Wide-range electromagnetic method-based geophysical data automatic processing method and system

Abstract

The present application relates to the technical field of geophysical exploration application, and discloses a kind of geophysical data automatic processing method and system based on wide area electromagnetic method.The method comprises obtaining wide area electromagnetic method original detection data and pre-processing, obtaining processed detection data;According to the processed detection data, effective data is extracted, and based on spatial interpolation method, GPS survey data achievement atlas is automatically generated according to effective data;Based on the processed detection data and GPS survey data achievement atlas, data quality inspection statistics are carried out, and GPS quality inspection statistical report and quality inspection statistical table are obtained;According to the processed detection data, wide area electromagnetic survey original data achievement and various geophysical exploration achievement atlas are generated;Based on the processed detection data and GPS survey data achievement atlas, wide area electromagnetic curve graph and wide area electromagnetic pseudo-section graph are drawn, and wide area electromagnetic inversion file is generated.The present application realizes the automatic processing of geophysical exploration original data from acquisition to achievement output.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration application technology, and in particular to an automated method and system for processing geophysical data based on the wide-area electromagnetic method. Background Technology

[0002] Currently, geophysical exploration professionals using wide-area electromagnetic surveys face challenges due to inconsistencies in instrument models and data output formats. This makes the analysis of raw data extremely cumbersome and results in low utilization rates. Geophysical exploration data comes from a wide range of sources, comes in diverse formats, and is massive in volume. Continuing to manage geophysical exploration data using existing technologies would require specialized management for data from different sources. This not only leads to significant waste of human and financial resources but also causes difficulties in data exchange, updates, and retrieval. Summary of the Invention

[0003] The main objective of this invention is to provide an automated method and system for processing geophysical data based on the wide-area electromagnetic method, aiming to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, this invention provides an automated processing method for geophysical data based on the wide-area electromagnetic method, comprising:

[0005] The raw detection data of the wide-area electromagnetic method is acquired, and the raw detection data of the wide-area electromagnetic method is preprocessed to obtain the processed detection data.

[0006] Based on the processed detection data, valid data is extracted, and a GPS measurement data result atlas is automatically generated based on the valid data using spatial interpolation.

[0007] Based on the processed detection data and GPS measurement data results atlas, data quality inspection and statistics are performed to obtain a GPS quality inspection statistical report and a quality inspection statistical table.

[0008] Based on the processed detection data, generate wide-area electromagnetic measurement raw data results and various geophysical exploration result atlases;

[0009] Based on the processed detection data and GPS measurement data, 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, acquiring raw wide-area electromagnetic method detection data and performing data preprocessing on the raw wide-area electromagnetic method detection data to obtain processed detection data includes:

[0011] Acquire raw detection data using the wide-area electromagnetic method;

[0012] The original detection data of the wide-area electromagnetic method is processed to remove missing values, duplicate values ​​and outliers, resulting in cleaned data.

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

[0014] In some embodiments, the step of extracting effective data from the processed detection data and automatically generating a GPS measurement data atlas based on the effective data using spatial interpolation includes:

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

[0016] Extract data that meets preset requirements based on the representation fields of the original GPS measurement data;

[0017] By eliminating outliers from data that meet preset requirements based on satellite signal strength and positioning accuracy factors, valid data is obtained.

[0018] Based on the valid data, a GPS measurement data result atlas is generated using spatial interpolation.

[0019] In some embodiments, the data quality inspection and 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:

[0020] Extract the original GPS quality inspection data based on the processed detection data;

[0021] Import the GPS measurement data results atlas;

[0022] The raw GPS quality inspection data and the GPS measurement data result map are subjected to data filtering and preset standard calculations to output GPS quality inspection statistical reports and quality inspection statistical tables.

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

[0024] Based on the processed detection data, extract the coordinates of the measuring point, the coordinates of the field source, the electric field file, the current file, the original electric field file, and the natural electric field file;

[0025] The wide-area electromagnetic measurement data results are calculated based on the inversion algorithm according to the coordinates of the measurement point, the coordinates of the field source, the electric field file, the current file, the original electric field file, and the natural electric field file.

[0026] Based on the processed detection data, a wide-area electromagnetic sounding quality checkpoint map and a wide-area electromagnetic instrument consistency map are generated.

[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] Extract checkpoint data based on the processed detection data for the checkpoint;

[0029] The checkpoints are calculated according to the preset quality inspection formula based on the checkpoint data to automatically generate a wide-area depth sounding quality checkpoint map.

[0030] Extract the original indoor consistency file and the original outdoor consistency file based on the processed detection data;

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

[0032] In some embodiments, the process of plotting wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections based on the processed detection data and GPS measurement data, and generating a wide-area electromagnetic inversion file, includes:

[0033] Extract the source coordinates, electric field data, and current data from the processed detection data;

[0034] Import the GPS measurement data results atlas;

[0035] A wide-area electromagnetic curve diagram is drawn based on the 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 the artificial field and natural field curves of wide-area electromagnetic sounding and a wide-area electromagnetic sounding apparent resistivity and electric field curve diagram;

[0036] A wide-area electromagnetic pseudo-section diagram is drawn based on the processed detection data;

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

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

[0039] The original data for generating the pseudo-section diagram is extracted from the processed detection data;

[0040] Data parsing and standard calculations are performed on the original 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 includes:

[0043] When plotting wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections, the jar package provided in the Matlab tool is called, and the apparent resistivity data is parsed and calculated based on the jar package.

[0044] The vector data to raster interface in the QGIS platform is called to perform format conversion of the simulated cross-section map results based on the vector data to raster interface.

