Geophysical exploration data processing method based on magnetic survey method, medium and equipment

Abstract

The invention provides a geophysical exploration data processing method based on a magnetic survey method, a medium and equipment, and relates to the technical field of geophysical exploration. Calculating the magnetometer consistency test data to remove abnormal values and obtain corresponding magnetometer consistency test data observation values; calculating the probe consistency test data to obtain an average value of the probe consistency test data and a difference value between each probe test data and the average value; drawing a picture book of host consistency observation data; calculating the noise determination test data to obtain a noise index; drawing a daily variation curve graph according to the daily variation observation data; calculating a magnetic anomaly value of ground magnetic measurement and subtracting the average field value; carrying out quality inspection and precision statistics on the ground magnetic measurement data; and summarizing processing results to generate a detection data processing report. According to the method, automation and intelligentization of data processing can be realized, manual intervention and judgment are reduced, and the efficiency, reliability and accuracy of data processing are improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a geophysical exploration data processing method, medium and equipment based on magnetic surveying. Background Art

[0002] High-precision ground magnetic survey is a commonly used method in geophysical exploration. It uses the changes in the geomagnetic field to detect underground geological structures and mineral resources. The traditional high-precision ground magnetic survey data processing process is cumbersome and requires a lot of manual data correction, filtering, interpretation and other work. It is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in low accuracy and reliability of data processing results.

[0003] At present, although there are some geophysical exploration data processing software that can be used for ground high-precision magnetic survey data processing, these software can usually only realize partial data processing automation, and for complex data processing tasks, manual intervention and judgment are still required. Therefore, how to improve the automation level of ground high-precision magnetic survey data processing, reduce the interference of human factors, and improve the accuracy and reliability of data processing is an urgent problem to be solved in the current field of geophysical exploration technology. Summary of the invention

[0004] The purpose of the present invention is to provide a geophysical exploration data processing method, medium and equipment based on magnetic surveying, aiming to realize automation and intelligence of data processing, reduce manual intervention and judgment, and improve the efficiency, reliability and accuracy of data processing. The specific technical scheme is as follows:

[0005] A method for processing geophysical exploration data based on magnetic surveying method, the method comprising the following steps:

[0006] S100, obtaining ground magnetic survey data, including magnetometer consistency test data, probe consistency test data, host consistency observation data, and noise measurement test data;

[0007] S200, preprocessing the ground magnetic survey data to achieve data accuracy and consistency;

[0008] S300, calculating the magnetometer consistency test data to remove outliers and obtain corresponding magnetometer consistency test data observation values, and calculating the mean square error of the magnetometer consistency test data observation values; calculating the probe consistency test data to obtain the average value of the probe consistency test data and the difference between each probe test data and the average value; using the host consistency observation data as the x-axis for time and the y-axis for reading, and drawing a map of the host consistency observation data by connecting points into lines; calculating the noise measurement test data to obtain the noise index; drawing a daily variation curve according to the daily variation observation data, and selecting the base value T0 and adding annotations; calculating the magnetic anomaly value of the ground magnetic measurement and subtracting the average field value;

[0009] S400, performing quality inspection and accuracy statistics on the ground magnetic survey data, and calculating and obtaining quality inspection and accuracy statistics results;

[0010] S500, summarizing the processing results of steps S300 and S400 to generate a detection data processing report.

[0011] Furthermore, in step S100, the ground magnetic survey data can be obtained by uploading a file or importing it into a database.

[0012] Furthermore, in step S200, preprocessing of the ground magnetic survey data includes data cleaning, format conversion, and normalization operations.

[0013] Furthermore, the step S200 is specifically: using regular expressions to parse the input unstructured data, and correcting data format errors and unifying the data format; the normalization calculation formula is as follows:

[0014]

[0015] Wherein, X is the ground magnetic survey data before normalization, max(X) is the maximum value of the ground magnetic survey data before normalization, min(X) is the minimum value of the ground magnetic survey data before normalization, and X′ is the ground magnetic survey data after normalization.

[0016] Furthermore, in step S300, calculating the magnetometer consistency test data to remove outliers includes the following steps: calculating the mean μ and standard deviation σ of the magnetometer consistency test data, defining the normal range as [μ-3σ, μ+3σ], and if a data point of the magnetometer consistency test data is not within the normal range, it is determined to be an outlier.

[0017] Furthermore, the calculation formulas for the mean μ and standard deviation σ of the magnetometer consistency test data are as follows:

[0018]

[0019]

[0020] Among them, x i represents the value of the ith data point in the dataset of the magnetometer consistency test data, and n represents the total number of data points in the dataset of the magnetometer consistency test data.

[0021] Furthermore, in step S300, the noise index includes a starting point difference, a difference average, an average interpolation square, and a noise root mean square error of the noise measurement test data.

