A method and system for processing magnetotelluric sounding data splicing

Abstract

The application provides a magnetotelluric sounding data splicing processing method and system, and the method comprises the following steps: (1) acquiring magnetotelluric sounding data and regional rock electrical property parameter data corresponding to different measuring points of adjacent blocks; (2) performing quality inspection on the magnetotelluric sounding data; (3) performing quality evaluation and classification on the magnetotelluric sounding data; (4) performing standardization processing on the magnetotelluric sounding data; (5) performing pretreatment on the magnetotelluric sounding data; (6) performing normalization processing on the magnetotelluric sounding data; (7) performing splicing processing on the normalized magnetotelluric sounding data, performing data smoothing transition on the data of the overlapping area of adjacent blocks, and performing inspection on the residual distribution of the data before and after splicing; (8) performing joint inversion on the spliced data; and (9) performing analysis on the depth domain resistivity data body. The application has reasonable design and can be applied to secondary continuous processing of magnetotelluric data collected in different periods of oil and gas exploration.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a method and system for stitching and processing magnetotelluric sounding data. Background Technology

[0002] Magnetotelluric sounding is an exploration method that studies the electrical properties and distribution characteristics of underground rock strata by observing the natural alternating electromagnetic field on the ground. Due to its advantages such as large exploration depth, lack of shielding by high-resistivity strata, high resolution for low-resistivity strata, and simple construction, it is widely used in oil and gas exploration.

[0003] As oil and gas exploration and development deepens, it is necessary to reprocess and interpret the magnetotelluric sounding data of adjacent blocks collected at different times, so as to provide reliable data for further comprehensive geological structure research in the whole region.

[0004] The construction and processing factors vary considerably across different blocks. Due to differences in the acquisition instruments, random interference, and the large span of acquisition years, there are errors between data collected in different years. This leads to significant errors in the splicing and processing of data from different blocks, resulting in obvious splicing marks that directly affect the tectonic morphology and make it difficult to reinterpret existing magnetotelluric sounding profiles.

[0005] Therefore, there is an urgent need for a method for stitching and processing magnetotelluric sounding data to improve the quality of data processing and provide strong support for the interpretation of regional integrated data. Summary of the Invention

[0006] To address the technical problems existing in the background art mentioned above, this invention proposes a method and system for stitching and processing magnetotelluric sounding data. Its concept is reasonable and can be applied to the secondary stitching processing of magnetotelluric data collected at different stages of oil and gas exploration, thereby improving the quality of data processing and providing technical support for subsequent geological interpretation and petroleum geological analysis research.

[0007] To solve the above-mentioned technical problems, the present invention provides a method for stitching and processing magnetotelluric sounding data, which includes the following steps:

[0008] (1) Obtain magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent electromagnetic exploration blocks;

[0009] (2) Conduct quality checks on the magnetotelluric sounding data and analyze whether there are any outliers or data acquisition errors;

[0010] (3) Classify and evaluate the quality of magnetotelluric sounding data based on the quality inspection results, and remove invalid data;

[0011] (4) Standardize the magnetotelluric sounding data to improve data consistency;

[0012] (5) Preprocess the magnetotelluric sounding data to ensure data quality and reduce noise and anomaly effects;

[0013] (6) Normalize the magnetotelluric sounding data to eliminate interference from non-geological signals;

[0014] (7) The normalized magnetotelluric sounding data are spliced ​​together. The data in the overlapping areas of adjacent blocks are smoothed by weighted average or sliding window method. The residual distribution of the spliced ​​data and the original data is checked to ensure that the splicing does not introduce systematic bias.

[0015] (8) The Occam optimal algorithm is used to perform joint inversion on the stitched data to obtain the stitched depth domain resistivity data volume model. The depth domain resistivity data volume is analyzed to avoid over-correction and ensure that the stitching results are consistent with the known geological structures.

[0016] The magnetotelluric sounding data stitching and processing method, wherein step (2) specifically includes the following steps:

[0017] (2.1) Check the apparent resistivity and phase curves of the magnetotelluric sounding data to confirm whether the curves are continuous and smooth, and whether there are any abrupt frequency jumps.

[0018] (2.2) The data confidence of magnetotelluric sounding data is evaluated by the error ellipse of the impedance tensor. The smaller the ellipse, the more reliable the data.

[0019] (2.3) Check the coherence of the electric and magnetic field components in the magnetotelluric sounding data of the electromagnetic exploration block, analyze the correlation of electromagnetic signals, and the value is less than 0.8, which is low coherence.

[0020] The magnetotelluric sounding data stitching processing method, wherein the specific process of analyzing the correlation of electromagnetic signals in step (2.3) is as follows:

[0021] (2.3.1) Perform spectral transformation on the original time series data of magnetotelluric sounding to extract the spectral characteristics of each component of electric and magnetic fields;

[0022] (2.3.2) Calculate the self-power spectrum and cross-power spectrum of each component of the electric and magnetic fields;

[0023] (2.3.3) The coherence between the electric and magnetic field components is calculated based on the self-power spectrum and cross-power spectrum. The coherence range is between 0 and 1. The closer it is to 1, the higher the signal correlation and the lower the noise interference. The formula is:

[0024]

[0025] The magnetotelluric sounding data stitching processing method, wherein the evaluation classification in step (3) includes high-quality data, usable data and invalid data; the high-quality data refers to data with high signal-to-noise ratio, smooth curve, small error ellipse and coherence greater than 0.9; the usable data refers to data with noise in some frequency bands but which can be used after processing; the invalid data refers to data with serious noise pollution or missing frequency points.