[0045] Furthermore, to achieve the above objectives, this invention also proposes an automated geophysical data processing system based on the wide-area electromagnetic method, comprising:

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

[0047] The data processing module is used to extract effective data from the processed detection data and automatically generate a GPS measurement data result atlas based on the effective data using spatial interpolation.

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

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

[0050] The plotting module is used to plot wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections based on the processed detection data and GPS measurement data, and to generate wide-area electromagnetic inversion files.

[0051] This invention provides an automated processing method for geophysical exploration data based on the wide-area electromagnetic method (WEM), comprising: acquiring raw WEM data; preprocessing the raw WEM data to obtain processed data; extracting valid data from the processed data and automatically generating a GPS measurement data atlas based on the valid data using spatial interpolation; performing data quality inspection and statistics on the processed data and the GPS measurement data atlas to obtain a GPS quality inspection statistical report and a quality inspection statistical table; generating raw WEM data results and various geophysical exploration result atlases based on the processed data; plotting WEM curves and WEM pseudo-sections based on the processed data and the GPS measurement data atlases, and generating a WEM inversion file. This invention automatically generates GPS measurement data atlases, GPS quality inspection statistics, raw WEM data results, various geophysical exploration result atlases, WEM curves and WEM pseudo-sections, and inversion files, achieving automated processing of raw geophysical exploration data from acquisition to output. Automated acquisition of geophysical exploration data obtained through wide-area electromagnetic methods saves manpower and financial resources spent on data management, updating, and searching, thus reducing the cost of geophysical exploration acquisition. It provides a convenient and effective working method for geophysical exploration application workers and researchers, improving the conversion rate of acquired data into practical applications. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the structure of an electronic device in the hardware operating environment involved in the embodiments of the present invention;

[0053] Figure 2 This is a flowchart illustrating an embodiment of the automated geophysical data processing method based on the wide-area electromagnetic method of the present invention.

[0054] Figure 3 This is a technical roadmap related to the embodiments of the present invention;

[0055] Figure 4 This is a schematic diagram of the GPS quality inspection data statistical quality inspection method involved in the embodiments of the present invention;

[0056] Figure 5 This is a flowchart illustrating the process of calculating the original data of wide-area electromagnetic measurement using the inversion algorithm involved in the embodiments of the present invention.

[0057] Figure 6 This is a schematic diagram of the wide-area depth sounding quality checkpoint inspection method involved in the embodiments of the present invention;

[0058] Figure 7 This is a schematic diagram of the wide-area electromagnetic conformance testing method involved in the embodiments of the present invention;

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

[0060] Figure 9 This is a schematic diagram of the wide-area electromagnetic conformance test manual involved in the embodiments of the present invention;

[0061] Figure 10 This is a schematic diagram of the wide-area electromagnetic curve involved in the embodiments of the present invention;

[0062] Figure 11 This is a schematic diagram of a simulated cross-section of the embodiment of the present invention;

[0063] Figure 12 This is a schematic diagram of the process for drawing wide-area electromagnetic pseudo-section diagrams involved in the embodiments of the present invention;

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

[0065] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0067] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0068] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the 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 embodiments 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. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also 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 does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

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

[0073] exist Figure 1In the illustrated electronic device, 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 installed in the electronic device, and the electronic device calls the automated geophysical data processing program based on the wide-area electromagnetic method stored in the memory 1005 through the processor 1001, and executes the automated geophysical data processing method based on the wide-area electromagnetic method provided in the embodiment of the present invention.

[0074] This invention proposes an automated processing method and system for geophysical data based on the wide-area electromagnetic method.

[0075] This invention provides an automated processing method for geophysical data based on the wide-area electromagnetic method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the automated geophysical data processing method based on the wide-area electromagnetic method of the present invention.

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

[0077] Step S100: Obtain the raw detection data of the wide-area electromagnetic method, and perform data preprocessing on the raw detection data of the wide-area electromagnetic method to obtain the processed detection data;

[0078] Step S200: Extract valid data from the processed detection data, and automatically generate a GPS measurement data result atlas based on the valid data using spatial interpolation.

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

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

[0081] Step S500: Based on the processed detection data and GPS measurement data, draw a wide-area electromagnetic curve and a wide-area electromagnetic pseudo-section diagram, and generate a wide-area electromagnetic inversion file.

[0082] It should be noted that the execution subject in this embodiment can be an electronic device, which can be a computer device with data processing function, 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 explanation.

[0083] Understandably, geophysical exploration professionals currently employ inconsistencies in instrument models and data output formats when conducting wide-area electromagnetic surveys. This makes the analysis of raw data extremely cumbersome and results in low utilization rates. Against this backdrop, the automated geophysical data processing method based on wide-area electromagnetic methods proposed in this embodiment is the first to achieve a fully integrated transformation of the entire process—from analysis and calculation to mapping of raw geophysical field data. This not only improves the efficiency of professional work but also enhances the application and conversion rate of the collected data.

