Automatic geophysical prospecting data processing method and system based on wide-area electromagnetic method

Abstract

The invention relates to the technical field of geophysical exploration application, and discloses a geophysical exploration data automatic processing method and system based on a wide-area electromagnetic method. The method comprises the following steps: acquiring original detection data of a wide-area electromagnetic method and preprocessing the original detection data to obtain processed detection data; extracting effective data according to the processed detection data, and automatically generating a GPS measurement data result atlas according to the effective data based on a spatial interpolation method; performing data quality inspection statistics based on the processed detection data and the GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table; according to the processed detection data, generating wide-area electromagnetic measurement original data achievements and various geophysical detection achievement atlas; and drawing a wide-area electromagnetic curve graph and a wide-area electromagnetic quasi-sectional diagram based on the processed detection data and GPS measurement data result atlas, and generating a wide-area electromagnetic inversion file. According to the invention, automatic processing of geophysical exploration original data from acquisition to result output is realized.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration application technology, and in particular to a method and system for automatically processing geophysical exploration data based on wide-area electromagnetic method. Background Art

[0002] Currently, geophysical survey professionals conducting wide-area electromagnetic surveys face inconsistent instrument models and data output formats. This makes analyzing raw data extremely cumbersome and results in low conversion rates. Geophysical data comes from a wide variety of sources, formats, and volumes. Continuing to use existing technologies to manage geophysical data requires specialized management for each source. This not only wastes significant human and financial resources but also makes data exchange, updating, and retrieval difficult. Summary of the Invention

[0003] The main purpose of the present invention is to provide a method and system for automatic processing of geophysical exploration data based on wide-area electromagnetic method, aiming to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for automatically processing geophysical exploration data based on wide-area electromagnetic method, comprising:

[0005] Acquiring raw wide-area electromagnetic detection data, and performing data preprocessing on the raw wide-area electromagnetic detection data to obtain processed detection data;

[0006] Extracting valid data according to the processed detection data, and automatically generating a GPS measurement data result atlas according to the valid data based on a spatial interpolation method;

[0007] Performing data quality inspection and statistics based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table;

[0008] generating wide-area electromagnetic survey raw data results and various geophysical exploration results atlases based on the processed detection data;

[0009] Based on the processed detection data and GPS measurement data result atlas, a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram are drawn, and a wide-area electromagnetic inversion file is generated.

[0010] In some embodiments, the acquiring of raw wide-area electromagnetic detection data and the preprocessing of the raw wide-area electromagnetic detection data to obtain processed detection data include:

[0011] Obtain raw wide-area electromagnetic detection data;

[0012] Processing missing values, duplicate values, and outliers on the original wide-area electromagnetic detection data to obtain cleaned data;

[0013] The cleaned data is standardized, normalized and discretized to achieve data conversion and obtain processed detection data.

[0014] In some embodiments, extracting valid data based on the processed detection data and automatically generating a GPS measurement data results atlas based on the valid data based on a spatial interpolation method includes:

[0015] extracting original GPS measurement data based on the processed detection data;

[0016] Extracting data that meets preset requirements according to the representation fields of the original GPS measurement data;

[0017] Based on the satellite signal strength and positioning precision index, outliers in the data that meet the preset requirements are eliminated to obtain valid data;

[0018] A GPS measurement data results atlas is generated based on the valid data using a spatial interpolation method.

[0019] In some embodiments, the data quality inspection statistics are performed based on the processed detection data and the GPS measurement data achievement atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table, including:

[0020] Extracting GPS quality inspection raw data based on the processed detection data;

[0021] Importing the GPS measurement data results atlas;

[0022] The GPS quality inspection raw data and the GPS measurement data result atlas are subjected to data screening and preset standard operations to output a GPS quality inspection statistical report and a quality inspection statistical table.

[0023] In some embodiments, generating wide-area electromagnetic measurement raw data results and various geophysical exploration results atlases based on the processed detection data includes:

[0024] Extracting measurement point coordinates, field source coordinates, electric field files, current files, original electric field files, and natural electric field files based on the processed detection data;

[0025] Calculating wide-area electromagnetic measurement data results based on the measurement point coordinates, field source coordinates, electric field files, current files, original electric field files, and natural electric field files based on an inversion algorithm;

[0026] A wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas are generated based on the processed detection data.

[0027] In some embodiments, generating a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas based on the processed detection data includes:

[0028] Extracting checkpoint data for the checked point based on the processed detection data;

[0029] Calculating the checked points according to the checkpoint data in accordance with a preset quality inspection formula to automatically generate a wide-area bathymetric quality inspection point atlas;

[0030] extracting an original indoor consistency file and an original outdoor consistency file according to the processed detection data;

[0031] The original indoor consistency files and the original outdoor consistency files are automatically batch-generated into wide-area electromagnetic instrument consistency atlases through data integration and calculation.

[0032] In some embodiments, the drawing of a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and the GPS measurement data result atlas, and the generation of a wide-area electromagnetic inversion file, includes:

[0033] extracting field source coordinates, electric field data, and current data based on the processed detection data;

[0034] Importing the GPS measurement data results atlas;

[0035] A wide-area electromagnetic curve diagram is drawn based on a comprehensive data file formed by the GPS measurement data atlas, field source coordinates, electric field data, and current data; wherein the wide-area electromagnetic curve diagram includes: a wide-area electromagnetic depth measurement artificial field and natural field curve comparison diagram and a wide-area electromagnetic depth measurement apparent resistivity and electric field curve diagram;

[0036] drawing a wide-area electromagnetic pseudo-section diagram based on the processed detection data;

[0037] A wide-area electromagnetic inversion file is generated based on a comprehensive data file formed by the GPS measurement data results atlas, field source coordinates, electric field data, and current data.

[0038] In some embodiments, drawing a wide-area electromagnetic pseudo-section map based on the processed detection data includes:

[0039] extracting raw data generated by the pseudo-section diagram based on the processed detection data;

[0040] Performing data analysis and standard calculation on the raw data generated by the pseudo-section diagram to generate an apparent resistivity pseudo-section diagram and a normalized electric field pseudo-section diagram;

[0041] The apparent resistivity pseudo-section diagram and the normalized electric field pseudo-section diagram are used as wide-area electromagnetic pseudo-section diagrams.