[0026] The magnetotelluric sounding data stitching and processing method, wherein step (4) specifically includes the following steps:

[0027] (4.1) Correct the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system to ensure that all points use the same coordinate system;

[0028] (4.2) Interpolate the magnetotelluric sounding data to unify the frequency range of the measurement points and align the frequency points of the measurement points;

[0029] (4.3) Convert the magnetotelluric sounding data to a specific direction to optimize data quality and eliminate impedance tensor deviation caused by differences in measurement orientation;

[0030] (4.4) Convert the magnetotelluric sounding data collected by different magnetotelluric sounding instruments into a unified EDI standard data format.

[0031] The magnetotelluric sounding data splicing processing method, wherein the specific process of step (4.2) is as follows: check the frequency range and sampling interval of the magnetotelluric sounding data of adjacent blocks, check whether the effective frequency bands of different measuring points overlap, interpolate the missing frequency bands, align the frequency points, and ensure that the frequency range is consistent and the spliced ​​data is aligned on the same frequency points.

[0032] The magnetotelluric sounding data stitching and processing method, wherein step (4.3) specifically involves: based on the quality of the magnetotelluric sounding data, selecting the phase tensor method to convert the magnetotelluric sounding data to the tectonic direction, optimizing the data quality, and eliminating impedance tensor deviations caused by differences in measurement azimuth; the specific steps are as follows:

[0033] (4.3.1) Calculate the magnetotelluric impedance tensor Z = E / H, which is the ratio of electric field strength E to magnetic field strength H;

[0034] (4.3.2) Rotate the impedance tensor Z to the construction direction to obtain the rotated impedance tensor Z′:

[0035] Z′=R(θ)×Z×R T (θ)

[0036] Where θ is the rotation angle, and R(θ) is the rotation matrix.

[0037] The magnetotelluric sounding data stitching processing method, wherein step (5) specifically includes the following steps:

[0038] (5.1) Noise filtering is performed on the magnetotelluric sounding data;

[0039] (5.2) Polarization pattern identification of magnetotelluric sounding data;

[0040] (5.3) Use the finite element method or finite difference method to simulate the influence of terrain and eliminate the electromagnetic field distortion caused by terrain;

[0041] (5.4) Determine the resistivity of the surface or shallow strata lithology at the location of the magnetotelluric sounding point based on the measurement results of the regional rock electrical parameters, obtain the correct position of the first branch of the apparent resistivity curve, translate the entire apparent resistivity curve so that the first branch of the measuring point curve reaches the correct position, correct the measuring points that have static displacement, and eliminate the influence of static displacement caused by near-surface electrical inhomogeneities.

[0042] The magnetotelluric sounding data stitching processing method includes step (5.1), which specifically involves using median filtering to eliminate instrument transient noise in the magnetotelluric sounding data, suppressing spike pulse noise, using average value filtering to suppress high-frequency random noise in the magnetotelluric sounding data, using Gaussian filtering to suppress broadband noise in the magnetotelluric sounding data, smoothing the spectral curve, enhancing the effective signal, and improving the signal-to-noise ratio and reliability of the data.

[0043] The magnetotelluric sounding data stitching processing method, wherein: step (5.2) specifically involves decomposing the impedance tensor into eigenvalues, obtaining two eigenvalues ​​and the corresponding eigenvectors, determining the shape and direction of the polarization ellipse, performing polarization pattern recognition on the magnetotelluric sounding data, identifying and correcting abnormal data caused by instrument errors or environmental interference, and improving the overall data reliability.

[0044] The magnetotelluric sounding data stitching and processing method, wherein the specific process of correcting the measuring points exhibiting static displacement in step (5.4) is as follows:

[0045] (5.4.1) Based on the measurement results of regional rock electrical parameters, calculate the average resistivity ρ of each lithology in each stratum. a Calculation formula:

[0046]

[0047] In the above formula, n is the number of specimen blocks, ρ i Let be the resistivity value of the i-th specimen;

[0048] (5.4.2) The resistivity average value of each lithology in each stratum is weighted by thickness to obtain the resistivity value ρ of each stratum. b Calculation formula:

[0049]

[0050] In the above formula, n is the number of lithologies in each stratigraphic set, and h n Let ρ be the thickness of the nth lithology within each stratum. an The average resistivity of the nth lithology;

[0051] (5.4.3) Statistically analyze the distribution of magnetotelluric sounding points in different exposed strata of adjacent blocks, based on the resistivity value ρ of the exposed strata. b Determine the correct location of the first branch of the apparent resistivity curve at each magnetotelluric sounding point;

[0052] (5.4.4) Without changing the shape of the apparent resistivity curve of the magnetotelluric sounding point, the second frequency point of the apparent resistivity curve of the magnetotelluric sounding point in different exposed strata areas is shifted to the correct position of the first branch, and other frequency points are shifted as a whole based on the second frequency point.

[0053] (5.4.5) Based on this standard, the measuring points that exhibit static displacement are corrected to eliminate the influence of static displacement caused by near-surface electrical inhomogeneities.