[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 Magnetotelluric (CSAMT) and the MELOS method, have limitations when probing subsurface structures. For example, CSAMT suffers from weak signals in distant areas, while MELOS requires complex calibration processes. WFEM combines the advantages of both methods and avoids their limitations. Key features of WFEM include: Use of artificial field sources: Unlike natural-source electromagnetic methods, WFEM uses artificially generated electromagnetic fields, which helps overcome the randomness of natural field sources and improves data acquisition accuracy. Expanded observation range: It not only inherits the advantages of CSAMT, such as using artificial field sources to overcome field source randomness, but also overcomes the disadvantage of weak signals in distant areas, thus expanding the observation range. Global applicability: WFEM does not rely on the Carnia formula but uses a formula suitable for the entire region to calculate apparent resistivity, thereby improving the accuracy and efficiency of observations. Improved exploration depth and accuracy: Compared with other electromagnetic methods, the wide-area electromagnetic method can achieve greater exploration depth and significantly improve measurement accuracy under the same transmitter-receiver distance conditions. Breakthrough of traditional theoretical limitations: The wide-area electromagnetic method breaks through the theoretical limitations of traditional electromagnetic methods that approximate the division of electromagnetic waves into "near-field," "transition," and "far-field" regions. It adopts precise electromagnetic field expressions, improving the accuracy of data processing and interpretation.

[0085] In this embodiment, the automated processing of raw geophysical exploration data from acquisition to output is achieved based on the wide-area electromagnetic method as the main process. Figure 3 As shown in this embodiment, the method automatically generates various geophysical exploration result atlases, including GPS measurement data atlases, GPS measurement point data quality inspection statistics, wide-area electromagnetic measurement 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 files, and pseudo-section maps. The following detailed explanations illustrate the specific steps.

[0086] In one embodiment, acquiring raw wide-area electromagnetic (WEMI) detection data and performing data preprocessing on the raw WEMI detection data to obtain processed detection data includes: acquiring raw WEMI detection data; processing the raw WEMI detection data for missing values, duplicate values, and outliers to obtain cleaned data; and performing standardization, normalization, and discretization on the cleaned data to achieve data transformation and obtain processed detection data.

[0087] Specifically, data input involves users entering raw wide-area electromagnetic (WME) detection data (such as GPS measurement data output maps, GPS quality inspection data statistics, WME raw measurement data, WME inversion files, etc.) and related report content into the computer device. WME raw detection data can be obtained through file uploads, database imports, and other methods.

[0088] For example, raw data from the wide-area electromagnetic method includes GPS measurement data atlases, GPS quality control data statistics, raw wide-area electromagnetic measurement data, and wide-area electromagnetic inversion files. Related reports may include text descriptions, charts, maps, etc. Users upload data files through interfaces provided by their computer devices.

[0089] Specifically, data preprocessing: Preprocessing the input data is a crucial initial step in the data analysis and mining process, which can improve data quality and make subsequent analysis results more accurate and reliable. In this embodiment, data cleaning can be done by handling missing values, duplicate values, outliers, etc., and data transformation can be done by standardization, normalization, discretization, etc., and this embodiment does not impose any restrictions on these methods.

[0090] For example, data cleaning. Handling missing values: For missing data points, the following methods can be used: directly delete records containing missing values, or fill in the missing values ​​using the mean, median, mode, or model-based prediction methods. Handling duplicate values: Delete identical records to ensure that each data point is analyzed only once. Handling outliers: Identify and remove outliers using statistical methods (e.g., standard deviation, interquartile range) or rule-based methods.

[0091] For example, data transformation includes: Standardization: Converting data into standardized data with zero mean and unit variance to facilitate effective comparison of data of different magnitudes. Normalization: Scaling data to a fixed range (e.g., 0 to 1) to eliminate the influence of different units. Discretization: Converting continuous variables into categorical variables; for example, dividing continuous resistivity values ​​into different levels for subsequent analysis and processing. If the data comes from different files or systems, they need to be integrated for unified analysis. After preprocessing, the data is validated again to ensure that the processed data meets the analytical 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, extracting valid data from the processed detection data and automatically generating a GPS measurement data result atlas based on the valid data using spatial interpolation includes: extracting raw GPS measurement data from the processed detection data; extracting data that meets preset requirements based on the representation fields of the raw GPS measurement data; removing outliers from the data that meets preset requirements based on satellite signal strength and positioning accuracy factor indicators to obtain valid data; and generating a GPS measurement data result atlas using spatial interpolation based on the valid data.

[0093] Specifically, the GPS measurement data output atlas is generated by automatically selecting valid data that meets the requirements from the raw GPS measurement data based on the represented fields. Outliers caused by signal obstruction, equipment malfunction, etc., are removed. Judgments can be made based on indicators such as satellite signal strength and positioning accuracy factor (PDOP). Based on the valid data, a corresponding output atlas (GPS measurement data output atlas) is generated using spatial interpolation methods.

[0094] For example, the raw GPS measurement data collected by the GPS receiver is extracted from the processed exploration 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 exploration data, removing obviously erroneous or invalid data points, such as receiver malfunctions or lost satellite signals, to obtain the raw GPS measurement data after data preprocessing.

[0095] For example, data validity assessment includes: Satellite signal strength assessment: Checking the satellite signal strength corresponding to each data point and discarding data points affected by weak signals or obstructions. Positioning accuracy factor (PDOP) assessment: Evaluating positioning accuracy using the PDOP value. If the PDOP value exceeds a preset threshold (e.g., 6 or 7), the positioning accuracy is considered insufficient, and the corresponding data point is discarded. Valid data extraction: Based on the above validity assessment criteria, valid data that meets the requirements is automatically selected. This valid data will be used for subsequent spatial interpolation and atlas generation. Further analysis of the valid data can be conducted to evaluate its quality, such as positioning error and data reliability, ensuring that the generated atlas has high accuracy. The validity criteria include, but are not limited to: Satellite signal quality: based on a signal-to-noise ratio (SNR) threshold, for example, data is considered valid only when the SNR is greater than a certain value (e.g., 20dB); Positioning accuracy: based on the PDOP value, generally the lower the PDOP value, the higher the positioning accuracy, and a PDOP upper limit (e.g., 5 or 6) is set as the judgment standard; Data integrity: The data must be complete, for example, it must contain all required fields and have no missing values; Timestamp consistency: Ensure that the timestamps of the data are within a reasonable time range, without obvious time jumps or missing values.