[0042] In some embodiments, the method further comprises:

[0043] When drawing wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections, the jar package provided in the Matlab tool is called to analyze and calculate the apparent resistivity data based on the jar package;

[0044] The vector data to raster interface in the Qgis platform is called, and the format conversion of the pseudo-section map result is performed based on the vector data to raster interface.

[0045] In addition, to achieve the above-mentioned purpose, the present invention also proposes an automatic processing system for geophysical exploration data based on wide-area electromagnetic method, comprising:

[0046] A preprocessing module is used to obtain raw detection data of the wide-area electromagnetic method, perform data preprocessing on the raw detection data of the wide-area electromagnetic method, and obtain processed detection data;

[0047] A data processing module is used to extract valid data based on the processed detection data, and automatically generate a GPS measurement data result atlas based on the valid data based on a spatial interpolation method;

[0048] A quality inspection module is used to perform data quality inspection statistics based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table;

[0049] An atlas generation module is used to generate wide-area electromagnetic measurement raw data results and various geophysical exploration results atlases based on the processed detection data;

[0050] The drawing module is used to draw a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and the GPS measurement data result atlas, and generate a wide-area electromagnetic inversion file.

[0051] The present invention provides an automated processing method for geophysical exploration data based on wide-field electromagnetic methods, comprising: acquiring raw wide-field electromagnetic exploration data, performing data preprocessing on the raw wide-field electromagnetic exploration data to obtain processed exploration data; extracting valid data based on the processed exploration data, and automatically generating a GPS measurement data results atlas based on the valid data using a spatial interpolation method; performing data quality inspection and statistics based on the processed exploration data and the GPS measurement data results atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table; generating wide-field electromagnetic measurement raw data results and various geophysical exploration results atlases based on the processed exploration data; drawing wide-field electromagnetic curves and wide-field electromagnetic pseudo-sections based on the processed exploration data and the GPS measurement data results atlas, and generating wide-field electromagnetic inversion files. In the present invention, the method automatically generates GPS measurement data results atlases, GPS quality inspection statistics, wide-field electromagnetic measurement raw data results, various geophysical exploration results atlases, wide-field electromagnetic curves and wide-field electromagnetic pseudo-sections, and inversion files, thereby realizing automated processing of geophysical exploration raw data from acquisition to results output. Automating the collection of geophysical data collected using wide-area electromagnetic methods saves manpower and financial resources required for data management, updating, and searching, reducing the cost of geophysical exploration and acquisition. This provides a convenient and effective work path for geophysical exploration application workers and researchers, and improves the application conversion rate of collected data results. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the structure of an electronic device in the hardware operating environment involved in an embodiment of the present invention;

[0053] Figure 2 1. A flow chart of an embodiment of a method for automatic processing of geophysical exploration data based on wide-area electromagnetic method according to the present invention;

[0054] Figure 3 A technical roadmap for the embodiments of the present invention;

[0055] Figure 4 Schematic diagram of a GPS quality inspection data statistical quality inspection method according to an embodiment of the present invention;

[0056] Figure 5 This is a flow chart of the results of calculating wide-area electromagnetic measurement raw data using an inversion algorithm according to an embodiment of the present invention;

[0057] Figure 6 Schematic diagram of a method for checking wide-area bathymetric quality checkpoints according to an embodiment of the present invention;

[0058] Figure 7 Schematic diagram of a wide-area electromagnetic consistency testing method according to an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of an atlas of wide-area electromagnetic sounding quality checkpoints involved in an embodiment of the present invention;

[0060] Figure 9 A schematic diagram of a wide-area electromagnetic consistency test atlas involved in an embodiment of the present invention;

[0061] Figure 10 A schematic diagram of a wide-area electromagnetic curve diagram involved in an embodiment of the present invention;

[0062] Figure 11 A schematic diagram of a pseudo-sectional view of an embodiment of the present invention;

[0063] Figure 12 A schematic diagram of the process of drawing a wide-area electromagnetic pseudo-section diagram according to an embodiment of the present invention;

[0064] Figure 13 This is a structural block diagram of an embodiment of the geophysical exploration data automatic processing system based on wide-area electromagnetic method of the present invention.

[0065] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0068] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0069] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiment of the present invention.

[0070] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 1003 may optionally include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM memory) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may optionally be a storage device independent of the aforementioned processor 1001.

[0071] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0072] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and an automatic processing program for geophysical exploration data based on wide-area electromagnetic method.

[0073] exist Figure 1In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the automatic processing program of geophysical data based on wide-area electromagnetic method stored in the memory 1005 through the processor 1001, and executes the automatic processing method of geophysical data based on wide-area electromagnetic method provided by the embodiment of the present invention.

[0074] The present invention provides a method and system for automatically processing geophysical exploration data based on wide-area electromagnetic method.

[0075] The embodiment of the present invention provides a method for automatically processing geophysical exploration data based on wide-area electromagnetic method, referring to Figure 2 , Figure 2 The figure is a flow chart of an embodiment of a method for automatic processing of geophysical exploration data based on wide-area electromagnetic method according to the present invention.

[0076] like Figure 2 As shown, the automatic processing method of geophysical exploration data based on wide-area electromagnetic method includes:

[0077] Step S100: acquiring raw wide-area electromagnetic detection data, and performing data preprocessing on the raw wide-area electromagnetic detection data to obtain processed detection data;

[0078] Step S200: extracting valid data according to the processed detection data, and automatically generating a GPS measurement data result atlas according to the valid data based on a spatial interpolation method;

[0079] Step S300: performing data quality inspection and statistics based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table;

[0080] Step S400: generating wide-area electromagnetic survey raw data results and various geophysical exploration results atlases based on the processed detection data;

[0081] Step S500: drawing a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and the GPS measurement data result atlas, and generating a wide-area electromagnetic inversion file.

[0082] It should be noted that the execution subject in this embodiment may be an electronic device, which may be a computer device with data processing capabilities, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, a computer device is used as an example for illustration.

[0083] Understandably, the measuring instruments and tools currently used by geophysical exploration professionals in wide-area electromagnetic surveys lack uniformity in instrument models and data output formats. This makes analyzing raw data extremely cumbersome and results in low conversion rates. Against this backdrop, the automated wide-area electromagnetic geophysical data processing method proposed in this embodiment achieves, for the first time, an integrated transformation of the entire process from parsing, calculating, and mapping raw geophysical field data. This not only improves the efficiency of professionals' back-office work but also increases the conversion rate of data collected into practical applications.