[0054] The magnetotelluric sounding data stitching processing method, wherein step (6) specifically includes the following steps:

[0055] (6.1) For each electromagnetic wave frequency f, calculate the ratio of the average apparent resistivity of the common measuring point at the boundary between adjacent electromagnetic exploration blocks A and B:

[0056]

[0057] ρ of all measurement points in block A A (f) is corrected by multiplying by κ(f);

[0058] (6.2) For each frequency f, calculate the average impedance phase difference at the common point of the boundary between adjacent blocks A and B:

[0059]

[0060] The impedance phase of all measuring points in block A is calibrated by adding Δθ(f) and taking the modulus of 360°.

[0061] The magnetotelluric sounding data stitching and processing method, wherein the specific process of analyzing the depth domain resistivity data volume in step (8) is as follows:

[0062] (8.1) The “geological capping method” is adopted to track the transverse variation trend of the electrical layer along the profile based on the geoelectric correspondence and the surface geology and well logging data to calibrate the depth domain resistivity data volume.

[0063] (8.2) If gravity and magnetic exploration data exist in adjacent blocks, extract the depth domain resistivity data volume at the corresponding location of the gravity and magnetic survey line to verify whether the regional characteristics such as structural zoning and basement undulation match.

[0064] (8.3) If there is seismic exploration data in adjacent blocks, extract the resistivity profile at the corresponding location of the seismic survey line and compare it with the seismic reflection interface to verify whether the vertical resolution matches.

[0065] (8.4) If the resistivity variation characteristics do not match the regional characteristics reflected by gravity and magnetism and deviate from the seismic interface, the correction parameters need to be re-evaluated.

[0066] A magnetotelluric sounding profile data stitching and processing system includes a data acquisition unit, a data quality inspection and evaluation unit, a data standardization unit, a data preprocessing unit, a data normalization unit, a data stitching unit, and a data inversion unit.

[0067] The data acquisition unit is used to acquire magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent blocks.

[0068] The data quality inspection and evaluation unit is used to inspect, evaluate and classify the magnetotelluric sounding data, and remove invalid data.

[0069] The data standardization unit is used to correct magnetotelluric sounding data to the same coordinate system, unify the frequency range of measuring points, align frequency points, convert data to a specific direction, unify the data format, ensure that all data files are in the same format, and optimize data quality.

[0070] The data preprocessing unit is used to preprocess the magnetotelluric sounding data to ensure data quality and avoid error accumulation after splicing.

[0071] The data normalization unit is used to eliminate non-geological signal interference caused by adverse factors, so that the data from different blocks have consistency and comparability when splicing or jointly inverting.

[0072] The data stitching unit is used to stitch together magnetotelluric sounding data, and to achieve smooth data transition by using weighted average or sliding window methods for data in overlapping areas of adjacent blocks. It also checks the residual distribution of data before and after stitching to ensure that no systematic bias is introduced during stitching.

[0073] The data inversion unit uses the Occam optimal algorithm to perform joint inversion on the stitched data to obtain a stitched depth domain resistivity data volume model; the depth domain resistivity data volume is analyzed to avoid over-correction and ensure that the stitching results are consistent with known geological structures.

[0074] The magnetotelluric sounding profile data stitching and processing system includes a data quality inspection and evaluation unit comprising a quality inspection module and a quality evaluation module. The quality inspection module is used to perform quality inspection on the magnetotelluric sounding data, while the quality evaluation module is used to evaluate and classify the magnetotelluric sounding data and remove invalid data.

[0075] The magnetotelluric sounding profile data stitching and processing system includes: a data standardization unit comprising a measurement point coordinate unification module, a measurement point frequency range and frequency alignment module, a data conversion to a specific direction module, and a data format unification module; the measurement point coordinate unification module is used to correct the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system; the measurement point frequency range and frequency alignment module is used to check the frequency range and sampling interval of magnetotelluric sounding data in adjacent blocks, unify the measurement point frequency range, and align the measurement point frequencies; the data conversion to a specific direction module is used to convert magnetotelluric sounding data to a specific direction, optimize data quality, and eliminate impedance tensor deviations caused by differences in measurement azimuth; the data format unification module is used to convert data collected by different magnetotelluric sounding instruments into a unified EDI standard data format, ensuring the consistency of the main magnetotelluric sounding data parameters, namely apparent resistivity, impedance phase, and impedance tensor.

[0076] The magnetotelluric sounding profile data stitching and processing system includes the following: The data preprocessing unit comprises a denoising and filtering module, a polarization pattern recognition module, a terrain correction module, and a static displacement correction module. The denoising and filtering module uses median filtering, average value filtering, and Gaussian filtering methods to denoise the magnetotelluric sounding data, eliminating high-frequency noise and low-frequency interference. The polarization pattern recognition module performs polarization pattern recognition on the magnetotelluric sounding data, identifying and correcting abnormal data caused by instrument errors or environmental interference, thus improving the overall data reliability. The terrain correction module uses finite element or finite difference methods to simulate terrain effects and eliminate electromagnetic field distortion caused by terrain. The static displacement correction module obtains the position of the first branch of the apparent resistivity curve, translates the entire apparent resistivity curve to ensure the first branch of the measuring point curve is in the correct position, corrects measuring points exhibiting static displacement, and eliminates the static displacement effect caused by near-surface electrical inhomogeneities.

