gun area data, the first arrival time of the data within the near-offset range is selected; The average value of the first arrival time of the shot offset in the horizontal arrangement direction is defined as the shot deviation standard along the arrangement direction; The average first arrival time of the vertical alignment direction shot offset is defined as the vertical alignment direction shot deviation standard, and the shots whose first arrival time values ​​exceed the horizontal and vertical standards are analyzed and counted.

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