[0096] For example, spatial interpolation: Spatial interpolation methods (such as Kriging interpolation, spline interpolation, inverse distance weighted interpolation, etc.) are used to interpolate the effective data, generating continuous grid data. The interpolated data is then converted into graphical formats, such as contour maps, color block maps, etc. Furthermore, necessary elements such as legends, coordinate axes, titles, and scale bars can be added according to user needs to generate a complete output atlas.

[0097] In one embodiment, data quality inspection and statistics are performed based on the processed detection data and GPS measurement data result map to obtain a GPS quality inspection statistical report and a quality inspection statistical table. This includes: extracting GPS quality inspection raw data based on the processed detection data; importing the GPS measurement data result map; and performing data filtering and preset standard calculations on the GPS quality inspection raw data and the GPS measurement data result map to output the GPS quality inspection statistical report and the quality inspection statistical table.

[0098] Specifically, GPS quality inspection data statistics: This involves performing quality checks on raw GPS data, importing GPS measurement results tables and raw GPS quality inspection data, and outputting GPS quality inspection statistical reports and tables after data filtering and pre-defined standard calculations (preset standard calculations) on the two sets of raw data. The quality inspection methods are as follows: Figure 4 As shown.

[0099] For example, importing a GPS measurement results table (GPS measurement data results atlas): Import the data file containing GPS measurement results, parse the file, and extract necessary fields such as measurement point ID, measurement time, and measurement values ​​(e.g., coordinates, speed, etc.). Import the raw GPS quality inspection data, which typically includes information such as signal strength, PDOP value, and positioning status. Data filtering: Check data integrity, data format and type, and perform validity judgments according to preset quality standards. Calculate statistics: Perform statistical analysis on the filtered data, calculating statistics such as mean, standard deviation, maximum, and minimum values, comparing measured values ​​with quality inspection values, and calculating deviation and error. A quality score can be assigned to each record according to preset quality scoring standards. The quality score can be based on multiple factors, such as positioning accuracy, signal strength, and observation duration. Based on the calculated statistics and quality scores, generate a quality inspection statistics table, which may include measurement point ID, measurement time, measured value, quality inspection value, deviation, error, and quality score. Generate a report containing detailed quality inspection information, which may include: an overview of overall quality inspection results, such as pass rate and non-conformance records; detailed quality inspection data for each measurement point, including statistics and quality scores; graphical representation of the quality inspection results, such as histograms and scatter plots; and suggestions for future improvement based on the quality inspection results. Through these steps, the quality inspection process for the raw GPS data is completed, and quality inspection statistical tables and reports that facilitate user analysis and decision-making are output. These documents are crucial for ensuring the quality and reliability of GPS data.

[0100] In one embodiment, generating wide-area electromagnetic measurement raw data results and various geophysical exploration result atlases based on the processed detection data includes: extracting measurement point coordinates, field source coordinates, electric field files, current files, raw electric field files, and natural electric field files from 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, raw electric field files, and natural electric field files using an inversion algorithm; and 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 raw data results of wide-area electromagnetic measurement are obtained by inputting the coordinates of the measurement point, the coordinates of the field source, the electric field file, the current file, the raw electric field file, and the natural electric field file. The measurement data results (wide-area electromagnetic measurement data results) are then calculated using an inversion algorithm. The calculation process is as follows: Figure 5 As shown. Figure 5As shown, the coordinates of the corresponding point number in the electric field file are extracted from the measurement point coordinates (source file 1), the coordinates of field source A and B are extracted from the field source coordinates (source file 2), and column data are extracted from 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) respectively. The data extracted from the above source files are summarized and the dataset is output (target file 1). The dataset is then calculated by inversion and the comprehensive data file (target file 2) is output.

[0102] Specifically, the following are the key components of the inversion algorithm:

[0103]

[0104] It should be noted that the above code is written in MATLAB and is used to execute an 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 of a set of stations and organizes this data into a Dot data structure for subsequent inversion analysis. A Dot may be an array of structures, with each element containing all relevant data and observations for a given station. In one example, the above code can be described as follows: Start a loop that iterates through 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 a new station has been encountered; set the current station to `first`; add a new `ndot`, which may be used to record the index of the new station; reset the `fNum` variable, which may be used to record the number of different frequencies within the current station; create a new `Dot` data structure for the current station and store the following information: `Stati` is set as the identifier of the current station, A, B, M, and N store the location or vector data associated with the station, `MN_center` calculates the coordinates of the center of two locations (M and N), and `MN` calculates the distance between two locations (M and N); if the current station is the same as `first`, it means the same station is still being processed, so `fNum` is incremented to record the new frequency within that station; regardless of whether it is a new station, update the following information 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 raw data from wide-area electromagnetic measurements involves analyzing and calculating the measurement data to obtain the electrical structure of the subsurface medium. Measurement point coordinates: Provides the geographical coordinates of all measurement points, typically latitude, longitude, and elevation. Field source coordinates: Provides the location coordinates of artificial field sources. Electric field file: Contains electric field data recorded during the measurement. Current file: Contains current intensity data emitted by the field sources. Raw electric field file: Contains raw electric field data without any processing. Natural electric field file: Contains natural electric field data without interference from artificial field sources. Data preprocessing: Ensures all input data are in a consistent format for easy subsequent processing. Removes invalid, erroneous, or abnormal data points. Normalizes the electric field and current data for easier analysis. Based on a hypothetical model of the subsurface medium, a forward model is constructed to simulate the propagation of electromagnetic waves in the subsurface medium. The response corresponding to the initial model is calculated using a forward modeling algorithm, i.e., converting the model parameters into the expected electric and magnetic field distributions. A suitable inversion algorithm is selected, such as least squares, gradient descent, Newton's method, or genetic algorithm. Constructing the Objective Function: An objective function is constructed to measure the difference between observed data and model predictions. Typically, the objective function minimizes the sum of squared errors between the observed and predicted data. The model parameters are continuously adjusted through an iterative process to minimize the difference between the model's predictions and the observed data. The convergence of the inversion process is checked; iteration stops if the objective function falls below a preset threshold or the maximum number of iterations is reached. The inverted geoelectric model results are output, including resistivity distribution maps and electrical structure models. The inversion results are visualized, generating charts, 3D models, etc., to obtain wide-area electromagnetic measurement data results for analysis and interpretation.