[0022] Furthermore, in step S400, the quality inspection and accuracy statistics results include the number of magnetic survey points, the number of GPS points, geodetic control points, geodetic survey points, geodetic elevation error survey points, magnetic survey point observations, and the total accuracy of magnetic survey anomalies.

[0023] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the geophysical exploration data processing method based on magnetic surveying are implemented as described above.

[0024] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the geophysical exploration data processing method based on magnetic surveying as described above are implemented.

[0025] The present invention provides a geophysical exploration data processing method, medium and device based on magnetic surveying, which have the following beneficial effects:

[0026] The present invention obtains ground magnetic survey data, including magnetometer consistency test data, probe consistency test data, host consistency observation data, and noise measurement test data; pre-processes the ground magnetic survey data to achieve data accuracy and consistency; calculates the magnetometer consistency test data to remove abnormal values ​​and obtain corresponding magnetometer consistency test data observation values, and calculates the mean square error of the magnetometer consistency test data observation values; calculates the probe consistency test data to obtain the average value of the probe consistency test data and the difference between each probe test data and the average value; takes the host consistency observation data as time on the x-axis and as reading on the y-axis, and draws a map of the host consistency observation data by connecting points into lines; and performs noise measurement test data Calculate the noise index; draw a daily variation curve according to the daily variation observation data, select the base value T0 and add annotations; calculate the magnetic anomaly value of the ground magnetic survey and subtract the mean field value; perform quality inspection and accuracy statistics on the ground magnetic survey data, and calculate the quality inspection and accuracy statistics results; summarize the processing results and generate a detection data processing report; thereby realizing standardized module design and automated calculation process, as well as full-process automated processing of ground high-precision magnetic survey data from collection to report generation, with data consistency verification, noise measurement, daily variation curve processing, GPS data calibration and accuracy statistics functions, which can reduce manual intervention and judgment, improve the efficiency, reliability and accuracy of data processing, ensure data accuracy, and generate results reports that meet standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a geophysical exploration data processing method based on magnetic surveying provided by the present invention;

[0028] Figure 2 is an overall flow chart of an embodiment of the present invention;

[0029] Figure 3 This is the magnetometer host consistency test diagram;

[0030] Figure 4 is a graph of magnetometer noise level determination;

[0031] Figure 5 It is a schematic diagram of the daily variation observation curve and the selection of T0 value;

[0032] Figure 6 It is a quality inspection and accuracy statistical chart;

[0033] Figure 7 It is a structural block diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying drawings provided by the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. According to the following description, the advantages and features of the present invention will be more clear. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0035] Example 1

[0036] This embodiment provides a method for processing geophysical exploration data based on magnetic surveying. Figure 1 , 2 As shown, the method comprises the following steps:

[0037] S100, obtaining ground magnetic survey data, including magnetometer consistency test data, probe consistency test data, host consistency observation data, and noise measurement test data.

[0038] Specifically, users input high-precision ground magnetic survey data, such as magnetometer consistency test data, probe consistency test data, host consistency observation data, noise measurement test data, etc., and the content required for related reports into the system. Data can be obtained by file upload, database import, etc.

[0039] S200, preprocessing the ground magnetic survey data to achieve data accuracy and consistency.

[0040] In one embodiment, preprocessing the ground magnetic survey data includes data cleaning, format conversion, and normalization operations.

[0041] Specifically, regular expressions are used to parse the input unstructured data, correct data format errors, and unify the data format; the normalization calculation formula is as follows:

[0042]

[0043] Wherein, X is the ground magnetic survey data before normalization, max(X) is the maximum value of the ground magnetic survey data before normalization, min(X) is the minimum value of the ground magnetic survey data before normalization, and X' is the ground magnetic survey data after normalization.

[0044] S300, calculating the magnetometer consistency test data to remove outliers and obtain corresponding magnetometer consistency test data observation values, and calculating the mean square error of the magnetometer consistency test data observation values; calculating the probe consistency test data to obtain the average value of the probe consistency test data and the difference between each probe test data and the average value; using the host consistency observation data as the time on the x-axis and the reading on the y-axis, and drawing a map of the host consistency observation data by connecting points into lines; calculating the noise measurement test data to obtain the noise index; drawing a daily variation curve according to the daily variation observation data, and selecting the base value T0 and adding annotations; calculating the magnetic anomaly value of the ground magnetic measurement and subtracting the mean field value.

[0045] In a preferred embodiment, calculating the magnetometer consistency test data to remove outliers includes the following steps: calculating the mean μ and standard deviation σ of the magnetometer consistency test data, defining the normal range as [μ-3σ,μ+3σ], and if a data point of the magnetometer consistency test data is not within the normal range, it is determined to be an outlier, that is, if a data point x satisfies x<μ-3σx or x>μ+3σx, it is determined to be an outlier.