[0077] The magnetotelluric sounding profile data stitching and processing system includes: a data normalization unit comprising an apparent resistivity correction module and an impedance phase correction module; the apparent resistivity correction module is used to calculate the average ratio of apparent resistivity at common measuring points and perform ratio correction; the impedance phase correction module is used to calculate the impedance phase difference at common measuring points and perform phase difference correction.

[0078] The magnetotelluric sounding profile data stitching and processing system includes: a data stitching unit comprising a data stitching module and a data checking module; the data stitching module is used to stitch data in overlapping areas of adjacent blocks using a weighted average or sliding window method to achieve a smooth data transition; the data checking module is used to check the residual distribution of the data before and after stitching to ensure that the stitching does not introduce systematic bias.

[0079] By adopting the above technical solution, the present invention has the following beneficial effects:

[0080] The magnetotelluric sounding data stitching and processing method and system of this invention are rationally conceived. By integrating electromagnetic data from adjacent regions, different observation scales, and frequency ranges, it improves the efficiency of technical application and can be applied to the secondary concatenation processing of magnetotelluric data collected at different stages of oil and gas exploration. The stitched regional structural profile can provide technical support for subsequent geological interpretation and petroleum geological analysis. The method of this invention has been successfully applied in the construction and structural analysis of regional structural profiles in western China. Simultaneously, this invention also has broad application value and prospects in geological structural research, environmental protection, and engineering exploration. In geological structural research, stitching processing can help reveal the geological structure of the deep crust, providing a scientific basis for earthquake prediction and geological disaster prevention. In environmental protection and engineering exploration, stitching processing can provide more accurate subsurface information, helping to avoid underground obstacles and ensuring engineering safety and environmental protection. Attached Figure Description

[0081] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0082] Figure 1 This is a flowchart of the magnetotelluric sounding data stitching and processing method of the present invention;

[0083] Figure 2 This is a structural block diagram of the magnetotelluric sounding data stitching and processing system of the present invention;

[0084] Figure 3The resistivity inversion profiles of two example blocks A and B directly spliced ​​together in this invention are shown.

[0085] Figure 4 This is a resistivity inversion profile diagram of two example blocks A and B spliced ​​together according to the present invention. Detailed Implementation

[0086] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] The present invention will be further explained below with reference to specific embodiments.

[0088] like Figure 1 As shown in the figure, the magnetotelluric sounding data stitching processing method provided in this embodiment mainly includes the following steps:

[0089] S100: Obtain magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent blocks.

[0090] S200. Perform a quality check on the magnetotelluric sounding data to check for outliers or data acquisition errors; the specific process is as follows:

[0091] S210. Check the apparent resistivity and phase curves of the magnetotelluric sounding data to confirm whether the curves are continuous and smooth, and whether there are any abrupt frequency jumps.

[0092] S220. The data confidence of magnetotelluric sounding data is evaluated by the error ellipse of the impedance tensor. The smaller the ellipse, the more reliable the data.

[0093] S230. Examine the coherence of the electric and magnetic field components in the magnetotelluric sounding data of the electromagnetic exploration block, analyze the correlation of the electromagnetic signals, and a value less than 0.8 indicates low coherence; the specific process for analyzing the correlation of the electromagnetic signals is as follows:

[0094] S231. Perform spectral transformation on the original time series data of magnetotelluric sounding to extract the spectral characteristics of each component of the electric field (Ex,Ey) and magnetic field (Hx,Hy,Hz).

[0095] S232. Calculate the self-power spectrum and cross-power spectrum of each component of the electric and magnetic fields;

[0096] S233. Calculate the coherence between the electric and magnetic field components based on the self-power spectrum and cross-power spectrum. The coherence range is between 0 and 1. The closer it is to 1, the higher the signal correlation and the lower the noise interference. The formula is:

[0097]

[0098] S300. Based on the inspection results, the magnetotelluric sounding data is evaluated and classified, and invalid data is removed. The evaluation classification includes high-quality data, usable data, and invalid data. High-quality data is data with high signal-to-noise ratio, smooth curve, small error ellipse, and coherence greater than 0.9. Usable data is data with noise in some frequency bands but which can be used after processing. Invalid data is data with serious noise pollution or missing frequency points.

[0099] S400. Standardize the magnetotelluric sounding data to improve data consistency; the specific process is as follows:

[0100] S410. Correct the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system to ensure that all points use the same coordinate system.

[0101] S420. Check the frequency range and sampling interval of magnetotelluric sounding data in adjacent blocks, check whether the effective frequency bands of different measuring points overlap, interpolate the magnetotelluric sounding data (i.e., use Kriging interpolation to interpolate missing frequency bands), align frequency points, and ensure that the frequency range is consistent and the spliced ​​data is aligned on the same frequency points.

[0102] S430. Convert magnetotelluric sounding data to a specific direction (such as structural strike or dip) to optimize data quality and eliminate impedance tensor bias caused by differences in measurement azimuth. The specific process is as follows: Use the phase tensor method to convert the data to the structural direction, optimize data quality, and eliminate impedance tensor bias caused by differences in measurement azimuth. The specific steps are as follows:

[0103] S431. Calculate the magnetotelluric impedance tensor Z = E / H, which is the ratio of electric field strength (E) to magnetic field strength (H).