[0084] Specifically, the wide-field electromagnetic method (WFEM) is an artificial-source frequency-domain electromagnetic sounding technique. Traditional electromagnetic exploration techniques, such as controlled-source audio-frequency magnetotellurics (CSAMT) and the MELOS method, have limitations when detecting underground structures. For example, the CSAMT method produces weak signals in the far-field region, while the MELOS method requires a complex calibration process. The wide-field electromagnetic method combines the advantages of both methods while avoiding their limitations. Features of the wide-field electromagnetic method include: Use of artificial sources: Unlike natural-source electromagnetic methods, the wide-field electromagnetic method employs artificially generated electromagnetic fields, which helps overcome the randomness of natural sources and improves data acquisition accuracy. Expanded observation scope: It inherits the advantages of the CSAMT method, such as using artificial sources to overcome source randomness, but also eliminates the disadvantage of CSAMT's weak signals in the far-field region, thereby expanding the observation range. Global applicability: The wide-field electromagnetic method does not rely on the Carnelian formula, but instead uses a formula applicable to the entire region to calculate apparent resistivity, thereby improving observation accuracy and efficiency. Improved exploration depth and accuracy: Compared to other electromagnetic methods, wide-field electromagnetic methods can achieve greater exploration depths and significantly improve measurement accuracy under the same transmission and reception distance conditions. Breaking through the limitations of traditional electromagnetic methods: Wide-field electromagnetic methods break through the theoretical limitations of traditional electromagnetic methods that approximate electromagnetic waves into "near zone," "transition zone," and "far zone." By using precise electromagnetic field expressions, they improve the accuracy of data processing and interpretation.

[0085] In this embodiment, the wide-area electromagnetic method is used as the main process to realize the automatic processing of geophysical exploration raw data from collection to output of results, such as Figure 3 As shown, the method described in this embodiment automatically generates various geophysical exploration results atlases, including GPS survey data atlases, GPS measurement point data quality inspection statistics, wide-area electromagnetic survey raw data results, wide-area electromagnetic sounding quality checkpoint atlases, wide-area electromagnetic instrument consistency atlases, wide-area electromagnetic apparent resistivity curves, wide-area electromagnetic inversion file generation, and pseudo-section maps. The following describes the detailed steps in detail.

[0086] In one embodiment, wide-area electromagnetic method original detection data is obtained, and data preprocessing is performed on the wide-area electromagnetic method original detection data to obtain processed detection data, including: obtaining wide-area electromagnetic method original detection data; performing missing value, duplicate value and outlier processing on the wide-area electromagnetic method original detection data to obtain data-cleaned data; and standardizing, normalizing and discretizing the data after data cleaning to achieve data conversion to obtain processed detection data.

[0087] Specifically, data input involves users entering raw wide-area electromagnetic survey data (e.g., GPS survey data atlases, GPS quality inspection data statistics, wide-area electromagnetic survey raw data, wide-area electromagnetic inversion files, etc.) and related report content into a computer. Raw wide-area electromagnetic survey data can be obtained through file upload, database import, and other methods.

[0088] For example, wide-area electromagnetic (WAM) raw survey data includes GPS survey data atlases, GPS quality inspection data statistics, WAM raw data, and WAM inversion files. Reports may include text descriptions, charts, and maps. Users upload data files through the interface provided by their computer devices.

[0089] Specifically, data preprocessing: Preprocessing input data is a critical initial step in the data analysis and mining process, improving data quality and making subsequent analysis results more accurate and reliable. In this embodiment, data cleaning can be achieved by processing missing values, duplicate values, and outliers, and data transformation can be achieved by standardization, normalization, and discretization, although this embodiment does not limit these methods.

[0090] For example, data cleaning. Handling missing values: For missing data points, the following methods can be used: directly delete records with missing values, or use the mean, median, mode, or model-based prediction methods to fill missing values. Handling duplicate values: Delete identical records to ensure that each data point is analyzed only once. Handling outliers: Use statistical methods (such as standard deviation, interquartile range) or rule-based methods to identify and eliminate outliers.

[0091] For example, data conversion. Standardization: convert the data into standardized data with zero mean and unit variance so that data of different magnitudes can be effectively compared. Normalization: scale the data to a fixed range (such as 0 to 1) to eliminate the influence of different dimensions. Discretization: convert continuous variables into categorical variables. For example, continuous resistivity values ​​can be divided into different levels for subsequent analysis and processing. If the data comes from different files or systems, they need to be integrated together for unified analysis. After preprocessing, the data is verified again to ensure that the processed data meets the analysis requirements. Through the above steps, the collected wide-area electromagnetic detection data is effectively input and preprocessed, laying a solid foundation for subsequent data analysis and mining. The preprocessing process not only improves the quality of the data, but also makes the data more suitable for subsequent inversion algorithms and model construction.

[0092] In one embodiment, valid data is extracted based on the processed detection data, and a GPS measurement data results atlas is automatically generated based on the valid data based on a spatial interpolation method, including: extracting original GPS measurement data based on the processed detection data; extracting data that meets preset requirements based on a representation field of the original GPS measurement data; eliminating outliers in the data that meets the preset requirements based on satellite signal strength and positioning precision factor indicators to obtain valid data; and generating a GPS measurement data results atlas based on the valid data using a spatial interpolation method.

[0093] Specifically, the GPS measurement data results atlas automatically selects valid data that meets the requirements from the raw GPS measurement data based on the indicated fields. It also removes outliers caused by signal obstruction, equipment failure, and other reasons. It uses indicators such as satellite signal strength and positioning dilution of precision (PDOP) to determine the valid data and uses spatial interpolation methods to generate the corresponding results atlas (GPS measurement data results atlas).

[0094] For example, the raw GPS measurement data collected by the GPS receiver is extracted from the processed detection data. This data typically includes satellite signals, timestamps, pseudoranges, carrier phases, etc. The above data preprocessing process can perform a preliminary check on the raw geophysical acquisition data, remove obviously erroneous or invalid data points, such as receiver failures, satellite signal loss, etc., and obtain the raw GPS measurement data that has undergone data preprocessing.