[0046] The calculation formulas for the mean μ and standard deviation σ of the magnetometer consistency test data are as follows:

[0047]

[0048] Wherein, xi represents the value of the i-th data point in the dataset of the magnetometer consistency test data, and n represents the total number of data points in the dataset of the magnetometer consistency test data.

[0049] The 3σ principle statistical method is used to detect outliers. In a normal distribution, most data (about 99.73%) will fall within the range of three times the standard deviation σ of the mean μ, that is: [μ-3σ,μ+3σ]. Data points outside this range are considered outliers.

[0050] See also Figure 3 As shown, a map of host consistency observation data is drawn, with the x-axis being time and the y-axis being reading, and the consistency of the host is displayed by connecting points into lines.

[0051] In a preferred embodiment, the noise index includes the starting point difference, difference average, average interpolation square, and noise root mean square error of the noise measurement test data. After the noise index is calculated, a measurement curve can be drawn, such as Figure 4 shown.

[0052] Optionally, use the Fast Fourier Transform (FFT) to analyze the spectral characteristics of the noise: decompose a signal into the sum of sine waves of different frequencies. For a continuous signal f(t), its Fourier transform is:

[0053]

[0054] Among them, F(ω) is the spectrum of the signal at frequency ω, f(t) is the time domain signal, and e -jωt is the complex exponential function of the frequency component.

[0055] For discrete signals (digital signals), use the Discrete Fourier Transform (DFT):

[0056]

[0057] Where F(k) is the amplitude of the frequency component, N is the number of signal samples, and f(n) is the signal value at the nth time point.

[0058] The root mean square error RMS is calculated as a measure of noise intensity, and the formula is as follows:

[0059]

[0060] Among them, x i represents the value of the ith data point in the data set of the noise measurement test data, represents the average value of the noise measurement test data, and N represents the total number of data points in the data set of the noise measurement test data.

[0061] According to the input daily variation observation data, draw the daily variation curve and select the base value T0, and add annotations, such as Figure 5 shown.

[0062] S400, performing quality inspection and precision statistics on the ground magnetic survey data, and calculating and obtaining quality inspection and precision statistics results.

[0063] In a preferred embodiment, the quality inspection and accuracy statistics include the number of magnetic survey points, GPS points, geodetic control points, geodetic survey points, geodetic elevation error survey points, magnetic survey point observations, and total magnetic survey anomaly accuracy. Figure 6 shown.

[0064] S500, summarizing the processing results of steps S300 and S400 to generate a detection data processing report.

[0065] The geophysical exploration data processing method based on magnetic survey provided by the present invention obtains ground magnetic survey data, including magnetometer consistency test data, probe consistency test data, host consistency observation data, and noise measurement test data; pre-processes the ground magnetic survey data to achieve data accuracy and consistency; calculates the magnetometer consistency test data to remove abnormal values ​​and obtain corresponding magnetometer consistency test data observation values, and calculates the mean square error of the magnetometer consistency test data observation values; calculates the probe consistency test data to obtain the average value of the probe consistency test data and the difference between each probe test data and the average value; and draws a map of the host consistency observation data by connecting points into lines, with the x-axis as time and the y-axis as reading. ; Calculate the noise index from the noise measurement test data; Draw a daily variation curve based on the daily variation observation data, and select the base value T0 and add annotations; Calculate the magnetic anomaly value of the ground magnetic survey and subtract the mean field value; Perform quality inspection and accuracy statistics on the ground magnetic survey data, and calculate the quality inspection and accuracy statistics results; Summarize the processing results and generate a detection data processing report; thereby realizing standardized module design and automated calculation process, as well as full-process automated processing of ground high-precision magnetic survey data from collection to report generation, with data consistency verification, noise measurement, daily variation curve processing, GPS data calibration and accuracy statistics functions, which can reduce manual intervention and judgment, improve the efficiency, reliability and accuracy of data processing, ensure data accuracy, and generate results reports that meet standards.

[0066] Example 2

[0067] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the geophysical exploration data processing method based on magnetic surveying are implemented as described above.

[0068] The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memory.

[0069] Example 3

[0070] This embodiment provides a computer device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the geophysical exploration data processing method based on magnetic surveying as described above when executing the program.

[0071] like Figure 7 As shown, the computer device may include: at least one processor 71, such as a CPU (Central Processing Unit), at least one communication interface 73, a memory 74, and at least one communication bus 72. The communication bus 72 is used to realize the connection and communication between these components. The communication interface 73 may include a display screen (Display) and a keyboard (Keyboard), and the optional communication interface 73 may also include a standard wired interface and a wireless interface. The memory 74 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk storage. The memory 74 may optionally be at least one storage device located away from the aforementioned processor 71. The memory 74 stores application programs, and the processor 71 calls the program code stored in the memory 74 to execute any of the above method steps.