[0106] In one embodiment, 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 checkpoints based on the processed detection data; calculating the checkpoints 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 batch generating a wide-area electromagnetic instrument consistency atlas by integrating and calculating the original indoor consistency files and original outdoor consistency files.

[0107] Specifically, the wide-area electromagnetic sounding quality checkpoint atlas: For each checkpoint, using checkpoint data and following the quality inspection formula, it automatically generates a wide-area electromagnetic sounding quality checkpoint error statistics calculation table, a wide-area electromagnetic sounding quality checkpoint data atlas error statistics summary table, and a wide-area electromagnetic sounding quality checkpoint data atlas report file. Specific inspection methods are as follows... Figure 6 As shown.

[0108] Specifically, the wide-area electromagnetic conformance test atlas function can automatically generate a batch of wide-area electromagnetic sounding instrument performance test atlases from the original indoor and outdoor conformance TXT files through data integration and calculation. This atlas includes: a comparison chart of indoor conformance curves for the wide-area instrument, a table of outdoor conformance data results, a table of mean square relative errors for each channel of the outdoor conformance, and conformance test methods such as... Figure 7 As shown.

[0109] Among them, the wide-area depth sounding quality checkpoint atlas (such as...) Figure 8 As shown, it may include: a statistical calculation table of errors at wide-area electromagnetic sounding quality checkpoints, a summary table of errors in the wide-area electromagnetic sounding quality checkpoint data atlas, and a report file of the wide-area electromagnetic sounding quality checkpoint data atlas. A wide-area electromagnetic instrument consistency atlas (such as...) Figure 9 As shown, it may include: a comparison chart of indoor consistency curves for wide-area instruments, a table of outdoor consistency data results for instruments, and a table of mean square relative errors for each channel of outdoor consistency for instruments.

[0110] For example, data from wide-area sounding points requiring quality inspection is collected. This data may include measured values, predicted values, and checkpoint coordinates. Based on the characteristics of wide-area sounding, an appropriate quality inspection formula is selected to calculate the error of the checkpoints. The quality inspection formula is applied to each checkpoint to calculate its error value. Based on the calculated error data, a statistical calculation table of errors for wide-area electromagnetic sounding quality inspection points is automatically generated. This table may contain the following: Checkpoint ID: uniquely identifies each checkpoint; Measured value: the actual measured value of the checkpoint; Predicted value: the value predicted according to the wide-area sounding model; Error: the error value calculated according to the quality inspection formula. Based on the error data of the checkpoints, a data atlas of wide-area electromagnetic sounding quality inspection points 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 a map. The error data from all checkpoints are compiled to generate a comprehensive error statistics table for the wide-area electromagnetic sounding quality checkpoint data atlas. This table may include the following: overall error indicators such as average error, maximum error, and minimum error; and error classification statistics such as the number of checkpoints with errors within a specific range. Based on the quality inspection results, a wide-area electromagnetic sounding quality checkpoint data atlas report file is generated. Through these steps, the calculation and statistical analysis of the wide-area sounding quality checkpoints are completed, generating statistical tables, data atlases, and report files that facilitate user analysis and decision-making. These documents are of great significance for evaluating the quality and reliability of wide-area sounding data.

[0111] For example, import the raw indoor and outdoor consistency test data files, typically in TXT format, containing measurements taken during the testing process. Parse these files to extract key data fields such as test time, test value, and test conditions. Clean the imported data, removing invalid, erroneous, or duplicate records to ensure consistent data format for easy subsequent calculations and integration. Convert the data to a format suitable for analysis as needed, such as converting measurements to corresponding physical quantities (e.g., resistivity). Perform consistency calculations on the indoor and outdoor test data, potentially including calculating statistics such as mean, standard deviation, and relative error. Automatically batch process multiple test records to generate consistency statistical results. Based on the indoor test data, generate curve comparison charts, typically showing changes in indoor test values ​​over time or under test conditions, and curve comparisons under different test conditions. Plot the curves using charting libraries (e.g., matplotlib, ggplot, etc.) and annotate key information. Generate a table displaying the results of the outdoor test data, which may include columns such as test point ID, test time, test value, and relative error. Calculate the mean square relative error for each test channel (which may refer to different test frequencies or measurement parameters) and generate a corresponding table. The table may include columns such as test channel ID and mean square relative error. Integrate the generated charts and tables into a single atlas to form a complete performance test report. Through the above steps, the consistency test of the wide-area electromagnetic depth sounder's performance is completed, and an intuitive atlas is generated for users to analyze and evaluate the instrument's 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 and a wide-area electromagnetic pseudo-section are plotted based on the processed detection data and GPS measurement data atlas, and a wide-area electromagnetic inversion file is generated. This includes: extracting source coordinates, electric field data, and current data from the processed detection data; importing the GPS measurement data atlas; plotting the wide-area electromagnetic curve based on the comprehensive data file formed by the GPS measurement data atlas, source coordinates, electric field data, and current data; wherein the wide-area electromagnetic curve includes: a comparison chart of the artificial and natural field curves of wide-area electromagnetic sounding and a curve of apparent resistivity and electric field of wide-area electromagnetic sounding; plotting the wide-area electromagnetic pseudo-section based on the processed detection data; and generating the wide-area electromagnetic inversion file based on the comprehensive data file formed by the GPS measurement data atlas, source coordinates, electric field data, and current data.