[0095] For example, data validity judgment. Satellite signal strength judgment: Check the satellite signal strength corresponding to each data point, and eliminate data caused by weak signal or obstruction. Positioning precision factor (PDOP) judgment: Use the PDOP value to evaluate positioning accuracy. If the PDOP value exceeds the preset threshold (for example, 6 or 7), the positioning accuracy is considered insufficient, and the corresponding data point is eliminated. Valid data extraction: According to the above-mentioned validity judgment criteria, valid data that meets the requirements are automatically selected. These valid data will be used for subsequent spatial interpolation and result atlas generation. Further analysis of the valid data can also be performed to evaluate its quality, such as positioning error, data reliability, etc., to ensure that the generated atlas has high accuracy. Among them, the validity criteria include but are not limited to: satellite signal quality: based on the signal-to-noise ratio (SNR) threshold, for example, the data is considered valid only when the SNR is greater than a certain value (such as 20dB); positioning accuracy: based on the PDOP value, generally the lower the PDOP value, the higher the positioning accuracy, and a PDOP upper limit (such as 5 or 6) is set as the judgment criterion; data integrity: the data must be complete, for example, it must contain all required fields and there must be no missing values; timestamp consistency: ensure that the timestamp of the data is within a reasonable time range, without obvious time jumps or omissions.

[0096] For example, spatial interpolation involves interpolating valid data using spatial interpolation methods (such as kriging, spline, and inverse distance weighted interpolation) to generate continuous grid data. The interpolated data is then converted into graphical formats, such as contour maps and color block maps. Furthermore, necessary elements such as legends, axes, titles, and scales can be added to generate a complete atlas of the results, based on user needs.

[0097] In one embodiment, data quality inspection statistics are performed based on the processed detection data and the GPS measurement data results atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table, including: extracting GPS quality inspection original data based on the processed detection data; importing the GPS measurement data results atlas; performing data screening and preset standard operations on the GPS quality inspection original data and the GPS measurement data results atlas to output a GPS quality inspection statistical report and a quality inspection statistical table.

[0098] Specifically, GPS quality inspection data statistics: implement quality inspection of original GPS data, import GPS measurement results table and GPS quality inspection original data, and output GPS quality inspection statistical report and quality inspection statistical table after data screening and predetermined standard operation (preset standard operation) of the two original data. The quality inspection method is as follows: Figure 4 shown.

[0099] For example, import the GPS measurement results table (GPS measurement data results atlas): import the data file containing the GPS measurement results, parse the file, and extract necessary fields such as measurement point ID, measurement time, and measurement value (such as coordinates, speed, etc.). Import GPS quality inspection raw data, which usually includes information such as signal strength, PDOP value, positioning status, etc. Data screening: check data integrity, check data format and type, and make validity judgments based on preset quality standards. Calculate statistics: perform statistical analysis on the filtered data, calculate statistics such as mean, standard deviation, maximum, minimum, etc., compare the measurement value with the quality inspection value, and calculate deviation and error. A quality score can be assigned to each record according to the preset quality scoring standard. The quality score can be based on multiple factors, such as positioning accuracy, signal strength, observation time, etc. Based on the calculated statistics and quality score, a quality inspection statistics table is generated. The statistics table may include measurement point ID, measurement time, measurement value, quality inspection value, deviation, error, quality score, etc. Generate a report containing detailed quality inspection information. This report may include: an overview of the overall quality inspection results, such as pass rate and failure records; detailed quality inspection data for each measurement point, including statistics and quality scores; graphical presentation of the quality inspection results, such as histograms and scatter plots; and recommendations for follow-up improvements based on the quality inspection results. Through these steps, the quality inspection process for raw GPS data is completed, and quality inspection statistics and reports are output for user analysis and decision-making. These documents are crucial for ensuring the quality and reliability of GPS data.

[0100] In one embodiment, wide-area electromagnetic measurement raw data results and various geophysical exploration results atlases are generated based on the processed detection data, including: extracting measurement point coordinates, field source coordinates, electric field files, current files, original electric field files and natural electric field files based on the processed detection data; calculating wide-area electromagnetic measurement data results based on the measurement point coordinates, field source coordinates, electric field files, current files, original electric field files and natural electric field files based on an inversion algorithm; generating a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas based on the processed detection data.

[0101] Specifically, the original data results of wide-area electromagnetic measurement: input the measurement point coordinates, field source coordinates, electric field file, current file, original electric field file, and natural electric field file, and calculate the measurement data results (wide-area electromagnetic measurement data results) through the inversion algorithm. The calculation process is as follows: Figure 5 As shown. Figure 5As shown in the figure, the coordinates of the corresponding point numbers in the electric field file are extracted through the measurement point coordinates (source file 1), the coordinates of field sources A and B are extracted through the field source coordinates (source file 2), and column data are extracted through the electric field file (source file 3), current file (source file 4), original electric field file (source file 5) and natural electric field file (source file 6). The data extracted from the above source files are summarized and the data set (target file 1) is output. The data set is inverted and calculated to output the comprehensive data file (target file 2).

[0102] Specifically, the key contents of the inversion algorithm are as follows:

[0103]

[0104] It should be noted that the above code is written in MATLAB and is used to execute the inversion algorithm. This algorithm processes data from a series of stations and creates a data structure (Dot) for each station to store various information related to that station. This code processes the observation data from a group of stations and organizes this data into the Dot data structure for subsequent inversion analysis. Dot can be an array of structures, with each element containing all the relevant data and observations for a station. In an example, the above code can be described as: start a loop and iterate over each element in the station array, where jd is the loop variable; if the current station station (jd) is not equal to a variable first, it means that a new station is encountered; set the current station to first; add a new ndot, which may be to record the index of the new station; reset the fNum variable, which may be used to record the number of different frequencies in the current station; create a new Dot data structure for the current station and store the following information: Stati is set to the identifier of the current station, A, B, M, N store the position or vector data related to the station, MN_center calculates the coordinates of the center of two positions (M and N), and MN calculates the distance between two positions (M and N); if the current station is the same as first, it means that the same station is still being processed, so fNum is increased to record the new frequency in the station; regardless of whether it is a new station, the following information will be updated for the current station and frequency: Freq records the current frequency f(jd), Amps records the current amplitude I(jd), V records the observed voltage V_obs(jd), and Emn calculates a value related to voltage and distance, which may be the electric field strength.