[0104] S432. Rotate the impedance tensor Z to the construction direction to obtain the rotated impedance tensor Z′:

[0105] Z′=R(θ)×Z×R T (θ);

[0106] Where θ is the rotation angle, and R(θ) is the rotation matrix.

[0107] S440 converts magnetotelluric sounding data (such as SPE and XPR formats) collected by different magnetotelluric sounding instruments into a unified EDI standard data format to ensure the consistency of parameters such as apparent resistivity, impedance phase, and impedance tensor.

[0108] S500: Preprocess the magnetotelluric sounding data to ensure data quality and reduce the impact of noise and anomalies; the specific process is as follows:

[0109] S510. Denoising and filtering are performed on the magnetotelluric sounding data. Specifically, median filtering is used to eliminate instrument transient noise and suppress spike noise in the magnetotelluric sounding data. Average value filtering is used to suppress high-frequency random noise in the magnetotelluric sounding data. Gaussian filtering is used to suppress broadband noise in the magnetotelluric sounding data, smooth the spectrum curve, enhance the effective signal, and improve the signal-to-noise ratio and reliability of the data.

[0110] S520. Polarization pattern recognition of magnetotelluric sounding data; specifically, eigenvalue decomposition of the impedance tensor is performed to obtain two eigenvalues ​​and the corresponding eigenvectors, determining the shape and direction of the polarization ellipse, and polarization pattern recognition of magnetotelluric sounding data to identify and correct abnormal data caused by instrument errors or environmental interference, thereby improving the overall data reliability.

[0111] S530. Use the finite element method or finite difference method to simulate the influence of terrain and eliminate electromagnetic field distortion caused by terrain; specifically, use the finite element method to decompose the total magnetotelluric field into the terrain distortion field and the background field, and eliminate terrain interference by the ratio of the pure terrain model response to the total field response.

[0112] S540. Based on the measurement results of regional rock electrical parameters, determine the resistivity of the surface or shallow strata lithology at the location of the magnetotelluric sounding point, obtain the correct position of the first branch of the apparent resistivity curve, and translate the entire apparent resistivity curve so that the first branch of the curve at the measuring point reaches the correct position. Correct the measuring points exhibiting static displacement to eliminate the influence of static displacement caused by near-surface electrical inhomogeneities. The specific process for correcting measuring points exhibiting static displacement is as follows:

[0113] S541. Based on the measurement results of regional rock electrical parameters, calculate the average resistivity ρ of each lithology for each stratum. a Calculation formula:

[0114]

[0115] In the above formula, n is the number of specimen blocks, ρ i Let be the resistivity value of the i-th specimen;

[0116] S542. The resistivity average of each lithology in each stratum is weighted by thickness to obtain the resistivity value ρ of each stratum.b Calculation formula:

[0117]

[0118] In the above formula, n is the number of lithologies in each stratigraphic set, and h n Let ρ be the thickness of the nth lithology within each stratum. an The average resistivity of the nth lithology;

[0119] S543. Statistically analyze the distribution of magnetotelluric sounding points in different exposed strata of adjacent blocks, based on the resistivity value ρ of the exposed strata. b Determine the correct location of the first branch of the apparent resistivity curve at each magnetotelluric sounding point;

[0120] S544. Without changing the shape of the apparent resistivity curve of the magnetotelluric sounding point, shift the second frequency point of the apparent resistivity curve of the magnetotelluric sounding point in different exposed strata to the correct position of the first branch, and shift the other frequency points as a whole based on the second frequency point.

[0121] S545. Based on this standard, the measuring points that exhibit static displacement are corrected to eliminate the influence of static displacement caused by near-surface electrical inhomogeneities.

[0122] S600. Normalize the magnetotelluric sounding data to eliminate non-geological signal interference; the specific process is as follows:

[0123] S610. For each electromagnetic wave frequency f, calculate the ratio of the average apparent resistivity at the common measuring point at the boundary between adjacent electromagnetic exploration blocks A and B:

[0124]

[0125] ρ of all measurement points in block A A (f) is corrected by multiplying by κ(f);

[0126] S620. For each frequency f, calculate the average impedance phase difference at the common point of adjacent blocks A and B at their boundary:

[0127]

[0128] The impedance phase of all measuring points in block A is calibrated by adding Δθ(f) and taking the modulus of 360°.

[0129] S700. The normalized magnetotelluric sounding data is stitched together. For overlapping areas of adjacent blocks, a weighted average or sliding window method is used to achieve a smooth data transition. The residual distribution of the stitched data and the original data is checked to ensure that no systematic bias is introduced during stitching. Where there are significant differences in data quality or uneven noise distribution in overlapping areas, a weighted average method is used, which reduces abrupt changes at the stitching point by assigning different weight coefficients to different datasets. If it is necessary to highlight the local characteristics of the overlapping area data, a sliding window method is used. By setting the window length and sliding step, statistical analysis and overlay are performed on the data within the window to preserve local data characteristics.

[0130] S800: The Occam optimal algorithm is used to perform joint inversion on the stitched data to obtain the stitched depth domain resistivity data volume model. The depth domain resistivity data volume is analyzed to avoid overcorrection and ensure that the stitching results match the known geological structures. The process of analyzing the depth domain resistivity data volume is as follows:

[0131] S810. The "geological capping method" is adopted, which tracks the transverse variation trend of the electrical layer along the profile based on the geoelectric correspondence and the surface geology and well logging data to calibrate the depth domain resistivity data volume.