[0113] In one embodiment, drawing a wide-area electromagnetic pseudo-section map based on the processed detection data includes: extracting the original data for generating the pseudo-section map based on the processed detection data; performing data parsing and standard calculations on the original data for generating the pseudo-section map to generate an apparent resistivity pseudo-section map and a normalized electric field pseudo-section map; and using the apparent resistivity pseudo-section map and the normalized electric field pseudo-section map as the wide-area electromagnetic pseudo-section map.

[0114] Specifically, wide-area electromagnetic curve plotting involves creating a comprehensive data file using data such as GPS measurement results tables (GPS measurement data results atlas), source coordinates, electric field, current, original electric field, and natural electric field. Figure 10 (As shown), for example, a comparison chart of artificial and natural field curves in wide-area electromagnetic sounding and a chart of apparent resistivity and electric field curves in wide-area electromagnetic sounding.

[0115] For example, wide-area electromagnetic curve plotting involves importing data such as GPS measurement results tables, source coordinates, electric field, current, original electric field, and natural electric field. These data files are then parsed to extract key information such as measurement time, coordinates, electric field strength, and current intensity. Based on the electric field and current data, apparent resistivity is calculated; apparent resistivity is a crucial parameter in electromagnetic sounding. The difference between the artificial field (generated by the source) and the natural field (naturally existing electromagnetic field) is calculated, which may involve comparing the electric field strengths of the two fields. A graph comparing the artificial and natural fields is plotted using a graphics library. The graph may include time or test number on the horizontal axis and electric field strength or apparent resistivity on the vertical axis. Based on the calculated apparent resistivity and electric field data, a curve depicting the relationship between apparent resistivity and electric field is plotted. This curve helps analyze the impact of electric field changes on apparent resistivity.

[0116] Specifically, wide-area electromagnetic pseudo-section plot drawing: This function supports generating apparent resistivity pseudo-section plots and normalized electric field pseudo-section plots (pseudo-section plots such as...). Figure 11 (As shown). Import the raw data containing the simulated cross-section diagram. Through data parsing and standard calculations of the information required for map generation, the system enables users to flexibly select the corresponding simulated cross-section diagram. The drawing process is as follows: Figure 12 As shown. Reference Figure 12The process for drawing a wide-area electromagnetic pseudo-section diagram is as follows: Input the source file; identify the point and line numbers (how many survey lines there are, and how many points are on each survey line); select the data to be drawn for the pseudo-section according to the line number (for example, if there are lines 1 and 2, the user can select all the survey point data of line 1 at once, and can also specifically select the survey point data within line 1); input (fill in the standard point distance); calculate the MN polar distance of each survey point based on the M and N coordinates of each survey point; input (the starting and ending coordinates of the survey line profile need to be entered; the starting coordinates are generally the M polar coordinates of the smallest survey point in a survey line, and the ending coordinates are generally the N polar coordinates of the largest survey point in a survey line, kilometer network). (Coordinates are in the same form as M and N); Project the measuring points onto the survey line profile (equivalent to determining the range of the horizontal coordinate 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 points on the cross-section. The horizontal axis position of the first measuring point is the starting point minus half of the standard point distance; Draw the apparent resistivity pseudo-cross-section diagram and plot it (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 the normalized electric field pseudo-cross-section diagram, input a standard current file of the full frequency of a measuring point, calculate the normalized electric field, and plot it (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] For example, wide-area electromagnetic pseudo-section plotting involves importing the raw data containing the pseudo-section plot generation data. This data may include measurement time, measurement point coordinates, and measured values ​​(e.g., electric field, current, apparent resistivity, etc.). The imported raw data is parsed to extract the necessary information, such as measurement point coordinates and measured values. If the raw data does not contain apparent resistivity, it needs to be calculated based on the electric field and current data. If a normalized electric field pseudo-section plot needs to be plotted, the electric field data needs to be normalized. A user interface is provided, allowing users to select the measurement lines for the pseudo-section plot to be plotted. Users can select a single measurement line or multiple measurement lines as needed. Based on the user-selected measurement lines and preprocessed data, apparent resistivity pseudo-section plots and normalized electric field pseudo-section plots are generated. The pseudo-section plots are typically displayed in a two-dimensional format, with the horizontal axis representing the measurement line distance, the vertical axis representing the depth, and color or height representing the apparent resistivity or normalized electric field value. Furthermore, the display parameters of the simulated cross-section can be adjusted using the provided tools, such as the color bar range, line type, markers, and legend. The scale, title, and axis labels can also be adjusted. Through these steps, a wide-area electromagnetic simulated cross-section is created, providing an intuitive way to analyze and display wide-area electromagnetic sounding data. The simulated cross-section helps users understand the electrical distribution of underground structures and is of great significance for geological exploration and resource assessment.