[0105] For example, the inversion process of the raw data results of wide-area electromagnetic surveys is the process of analyzing and calculating the measured data to obtain the electrical structure of the underground medium. Measurement point coordinates: Provide the geographic coordinates of all measurement points, usually longitude, latitude and elevation. Field source coordinates: Provide the location coordinates of the artificial field source. Electric field file: Contains the electric field data recorded during the measurement. Current file: Contains the current intensity data emitted by the field source. Raw electric field file: Contains the raw electric field data without any processing. Natural electric field file: Contains natural electric field data without interference from artificial field sources. Data preprocessing: Ensure that the format of all input data is consistent to facilitate subsequent processing. Remove invalid, erroneous or abnormal data points. Normalize the electric field and current data for ease of analysis. Based on the assumed model of the underground medium, a forward model is constructed to simulate the propagation process of electromagnetic waves in the underground medium. Use the forward algorithm to calculate the response corresponding to the initial model, that is, convert the model parameters into the expected electric and magnetic field distributions. Select an appropriate inversion algorithm, such as the least squares method, gradient descent method, Newton method, genetic algorithm, etc. Constructing an objective function: This function measures the difference between observed data and model predictions. Typically, the objective function is to minimize the sum of squared errors between the observed and predicted data. Model parameters are adjusted iteratively to minimize the difference between the model's predictions and the observed data. The inversion process is checked for convergence. If the objective function falls below a preset threshold or the number of iterations reaches an upper limit, the iterations are terminated. The inverted geoelectric model is output, including resistivity maps and electrical structural models. The inversion results are visualized, generating charts, 3D models, and other tools to produce wide-area electromagnetic survey data for easy analysis and interpretation.

[0106] In one embodiment, a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas are generated based on the processed detection data, including: extracting checkpoint data for the checked points based on the processed detection data; calculating the checked points according to a preset quality inspection formula based on the checkpoint data to automatically generate a wide-area electromagnetic sounding quality checkpoint atlas; extracting original indoor consistency files and original outdoor consistency files based on the processed detection data; and automatically generating a wide-area electromagnetic instrument consistency atlas in batches through data integration and calculation of the original indoor consistency files and the original outdoor consistency files.

[0107] Specifically, wide-area electromagnetic sounding quality checkpoint atlas: for the checked points, the checkpoint data is used to calculate the checked points according to the quality inspection formula to automatically generate the wide-area electromagnetic sounding quality checkpoint error statistics calculation table, the wide-area electromagnetic sounding quality checkpoint data atlas set error statistics summary table and the wide-area electromagnetic sounding quality checkpoint data atlas report file. The specific inspection methods are as follows: Figure 6 shown.

[0108] Specifically, wide-area electromagnetic consistency test atlas: This function can automatically generate wide-area electromagnetic sounding instrument performance test atlases in batches through data integration and calculation of the original indoor and outdoor consistency TXT files, including: wide-area instrument indoor consistency curve comparison chart, instrument outdoor consistency data results table, instrument outdoor consistency each channel mean square relative error table, consistency test method such as Figure 7 shown.

[0109] Among them, the wide-area bathymetric quality checkpoint atlas (such as Figure 8 The wide-area electromagnetic sounding instrument consistency atlas (as shown) may include: wide-area electromagnetic sounding quality checkpoint error statistics calculation table, wide-area electromagnetic sounding quality checkpoint data atlas error statistics summary table and wide-area electromagnetic sounding quality checkpoint data atlas report file. Figure 9 As shown), it may include: wide-area instrument indoor consistency curve comparison chart, instrument outdoor consistency data results table, instrument outdoor consistency each channel mean square relative error table.

[0110] For example, data on wide-area sounding points requiring quality inspection are collected. This data may include measured values, predicted values, checkpoint coordinates, etc.; based on the characteristics of wide-area sounding, an appropriate quality inspection formula is selected to calculate the errors of the checkpoints; the quality inspection formula is applied to each checkpoint to calculate its error value; and based on the calculated error data, a wide-area electromagnetic sounding quality checkpoint error statistical calculation table is automatically generated. This table may contain the following columns: checkpoint ID: uniquely identifies each checkpoint; measured value: the actual measured value of the checkpoint; predicted value: the value predicted based on the wide-area sounding model; error: the error value calculated based on the quality inspection formula. Based on the checkpoint error data, a wide-area electromagnetic sounding quality checkpoint data atlas is automatically generated. The atlas may include the following: error distribution map: showing the error distribution of the checkpoints; error histogram: showing the frequency distribution of error values; data point location map: marking the location and error of the checkpoints on the map. Error data from all checkpoints is aggregated to generate a summary error statistics table for the wide-area electromagnetic sounding quality checkpoint data atlas. This summary table may include overall error metrics such as average error, maximum error, and minimum error, as well as error classification statistics such as the number of checkpoints with errors within a specific range. Based on the quality check results, a wide-area electromagnetic sounding quality checkpoint data atlas report file is generated. The above steps complete the calculation and statistical analysis of the wide-area electromagnetic sounding quality checkpoints, generating statistical tables, data atlases, and report files that facilitate user analysis and decision-making. These documents are crucial for assessing the quality and reliability of wide-area electromagnetic sounding data.

[0111] For example, the original indoor and outdoor consistency test data files are imported. These files are usually in TXT format and contain the measured values ​​during the test process. These files are parsed to extract key data fields, such as test time, test value, test conditions, etc. The imported data is cleaned to remove invalid, erroneous or duplicate data records to ensure consistent data format for subsequent calculations and integration. As needed, the data is converted into a format suitable for analysis, for example, the measured values ​​are converted into corresponding physical quantities (such as resistivity). Consistency calculations are performed on the indoor and outdoor test data, which may include calculating statistics such as mean, standard deviation, and relative error. Multiple test records are automatically batch processed to generate consistency statistical results. Based on the indoor test data, curve comparison charts are generated. These charts usually show the changes in indoor test values ​​over time or test conditions, as well as the comparison of curves under different test conditions. Use a chart library (such as matplotlib, ggplot, etc.) to draw curves and annotate key information. Generate a table to show the results of the outdoor test data. The table may contain columns such as test point ID, test time, test value, relative error, etc. The mean squared relative error (RMSE) for each test channel (which may refer to different test frequencies or measurement parameters) is calculated and a corresponding table is generated. The table may include columns such as the test channel ID and the mean squared relative error. The generated charts and tables are integrated into an atlas to form a complete performance test report. Through the above steps, the consistency test of the wide-area electromagnetic sounding instrument performance is completed, and an intuitive atlas is generated for users to analyze and evaluate the instrument performance. These atlases and tables provide an effective way to judge and compare the instrument's performance under indoor and outdoor conditions.