[0132] S820. If gravity and magnetic exploration data exist in adjacent blocks, extract the depth domain resistivity data volume at the corresponding location of the gravity and magnetic survey line to verify whether the regional characteristics such as structural zoning and basement undulation match.

[0133] S830. If seismic exploration data exists in adjacent blocks, extract the resistivity profile at the corresponding location of the seismic survey line and compare it with the seismic reflection interface to verify whether the vertical resolution matches.

[0134] S840. If the resistivity variation characteristics do not match the regional characteristics reflected by gravity and magnetic fields, and deviate from the seismic interface, the correction parameters need to be re-evaluated.

[0135] like Figure 2 As shown, the magnetotelluric sounding data stitching and processing system of the present invention includes a data acquisition unit, a data quality inspection and evaluation unit, a data standardization unit, a data preprocessing unit, a data normalization unit, a data stitching unit, and a data inversion unit.

[0136] This data acquisition unit is used to acquire magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent blocks.

[0137] This data quality inspection and evaluation unit is used to inspect, evaluate, and classify magnetotelluric sounding data, and remove invalid data.

[0138] This data standardization unit is used to correct magnetotelluric sounding data to the same coordinate system, unify the frequency range of measurement points, align frequency points, uniformly convert data to structural strike or dip directions, unify data format, ensure that all data files are in the same format, optimize data quality, and improve the consistency of data in adjacent blocks.

[0139] This data preprocessing unit is used to perform preprocessing on magnetotelluric sounding data, including denoising and filtering, polarization pattern recognition, terrain correction, and static displacement correction, to ensure data quality and avoid error accumulation after stitching.

[0140] This data normalization unit is used to eliminate non-geological signal interference caused by factors such as instrument differences, environmental noise, and different observation times, so that data from different blocks have consistency and comparability when splicing or jointly inverting.

[0141] This data stitching unit is used to stitch together magnetotelluric sounding data and check the residual distribution of the data before and after stitching to ensure that no systematic bias is introduced during stitching.

[0142] This data inversion unit is used to perform joint inversion on the stitched data to obtain a stitched depth domain resistivity data volume model, and to analyze the depth domain resistivity data volume to avoid overcorrection.

[0143] Furthermore, the data quality inspection and evaluation unit includes a quality inspection module and a quality evaluation module; wherein, the data quality inspection module is used to inspect the quality of magnetotelluric sounding data; and the data quality evaluation module is used to classify the quality of magnetotelluric sounding data and remove invalid data.

[0144] The data standardization unit includes a measurement point coordinate unification module, a measurement point frequency range and frequency alignment module, a data conversion to a specific direction module, and a data format unification module. Specifically, the measurement point coordinate unification module corrects the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system; the measurement point frequency range and frequency alignment module checks the frequency range and sampling interval of magnetotelluric sounding data from adjacent blocks, unifying the measurement point frequency range and aligning the measurement point frequencies; the data conversion to a specific direction module converts magnetotelluric sounding data to a specific direction (such as structural strike or dip), optimizing data quality and eliminating impedance tensor deviations caused by differences in measurement azimuth; and the data format unification module converts data collected by different magnetotelluric sounding instruments into a unified EDI standard data format, ensuring the consistency of key magnetotelluric sounding data parameters such as apparent resistivity, impedance phase, and impedance tensor.

[0145] The data preprocessing unit includes a denoising and filtering module, a polarization pattern recognition module, a terrain correction module, and a static displacement correction module. The denoising and filtering module uses median filtering, average value filtering, and Gaussian filtering methods to denoise the magnetotelluric sounding data, eliminating high-frequency noise and low-frequency interference. The polarization pattern recognition module identifies polarization patterns in the magnetotelluric sounding data, identifying and correcting abnormal data caused by instrument errors or environmental interference, thus improving the overall data reliability. The terrain correction module uses finite element or finite difference methods to simulate the influence of terrain, eliminating electromagnetic field distortion caused by terrain. The static displacement correction module obtains the position of the first branch of the apparent resistivity curve, translates the entire apparent resistivity curve to ensure the first branch of the measuring point curve is in the correct position, corrects measuring points exhibiting static displacement, and eliminates the static displacement influence caused by near-surface electrical inhomogeneities.

[0146] The data normalization unit includes an apparent resistivity correction module and an impedance phase correction module. The apparent resistivity correction module calculates the ratio of the average apparent resistivity at common measurement points and performs ratio correction; the impedance phase correction module calculates the impedance phase difference at common measurement points and performs phase difference correction.

[0147] The data stitching unit includes a data stitching module and a data checking module. The data stitching module is used to stitch data in overlapping areas of adjacent blocks using a weighted average or sliding window method to achieve a smooth data transition. The data checking module is used to check the residual distribution of the data before and after stitching to ensure that no systematic bias is introduced during stitching.