[0072] The communication bus 72 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The communication bus 72 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0073] Among them, the memory 74 may include a volatile memory (English: volatile memory), such as a random access memory (English: random-access memory, abbreviated: RAM); the memory may also include a non-volatile memory (English: non-volatile memory), such as a flash memory (English: flash memory), a hard disk (English: hard disk drive, abbreviated: HDD) or a solid-state drive (English: solid-state drive, abbreviated: SSD); the memory 74 may also include a combination of the above types of memory.

[0074] The processor 71 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and a NP.

[0075] The processor 71 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0076] Optionally, the memory 74 is also used to store program instructions. The processor 71 can call the program instructions to implement the geophysical exploration data processing method based on magnetic surveying method of the present invention.

[0077] Those skilled in the art should understand that the present invention can be implemented in many other specific forms without departing from the spirit and scope of the present invention. Based on the embodiments of the present invention, any changes and modifications made by ordinary technicians in the field of the present invention according to the above disclosure are within the scope of protection of the claims.

Claims

1. A method for processing geophysical exploration data based on magnetic surveying, characterized in that: The method comprises the following steps: S100, obtaining ground magnetic survey data, including magnetometer consistency test data, probe consistency test data, host consistency observation data, and noise measurement test data; S200, preprocessing the ground magnetic survey data to achieve data accuracy and consistency; S300, calculating the magnetometer consistency test data to remove outliers and obtain corresponding magnetometer consistency test data observation values, and calculating the mean square error of the magnetometer consistency test data observation values; calculating the probe consistency test data to obtain the average value of the probe consistency test data and the difference between each probe test data and the average value; using the host consistency observation data as the x-axis for time and the y-axis for reading, and drawing a map of the host consistency observation data by connecting points into lines; calculating the noise measurement test data to obtain the noise index; drawing a daily variation curve according to the daily variation observation data, and selecting the base value T0 and adding annotations; calculating the magnetic anomaly value of the ground magnetic measurement and subtracting the average field value; S400, performing quality inspection and accuracy statistics on the ground magnetic survey data, and calculating and obtaining quality inspection and accuracy statistics results; S500, summarizing the processing results of steps S300 and S400 to generate a detection data processing report.

2. The method for processing geophysical exploration data based on magnetic surveying according to claim 1, characterized in that: In step S100, the ground magnetic survey data can be obtained by uploading files or importing them into a database.

3. The method for processing geophysical exploration data based on magnetic surveying according to claim 1, characterized in that: In step S200, the ground magnetic survey data is preprocessed including data cleaning, format conversion, and normalization operations.

4. The method for processing geophysical exploration data based on magnetic surveying according to claim 3, characterized in that: The step S200 specifically includes: using regular expressions to parse the input unstructured data, correcting data format errors and unifying the data format; the normalization calculation formula is as follows: Wherein, X is the ground magnetic survey data before normalization, max(X) is the maximum value of the ground magnetic survey data before normalization, min(X) is the minimum value of the ground magnetic survey data before normalization, and X′ is the ground magnetic survey data after normalization.

5. The method for processing geophysical exploration data based on magnetic surveying according to claim 1, characterized in that: In step S300, calculating the magnetometer consistency test data to remove outliers includes the following steps: calculating the mean μ and standard deviation σ of the magnetometer consistency test data, defining the normal range as [μ-3σ, μ+3σ], and determining that a data point of the magnetometer consistency test data is an outlier if it is not within the normal range.

6. The method for processing geophysical exploration data based on magnetic surveying according to claim 5, characterized in that: The calculation formulas for the mean μ and standard deviation σ of the magnetometer consistency test data are as follows: Among them, x i represents the value of the ith data point in the dataset of the magnetometer consistency test data, and n represents the total number of data points in the dataset of the magnetometer consistency test data.

7. The method for processing geophysical exploration data based on magnetic surveying according to claim 1, characterized in that: In step S300, the noise index includes the starting point difference, the difference average, the average interpolation square, and the noise root mean square error of the noise measurement test data.

8. The method for processing geophysical exploration data based on magnetic surveying according to claim 1, characterized in that: In step S400, the quality inspection and accuracy statistics results include the number of magnetic survey points, the number of GPS points, the geodetic control points, the geodetic survey points, the geodetic elevation error survey points, the magnetic survey point observations, and the total accuracy of the magnetic survey anomalies.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for processing geophysical exploration data based on magnetic surveying are implemented as described in any one of claims 1 to 8.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the geophysical exploration data processing method based on magnetic surveying as described in any one of claims 1 to 8 are implemented.

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