[0112] In one embodiment, a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram are drawn based on the processed detection data and the GPS measurement data results atlas, and a wide-area electromagnetic inversion file is generated, including: extracting field source coordinates, electric field data, and current data according to the processed detection data; importing the GPS measurement data results atlas; drawing a wide-area electromagnetic curve diagram based on a comprehensive data file formed by the GPS measurement data results atlas, field source coordinates, electric field data, and current data; wherein the wide-area electromagnetic curve diagram includes: a comparison diagram of wide-area electromagnetic sounding artificial field and natural field curves and a wide-area electromagnetic sounding apparent resistivity and electric field curve diagram; drawing a wide-area electromagnetic pseudo-section diagram based on the processed detection data; and generating a wide-area electromagnetic inversion file based on the comprehensive data file formed by the GPS measurement data results atlas, field source coordinates, electric field data, and current data.

[0113] In one embodiment, a wide-area electromagnetic pseudo-section diagram is drawn based on the processed detection data, including: extracting raw data generated by the pseudo-section diagram based on the processed detection data; performing data analysis and standard calculations on the raw data generated by the pseudo-section diagram to generate an apparent resistivity pseudo-section diagram and a normalized electric field pseudo-section diagram; and using the apparent resistivity pseudo-section diagram and the normalized electric field pseudo-section diagram as a wide-area electromagnetic pseudo-section diagram.

[0114] Specifically, the wide-area electromagnetic curve diagram is drawn by using the comprehensive data file formed by GPS measurement results table (GPS measurement data results atlas), field source coordinates, electric field, current, original electric field and natural electric field data to draw the wide-area electromagnetic curve diagram (such as Figure 10 (as shown), for example, a comparison of wide-area electromagnetic sounding artificial and natural field curves and a wide-area electromagnetic sounding apparent resistivity and electric field curve.

[0115] Exemplarily, wide-area electromagnetic curve graphing: import GPS measurement results table, field source coordinates, electric field, current, original electric field and natural electric field data, parse these data files, and extract key information such as measurement time, coordinates, electric field strength, current strength, etc. Calculate apparent resistivity based on electric field and current data. Apparent resistivity is an important parameter in electromagnetic sounding. Calculate the difference between artificial fields (generated by field sources) and natural fields (naturally existing electromagnetic fields). This may involve comparing the electric field strengths of the two fields. Use the graphics library to draw a curve comparison chart of artificial fields and natural fields. The chart may contain time or test serial number as the horizontal axis and electric field strength or apparent resistivity as the vertical axis. Based on the apparent resistivity calculation results and electric field data, draw a curve chart of the relationship between apparent resistivity and electric field. This curve chart helps to analyze the impact of electric field changes on apparent resistivity.

[0116] Specifically, wide-area electromagnetic pseudo-section drawing: This function supports the generation of apparent resistivity pseudo-sections and normalized electric field pseudo-sections (pseudo-sections such as Figure 11 Import the original data containing the pseudo-section diagram, analyze the data and perform standard calculations on the information required to generate the diagram, so that users can flexibly select the corresponding pseudo-section diagram of the survey line. The drawing process is as follows: Figure 12 Reference Figure 12The drawing process of wide-area electromagnetic pseudo-section diagram is as follows: input source file; identify point number and line number (how many survey lines there are and how many points each survey line has); select the data to be drawn for pseudo-section according to line number (for example, if there are line 1 and line 2, the user can select all the survey point data of line 1 at once, and can make specific selections for the survey point data in line 1); input (fill in the standard point distance), calculate the MN polar distance of each survey point according to the M and N coordinates of each survey point, input (the starting point and end point coordinates of the survey line section need to be entered, the starting point coordinate is generally the M polar coordinate of the smallest survey point in a survey line, and the end point coordinate is generally the N polar coordinate of the largest survey point in a survey line, kilometer network The coordinates are in the same form as the coordinates of M and N); project the measuring point onto the cross-section of the survey line (equivalent to determining the horizontal coordinate range of the cross-section, with the starting point of the horizontal coordinate being 0 and the length of the horizontal axis being the length of the survey line) and the position of the measuring point on the cross-section, with the horizontal axis position of the first measuring point being the starting point minus half the standard point distance; draw a pseudo-section diagram of apparent resistivity and plot (data used: the vertical axis is the logarithm of the frequency, the horizontal axis is the distance, and the value is the apparent resistivity); draw a pseudo-section diagram of the normalized electric field, input a standard current file for all frequency points of the measuring point, calculate the normalized electric field, and plot (data used: the vertical axis is the logarithm of the frequency, the horizontal axis is the distance, and the value is the normalized electric field value).

[0117] Exemplarily, the drawing of a wide-area electromagnetic pseudo-section: the raw data containing the pseudo-section to be generated is imported, which may include measurement time, measurement point coordinates, and measurement values ​​(such as electric field, current, apparent resistivity, etc.), and the imported raw data is parsed to extract the required information such as measurement point coordinates, measurement values, etc. If the raw data does not contain apparent resistivity, it is necessary to calculate the apparent resistivity based on the electric field and current data. If a normalized electric field pseudo-section needs to be drawn, the electric field data needs to be normalized. A user interface is provided to allow the user to select the survey line for the pseudo-section to be drawn. The user can select a single survey line or multiple survey lines as needed. Based on the survey line selected by the user and the pre-processed data, an apparent resistivity pseudo-section and a normalized electric field pseudo-section are generated. The pseudo-section is usually displayed in two dimensions, with the horizontal axis representing the survey line distance, the vertical axis representing the depth, and the color or height representing the apparent resistivity or normalized electric field value. In addition, the provided tools allow you to adjust the display parameters of the pseudo-section, such as the color bar range, line type, markers, and legend. You can also adjust the pseudo-section's scale, title, and axis labels. The above steps complete the creation of a wide-area electromagnetic pseudo-section, providing an intuitive way to analyze and display wide-area electromagnetic sounding data. Pseudo-sections help users understand the electrical distribution of underground structures, which is of great significance for geological exploration and resource evaluation.