[0118] Specifically, the wide-area electromagnetic inversion file is generated from a comprehensive data file that integrates GPS measurement results (GPS measurement data results atlas), source coordinates, electric field, current, original electric field, and natural electric field.

[0119] For example, the generation of a wide-area electromagnetic inversion file involves: collecting data such as GPS measurement results tables, source coordinates, electric field, current, original electric field, and natural electric field. This data is then imported into wide-area electromagnetic inversion software. The data is merged into a single comprehensive data file, a process that may involve data format conversion and coordinate system unification. A suitable inversion algorithm is selected; common algorithms include least squares, Monte Carlo methods, and genetic algorithms. The parameters of the inversion algorithm are set, such as the initial model, the search range of inversion parameters, and convergence conditions. The comprehensive data file is then calculated using the inversion algorithm. This involves numerous iterative calculations to optimize the model parameters and minimize the difference between model predictions and actual measurement data. The inversion results are output, which may include apparent resistivity distribution and electrical structure models. The inversion results are packaged into an inversion file containing key information from the inversion process and the final results. This inversion file can be used for subsequent data analysis and interpretation. Through these steps, the generation of a wide-area electromagnetic inversion file is completed, providing a method for extracting subsurface electrical structure information from measurement data. These inversion files have significant application value in fields such as geological exploration, mineral resource assessment, and geological hazard prediction.

[0120] In one embodiment, the method further includes: when drawing wide-area electromagnetic curves and wide-area electromagnetic pseudo-section diagrams, calling a jar package provided in the Matlab tool to parse and calculate apparent resistivity data based on the jar package; and calling the vector data to raster interface in the Qgis platform to perform 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 method described in this embodiment are shown in Table 1 below:

[0122] Table 1 External Calling Program Service Table

[0123] Serial Number name 1 Matlab 2 Qgis

[0124] The method described in this embodiment calls the jar package provided by 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 simulated cross-section diagram results.

[0125] For example, MATLAB tools call a JAR file to parse and calculate apparent resistivity data: Ensure that a MATLAB JAR file for parsing and calculating apparent resistivity data is available. This JAR file may be a third-party library or a custom-written Java program packaged together. 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 file, 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: Ensure you have a vector data file containing a simulated cross-section, typically stored in Shapefile format. Launch 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 may include the input vector layer, rasterization fields, output raster data type, resolution, and output file path. After rasterization, save the generated raster data file.

[0127] It is understandable that geophysical exploration data comes from a wide range of sources, comes in diverse formats, and is massive in volume. If traditional technologies are continued to be used to manage geophysical exploration data, separate management is required for data from different sources. This not only wastes significant human and financial resources but also leads to difficulties in data exchange, updates, and retrieval. The method described in this embodiment, which automates the acquisition of geophysical exploration data using the wide-area electromagnetic method, will save on the human and financial resources consumed in data management, updates, and retrieval, reduce the cost of geophysical exploration acquisition, provide a convenient and effective approach for geophysical exploration application workers and researchers, and improve the conversion rate of acquired data into practical applications.

[0128] This embodiment provides an automated processing method for geophysical data based on the wide-area electromagnetic method. This method automatically generates GPS measurement data atlases, GPS quality control statistics, raw wide-area electromagnetic measurement data, various geophysical exploration result atlases, wide-area electromagnetic curves and pseudo-section maps, and inversion files, achieving automated processing of raw geophysical exploration data from acquisition to output. Automated acquisition of geophysical exploration data using the wide-area electromagnetic method saves manpower and financial resources spent on data management, updating, and searching, reducing the cost of geophysical exploration acquisition. It provides a convenient and effective working method for geophysical exploration application workers and researchers, improving the conversion rate of acquired data into practical applications.

[0129] Furthermore, this embodiment of the invention also proposes a storage medium storing an automated geophysical data processing program based on the wide-area electromagnetic method. When the automated geophysical data processing program based on the wide-area electromagnetic method is executed by a processor, it implements the steps of the automated geophysical data processing method based on the wide-area electromagnetic method described above.

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

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

[0132] Preprocessing module 10 is used to acquire raw detection data of wide-area electromagnetic method, and to preprocess the raw detection data of wide-area electromagnetic method to obtain processed detection data.

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

[0134] The quality inspection module 30 is used to perform data quality inspection and statistics based on the processed detection data and GPS measurement data result map, and to obtain a GPS quality inspection statistical report and a quality inspection statistical table.

[0135] The atlas generation module 40 is used to generate atlases of wide-area electromagnetic measurement raw data results and various geophysical exploration results based on the processed detection data.

[0136] The plotting module 50 is used to plot wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections based on the processed detection data and GPS measurement data results atlas, and to generate wide-area electromagnetic inversion files.

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

[0138] This embodiment provides an automated geophysical data processing system based on the wide-area electromagnetic method. This system automatically generates GPS measurement data atlases, GPS quality control statistics, raw wide-area electromagnetic measurement data, various geophysical exploration result atlases, wide-area electromagnetic curves and pseudo-section maps, and inversion files, achieving automated processing of raw geophysical exploration data from acquisition to output. Automated acquisition of geophysical exploration data using the wide-area electromagnetic method saves manpower and financial resources spent on data management, updating, and searching, reducing the cost of geophysical exploration acquisition. It provides a convenient and effective working method for geophysical exploration application workers and researchers, improving the conversion rate of acquired data into practical applications.

[0139] It should be noted that technical details not described in detail in this embodiment of the automated geophysical data processing system based on the wide-area electromagnetic method can be found in any embodiment of the present invention applied to the automated geophysical data processing method based on the wide-area electromagnetic method as described above, and will not be repeated here.

[0140] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions 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 restrictions on this.