[0118] Specifically, the wide-area electromagnetic inversion file: an inversion file is generated by a comprehensive data file formed by integrating GPS measurement results table (GPS measurement data results atlas), field source coordinates, electric field, current, original electric field and natural electric field data.

[0119] Exemplarily, wide-area electromagnetic inversion file generation: collect GPS measurement results table, field source coordinates, electric field, current, original electric field and natural electric field data. Import these data into wide-area electromagnetic inversion software. Merge these data into a comprehensive data file. This process may involve data format conversion, coordinate system unification, etc. Select a suitable inversion algorithm. Common algorithms include least squares method, Monte Carlo method, genetic algorithm, etc. Set the parameters of the inversion algorithm, such as the initial model, the search range of the inversion parameters, the convergence conditions, etc. Use the inversion algorithm to calculate the comprehensive data file. It involves a large number of iterative calculations to optimize the model parameters to minimize the difference between the model prediction and the actual measurement data. Output the inversion results, which may include apparent resistivity distribution, electrical structure model, etc. Package the inversion results into an inversion file, which contains the key information and final results of the inversion process. The inversion file can be used for subsequent data analysis and interpretation. Through the above steps, the generation of wide-area electromagnetic inversion file is completed, providing a method for extracting underground electrical structure information from measurement data. These inversion files have important application value in geological exploration, mineral resource assessment and geological disaster prediction.

[0120] In one embodiment, the method further includes: when drawing the wide-area electromagnetic curve diagram and the wide-area electromagnetic pseudo-section diagram, calling the jar package provided in the Matlab tool, and parsing and calculating the apparent resistivity data based on the jar package; calling the vector data to raster interface in the Qgis platform, and performing format conversion of the pseudo-section diagram results based on the vector data to raster interface.

[0121] Specifically, refer to Figure 3 The external program services called by the implementation of the method described in this embodiment are shown in Table 1 below:

[0122] Table 1 External call program service table

[0123] Serial number name 1 Matlab 2 Qgis

[0124] During the implementation process, the method described in this embodiment calls the jar package provided in the Matlab tool to parse and calculate the apparent resistivity data; and calls the vector data to raster interface in the Qgis platform to convert the format of the pseudo-section map results.

[0125] For example, to use a MATLAB tool to call a JAR package to parse and calculate apparent resistivity data: Ensure that a MATLAB JAR package is available for parsing and calculating apparent resistivity data. This JAR package may be a third-party library or a custom Java program. In MATLAB, set up the Java environment to ensure that MATLAB can call Java programs, typically using the javaaddpath function. Use the javaObject function to create an instance of the class in the JAR package, and then call the corresponding function to parse and calculate the apparent resistivity data.

[0126] For example, the Qgis platform calls the vector data to raster interface: Make sure there is a vector data file containing a pseudo-section map, which is usually stored in Shapefile format. Start the Qgis software and load the vector data file. In Qgis, select the "Rasterize" tool, or use the "Vector to Raster" tool as needed. Set the conversion parameters, which can include the input vector layer, rasterization field, output raster data type, resolution, and output file path. After the rasterization is completed, save the generated raster data file.

[0127] It is understandable that geophysical exploration data comes from a wide range of sources, has diverse formats, and is a large amount of data. If traditional technologies are continued to be used to manage geophysical exploration data, special management of data from different sources will be required. This will not only result in a large waste of manpower and financial resources, but will also lead to difficulties in interacting with, updating, and searching geophysical exploration data. The method described in this embodiment automates the collection of geophysical exploration data collected by wide-area electromagnetic methods, saving manpower and financial resources consumed in data management, updating, and searching, reducing the cost of geophysical exploration and acquisition, providing a convenient and effective way for geophysical exploration application workers and researchers, and improving the application conversion rate of collected data results.

[0128] This embodiment provides a method for automatically processing geophysical data based on wide-field electromagnetic methods. This method automatically generates GPS measurement data results atlases, GPS quality inspection statistics, wide-field electromagnetic measurement raw data results, various geophysical exploration results atlases, wide-field electromagnetic curves and wide-field electromagnetic pseudo-sections, and inversion files, thereby realizing the automated processing of geophysical exploration raw data from collection to output. Automated collection of geophysical exploration data collected by wide-field electromagnetic methods saves manpower and financial resources consumed in data management, updating, and searching, and reduces the cost of geophysical exploration collection. It provides a convenient and effective working method for the majority of geophysical exploration application workers and researchers, and improves the application conversion rate of collected data results.

[0129] In addition, an embodiment of the present invention also proposes a storage medium, on which is stored a program for automatically processing geophysical data based on wide-area electromagnetic methods. When the program for automatically processing geophysical data based on wide-area electromagnetic methods is executed by a processor, the steps of the method for automatically processing geophysical data based on wide-area electromagnetic methods as described above are implemented.

[0130] Reference Figure 13 , Figure 13 This is a structural block diagram of an embodiment of the geophysical exploration data automatic processing system based on wide-area electromagnetic method of the present invention.

[0131] like Figure 13 As shown, the automatic processing system for geophysical exploration data based on wide-area electromagnetic method includes:

[0132] A preprocessing module 10 is used to obtain raw wide-area electromagnetic detection data and perform data preprocessing on the raw wide-area electromagnetic detection data to obtain processed detection data;

[0133] The data processing module 20 is used to extract valid data from the processed detection data, and automatically generate a GPS measurement data result atlas based on the valid data based on the spatial interpolation method;

[0134] A quality inspection module 30 is configured to perform data quality inspection and statistics based on the processed detection data and the GPS measurement data result atlas, and obtain a GPS quality inspection statistical report and a quality inspection statistical table;

[0135] An atlas generating module 40 is configured to generate atlases of wide-area electromagnetic survey raw data and various geophysical exploration results based on the processed detection data;

[0136] The drawing module 50 is used to draw a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and the GPS measurement data result atlas, and generate a wide-area electromagnetic inversion file.

[0137] In one embodiment, the system calls the jar package provided in the Matlab tool to parse and calculate the apparent resistivity data; and calls the vector data to raster interface in the Qgis platform to convert the format of the pseudo-section map results.

[0138] This embodiment provides an automated processing system for geophysical exploration data based on wide-area electromagnetic methods. This system automatically generates GPS measurement data results atlases, GPS quality inspection statistics, wide-area electromagnetic measurement raw data results, various geophysical exploration results atlases, wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections, and inversion files, thereby realizing the automated processing of geophysical exploration raw data from collection to output. Automated collection of geophysical exploration data collected by wide-area electromagnetic methods saves manpower and financial resources consumed in data management, updating, and searching, and reduces the cost of geophysical exploration collection. This system provides a convenient and effective working method for the vast number of geophysical exploration application workers and researchers, and improves the application conversion rate of collected data results.

[0139] It should be noted that the technical details that are not fully described in the embodiment of the automatic processing system for geophysical exploration data based on wide-area electromagnetic method can be referred to the automatic processing method for geophysical exploration data based on wide-area electromagnetic method as described above provided in any embodiment of the present invention, and will not be repeated here.

[0140] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0141] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0142] In addition, it should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0143] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0144] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for automatic processing of geophysical exploration data based on wide-area electromagnetic method, characterized in that: include: Acquiring raw wide-area electromagnetic detection data, and performing data preprocessing on the raw wide-area electromagnetic detection data to obtain processed detection data; Extracting valid data according to the processed detection data, and automatically generating a GPS measurement data result atlas according to the valid data based on a spatial interpolation method; Performing data quality inspection and statistics based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table; generating wide-area electromagnetic survey raw data results and various geophysical exploration results atlases based on the processed detection data; Based on the processed detection data and GPS measurement data result atlas, a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram are drawn, and a wide-area electromagnetic inversion file is generated.

2. The method according to claim 1, wherein The acquiring of raw wide-area electromagnetic detection data and preprocessing of the raw wide-area electromagnetic detection data to obtain processed detection data include: Obtain raw wide-area electromagnetic detection data; Processing missing values, duplicate values, and outliers on the original wide-area electromagnetic detection data to obtain cleaned data; The cleaned data is standardized, normalized and discretized to achieve data conversion and obtain processed detection data.

3. The method according to claim 1, wherein The extracting effective data according to the processed detection data, and automatically generating a GPS measurement data result atlas according to the effective data based on a spatial interpolation method, includes: extracting original GPS measurement data based on the processed detection data; Extracting data that meets preset requirements according to the representation fields of the original GPS measurement data; Based on the satellite signal strength and positioning precision index, outliers in the data that meet the preset requirements are eliminated to obtain valid data; A GPS measurement data results atlas is generated based on the valid data using a spatial interpolation method.

4. The method according to claim 1, wherein The data quality inspection statistics are performed based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table, including: Extracting GPS quality inspection raw data based on the processed detection data; Importing the GPS measurement data results atlas; The GPS quality inspection raw data and the GPS measurement data result atlas are subjected to data screening and preset standard operations to output a GPS quality inspection statistical report and a quality inspection statistical table.

5. The method according to claim 1, wherein The wide-area electromagnetic measurement raw data results and various geophysical exploration results atlases are generated based on the processed detection data, including: Extracting measurement point coordinates, field source coordinates, electric field files, current files, original electric field files, and natural electric field files based on the processed detection data; Calculating wide-area electromagnetic measurement data results based on the measurement point coordinates, field source coordinates, electric field files, current files, original electric field files, and natural electric field files based on an inversion algorithm; A wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas are generated based on the processed detection data.

6. The method according to claim 5, wherein Generating a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas based on the processed detection data includes: Extracting checkpoint data for the checked point based on the processed detection data; Calculating the checked points according to the checkpoint data in accordance with a preset quality inspection formula to automatically generate a wide-area bathymetric quality inspection point atlas; extracting an original indoor consistency file and an original outdoor consistency file according to the processed detection data; The original indoor consistency files and the original outdoor consistency files are automatically batch-generated into wide-area electromagnetic instrument consistency atlases through data integration and calculation.

7. The method according to claim 1, wherein Drawing a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and GPS measurement data result atlas, and generating a wide-area electromagnetic inversion file, includes: extracting field source coordinates, electric field data, and current data based on the processed detection data; Importing the GPS measurement data results atlas; A wide-area electromagnetic curve diagram is drawn based on a comprehensive data file formed by the GPS measurement data atlas, field source coordinates, electric field data, and current data; wherein the wide-area electromagnetic curve diagram includes: a wide-area electromagnetic depth measurement artificial field and natural field curve comparison diagram and a wide-area electromagnetic depth measurement apparent resistivity and electric field curve diagram; drawing a wide-area electromagnetic pseudo-section diagram based on the processed detection data; A wide-area electromagnetic inversion file is generated based on a comprehensive data file formed by the GPS measurement data results atlas, field source coordinates, electric field data, and current data.

8. The method according to claim 7, wherein Drawing a wide-area electromagnetic pseudo-section diagram based on the processed detection data includes: extracting raw data generated by the pseudo-section diagram based on the processed detection data; Performing data analysis and standard calculation on the raw data generated by the pseudo-section diagram to generate an apparent resistivity pseudo-section diagram and a normalized electric field pseudo-section diagram; The apparent resistivity pseudo-section diagram and the normalized electric field pseudo-section diagram are used as wide-area electromagnetic pseudo-section diagrams.

9. The method according to claim 8, wherein The method further comprises: When drawing wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections, the jar package provided in the Matlab tool is called to analyze and calculate the apparent resistivity data based on the jar package; The vector data to raster interface in the Qgis platform is called, and the format conversion of the pseudo-section map result is performed based on the vector data to raster interface.

10. An automatic processing system for geophysical exploration data based on wide-area electromagnetic method, characterized in that: include: A preprocessing module is used to obtain raw detection data of the wide-area electromagnetic method, perform data preprocessing on the raw detection data of the wide-area electromagnetic method, and obtain processed detection data; A data processing module is used to extract valid data based on the processed detection data, and automatically generate a GPS measurement data result atlas based on the valid data based on a spatial interpolation method; A quality inspection module is used to perform data quality inspection statistics based on the processed detection data and GPS measurement data result atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table; An atlas generation module is used to generate wide-area electromagnetic measurement raw data results and various geophysical exploration results atlases based on the processed detection data; The drawing module is used to draw a wide-area electromagnetic curve diagram and a wide-area electromagnetic pseudo-section diagram based on the processed detection data and the GPS measurement data result atlas, and generate a wide-area electromagnetic inversion file.

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