[0141] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0142] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0143] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0144] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. 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. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0145] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An automated processing method for geophysical data based on the wide-area electromagnetic method, characterized in that, include: The raw detection data of the wide-area electromagnetic method is acquired, and the raw detection data of the wide-area electromagnetic method is preprocessed to obtain the processed detection data. Based on the processed detection data, valid data is extracted, and a GPS measurement data result atlas is automatically generated based on the valid data using spatial interpolation. Based on the processed detection data and GPS measurement data results atlas, data quality inspection and statistics are performed to obtain a GPS quality inspection statistical report and a quality inspection statistical table. Based on the processed detection data, generate wide-area electromagnetic measurement raw data results and various geophysical exploration result atlases; Based on the processed detection data and GPS measurement data, 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 as described in claim 1, characterized in that, The process of acquiring raw detection data using the wide-area electromagnetic method and preprocessing the raw detection data to obtain processed detection data includes: Acquire raw detection data using the wide-area electromagnetic method; The original detection data of the wide-area electromagnetic method is processed to remove missing values, duplicate values ​​and outliers, resulting in cleaned data. The cleaned data is then standardized, normalized, and discretized to achieve data transformation and obtain processed detection data.

3. The method as described in claim 1, characterized in that, The step of extracting effective data from the processed detection data and automatically generating a GPS measurement data result atlas based on the effective data using spatial interpolation includes: Extract the original GPS measurement data based on the processed detection data; Extract data that meets preset requirements based on the representation fields of the original GPS measurement data; By eliminating outliers from data that meet preset requirements based on satellite signal strength and positioning accuracy factors, valid data is obtained. Based on the valid data, a GPS measurement data result atlas is generated using spatial interpolation.

4. The method as described in claim 1, characterized in that, The data quality inspection and statistics are performed on the resulting atlas based on the processed detection data and GPS measurement data to obtain a GPS quality inspection statistical report and a quality inspection statistical table, including: Extract the original GPS quality inspection data based on the processed detection data; Import the GPS measurement data results atlas; The raw GPS quality inspection data and the GPS measurement data result map are subjected to data filtering and preset standard calculations to output GPS quality inspection statistical reports and quality inspection statistical tables.

5. The method as described in claim 1, characterized in that, The process of generating wide-area electromagnetic measurement raw data results and various geophysical exploration result atlases based on the processed detection data includes: Based on the processed detection data, extract the coordinates of the measuring point, the coordinates of the field source, the electric field file, the current file, the original electric field file, and the natural electric field file; The wide-area electromagnetic measurement data results are calculated based on the inversion algorithm according to the coordinates of the measurement point, the coordinates of the field source, the electric field file, the current file, the original electric field file, and the natural electric field file. Based on the processed detection data, a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas are generated.

6. The method as described in claim 5, characterized in that, The process of generating a wide-area electromagnetic sounding quality checkpoint atlas and a wide-area electromagnetic instrument consistency atlas based on the processed detection data includes: Extract checkpoint data based on the processed detection data for the checkpoint; The checkpoints are calculated according to the preset quality inspection formula based on the checkpoint data to automatically generate a wide-area depth sounding quality checkpoint map. Extract the original indoor consistency file and the original outdoor consistency file based on the processed detection data; The original indoor consistency files and the original outdoor consistency files are automatically batch-generated into a wide-area electromagnetic instrument consistency catalog through data integration and calculation.

7. The method as described in claim 1, characterized in that, The process involves plotting wide-area electromagnetic curves and pseudo-sections based on the processed detection data and GPS measurement data, and generating a wide-area electromagnetic inversion file, including: Extract the source coordinates, electric field data, and current data from the processed detection data; Import the GPS measurement data results atlas; A wide-area electromagnetic curve diagram is drawn based on the 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 the artificial field and natural field curves of wide-area electromagnetic sounding and a wide-area electromagnetic sounding apparent resistivity and electric field curve diagram; A wide-area electromagnetic pseudo-section diagram is drawn based on the processed detection data; A wide-area electromagnetic inversion file is generated based on the comprehensive data file formed by the GPS measurement data results map, field source coordinates, electric field data, and current data.

8. The method as described in claim 7, characterized in that, The step of drawing a wide-area electromagnetic pseudo-section diagram based on the processed detection data includes: The original data for generating the pseudo-section diagram is extracted from the processed detection data; Data parsing and standard calculations are performed on the original 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 as described in claim 8, characterized in that, The method further includes: When plotting wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections, the jar package provided in the Matlab tool is called, and the apparent resistivity data is parsed and calculated based on the jar package. The vector data to raster interface in the QGIS platform is called to perform format conversion of the simulated cross-section map results based on the vector data to raster interface.

10. An automated geophysical data processing system based on the wide-area electromagnetic method, characterized in that, include: The preprocessing module is used to acquire the raw detection data of the wide-area electromagnetic method, and to perform data preprocessing on the raw detection data of the wide-area electromagnetic method to obtain the processed detection data. The data processing module is used to extract effective data from the processed detection data and automatically generate a GPS measurement data result atlas based on the effective data using spatial interpolation. The quality inspection module is used to perform data quality inspection and statistics based on the processed detection data and GPS measurement data result atlas, and to obtain GPS quality inspection statistical report and quality inspection statistical table; The atlas generation module is used to generate atlases of wide-area electromagnetic measurement raw data results and various geophysical exploration results based on the processed detection data. The plotting module is used to plot wide-area electromagnetic curves and wide-area electromagnetic pseudo-sections based on the processed detection data and GPS measurement data, and to generate wide-area electromagnetic inversion files.

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