[0148] The following is an application example of the magnetotelluric sounding data stitching and processing method and system proposed in this invention in a western oilfield in China. Block A is located northwest of Block B. The magnetotelluric sounding data for Block A was acquired in 2000 using the MT-1 magnetotelluric system manufactured by EMI Corporation of the United States. The magnetotelluric sounding data for Block B was acquired in 2009 using the V5-2000 series magnetotelluric system manufactured by Phoenix Technologies of Canada. Figure 3 The resistivity inversion profile is a direct splicing of blocks A and B, as shown below. Figure 3 As shown, there is a significant difference in resistivity between block A and block B in the overlapping area, and the splicing marks are obvious. Figure 4 The resistivity inversion profile of the spliced ​​block A and block B according to the present invention shows a continuous change in resistivity in the overlapping area, a natural connection at the splice point without obvious splicing marks, and better agreement with known faults and lithological interfaces. The specific implementation steps are as follows:

[0149] Step 1): Obtain magnetotelluric sounding data and regional rock electrical parameter data corresponding to the measuring points in Block A and Block B.

[0150] Step 2): Perform a quality check on the magnetotelluric sounding data of Block A and Block B to check for outliers or data acquisition errors.

[0151] Step 3): Based on the inspection results, classify and evaluate the quality of the magnetotelluric sounding data of Block A and Block B, and remove invalid data.

[0152] Step 4): Convert the Beijing 54 coordinates of Block A and the WGS-84 coordinates of Block B to the 2000 national geodetic coordinates.

[0153] Step 5): The frequency range of the magnetotelluric sounding data in Block A and Block B is the same, but the sampling interval is different. Taking the sampling interval of Block A as the standard, the Kriging method is used to interpolate and align the frequency points of Block B.

[0154] Step 6): Rotate the magnetotelluric sounding data of Block A and Block B to the structural strike.

[0155] According to the present invention, step 7) is performed: the magnetotelluric sounding data of block A and block B are converted into EDI standard data format.

[0156] Step 8): Perform noise reduction filtering on the magnetotelluric sounding data of Block A and Block B to reduce high-frequency noise and low-frequency interference, correct the influence of instrument errors and environmental factors on the data, enhance the effective signal, and improve the signal-to-noise ratio and reliability of the data.

[0157] Step 9): Perform eigenvalue decomposition on the impedance tensor of the magnetotelluric sounding data of Block A and Block B to determine the shape and direction of the polarization ellipse. Perform polarization pattern recognition on the magnetotelluric sounding data to identify and correct abnormal data caused by instrument errors or environmental interference, thereby improving the overall data reliability.

[0158] Step 10): Use the finite element method to simulate the terrain effects of blocks A and B, and eliminate the electromagnetic field distortion caused by the terrain.

[0159] Step 11): Based on the measurement results of the rock electrical parameters of Block A and Block B, determine the resistivity of the surface or shallow strata lithology at the location of the magnetotelluric sounding observation point, obtain the correct position of the first branch of the apparent resistivity curve, correct the measurement points that show static displacement, and eliminate the influence of static displacement caused by near-surface electrical inhomogeneities.

[0160] Step 12): Calculate the ratio κ(f) of the average apparent resistivity at the common measuring point at the boundary between blocks A and B. Then, calculate the ρ(f) of all measuring points in block A. A (f) is corrected by multiplying by κ(f).

[0161] Step 13): Calculate the average impedance phase difference Δθ(f) at the common point of intersection of block A and block B, add Δθ(f) to the impedance phase of all measuring points in block A and correct it by taking the modulus of 360°.

[0162] Step 14): The data of block A and block B are spliced ​​together, and a weighted average is used to achieve a smooth data transition for the overlapping areas.

[0163] Step 15): Check the residual distribution between the spliced ​​data and the original data to ensure that the splicing did not introduce systematic bias.

[0164] Step 16): Use the Occam optimal algorithm to perform joint inversion on the stitched data to obtain the stitched depth domain resistivity data volume model, analyze whether the stitching process is reasonable, and avoid over-correction.

[0165] This application example demonstrates the secondary concatenation and stitching of magnetotelluric sounding data from adjacent blocks in a western oilfield in China. This process eliminates non-geological signal interference caused by factors such as instrument differences, environmental noise, and varying observation times, optimizes data quality, and improves the consistency and quality of data from adjacent blocks. It provides strong support for the comprehensive interpretation and study of the geological structure of the entire region, and this method has significant application and promotion value.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for stitching and processing magnetotelluric sounding data, characterized in that, Includes the following steps: (1) Obtain magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent electromagnetic exploration blocks; (2) Conduct quality checks on the magnetotelluric sounding data and analyze whether there are any outliers or data acquisition errors; (3) Classify and evaluate the quality of magnetotelluric sounding data based on the quality inspection results, and remove invalid data; (4) Standardize the magnetotelluric sounding data to improve data consistency; (5) Preprocess the magnetotelluric sounding data to ensure data quality and reduce the impact of noise and anomalies; specifically, this includes the following steps: (5.1) Noise filtering is performed on the magnetotelluric sounding data; (5.2) Polarization pattern identification of magnetotelluric sounding data; (5.3) Use the finite element method or finite difference method to simulate the influence of terrain and eliminate the electromagnetic field distortion caused by terrain; (5.4) Determine the resistivity of the surface or shallow strata lithology at the location of the magnetotelluric sounding point based on the measurement results of the regional rock electrical parameters, obtain the correct position of the first branch of the apparent resistivity curve, translate the entire apparent resistivity curve so that the first branch of the measuring point curve reaches the correct position, correct the measuring points that have static displacement, and eliminate the influence of static displacement caused by near-surface electrical inhomogeneities. (6) Normalize the magnetotelluric sounding data to eliminate non-geological signal interference; specifically, this includes the following steps: (6.1) For each electromagnetic wave frequency f, calculate the ratio of the average apparent resistivity of the common measuring points at the boundary between adjacent electromagnetic exploration blocks A and B: ； All measurement points in block A Multiply Perform correction; (6.2) For each frequency f, calculate the average impedance phase difference at the common point of intersection of adjacent blocks A and B: ； Impedance phase addition at all measuring points in block A And a 360° model is taken for calibration; (7) The normalized magnetotelluric sounding data are spliced ​​together. The data of the overlapping areas of adjacent blocks are smoothed by weighted average or sliding window method. The residual distribution of the spliced ​​data and the original data is checked to ensure that the splicing does not introduce systematic bias. (8) The Occam optimal algorithm is used to perform joint inversion on the stitched data to obtain the stitched depth domain resistivity data volume model. The depth domain resistivity data volume is analyzed to avoid over-correction and ensure that the stitching results are consistent with the known geological structures.

2. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that, Step (2) specifically includes the following steps: (2.1) Check the apparent resistivity and phase curves of the magnetotelluric sounding data to confirm whether the curves are continuous and smooth, and whether there are any abrupt frequency jumps. (2.2) The data confidence of magnetotelluric sounding data is evaluated by the error ellipse of the impedance tensor. The smaller the ellipse, the more reliable the data. (2.3) Check the coherence of the electric and magnetic field components in the magnetotelluric sounding data of the electromagnetic exploration block, analyze the correlation of electromagnetic signals, and the value is less than 0.8, which is low coherence.

3. The magnetotelluric sounding data stitching and processing method as described in claim 2, characterized in that, The specific process of analyzing the correlation of electromagnetic signals in step (2.3) is as follows: (2.3.1) Perform spectral transformation on the original time series data of magnetotelluric sounding to extract the spectral characteristics of each component of the electric and magnetic fields; (2.3.2) Calculate the self-power spectrum and cross-power spectrum of each component of the electric and magnetic fields; (2.3.3) The coherence between the electric and magnetic field components is calculated based on the self-power spectrum and cross-power spectrum. The coherence range is between 0 and 1. The closer it is to 1, the higher the signal correlation and the lower the noise interference. The formula is: Coherence = .

4. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that, The evaluation classification in step (3) includes high-quality data, usable data, and invalid data; The high-quality data refers to data with a high signal-to-noise ratio, smooth curves, small error ellipses, and a coherence greater than 0.9; The available data represents data in some frequency bands that contain noise but can be used after processing. The invalid data refers to data with severe noise pollution or missing frequency points.

5. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that, Step (4) specifically includes the following steps: (4.1) Correct the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system to ensure that all points use the same coordinate system; (4.2) Interpolate the magnetotelluric sounding data to unify the frequency range of the measuring points and align the frequency points of the measuring points; (4.3) Convert the magnetotelluric sounding data to a specific direction to optimize data quality and eliminate impedance tensor deviation caused by differences in measurement orientation; (4.4) Convert the magnetotelluric sounding data collected by different magnetotelluric sounding instruments into a unified EDI standard data format.

6. The magnetotelluric sounding data stitching and processing method as described in claim 5, characterized in that, The specific process of step (4.2) is as follows: check the frequency range and sampling interval of the magnetotelluric sounding data of adjacent blocks, check whether the effective frequency bands of different measuring points overlap, interpolate the missing frequency bands, align the frequency points, and ensure that the frequency range is consistent and the spliced ​​data is aligned on the same frequency point.

7. The magnetotelluric sounding data stitching and processing method as described in claim 5, characterized in that, The specific process of step (4.3) is as follows: Based on the quality of the magnetotelluric sounding data, the phase tensor method is selected to convert the magnetotelluric sounding data to the tectonic direction, optimize the data quality, and eliminate the impedance tensor deviation caused by the difference in measurement azimuth; the specific steps are as follows: (4.3.1) Calculate the magnetotelluric impedance tensor Z=E / H, which is the ratio of electric field strength E to magnetic field strength H; (4.3.2) Rotate the impedance tensor Z to the construction direction to obtain the rotated impedance tensor Z': Z´= ； in, For rotation angle, For rotation matrix, .

8. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that: Specifically, step (5.1) involves using median filtering to eliminate instrument transient noise in magnetotelluric sounding data, suppressing spike noise, using average value filtering to suppress high-frequency random noise in magnetotelluric sounding data, using Gaussian filtering to suppress broadband noise in magnetotelluric sounding data, smoothing the spectral curve, enhancing the effective signal, and improving the signal-to-noise ratio and reliability of the data.

9. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that: Specifically, step (5.2) involves decomposing the impedance tensor into eigenvalues, obtaining two eigenvalues ​​and their corresponding eigenvectors, determining the shape and direction of the polarization ellipse, performing polarization pattern recognition on the magnetotelluric sounding data, identifying and correcting abnormal data caused by instrument errors or environmental interference, and improving the overall data reliability.

10. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that, The specific process for correcting the measuring points exhibiting static displacement in step (5.4) is as follows: (5.4.1) Based on the measurement results of regional rock electrical parameters, calculate the average resistivity of each lithology in each stratum. Calculation formula: ; In the above formula, n is the number of specimen blocks. Let be the resistivity value of the i-th specimen; (5.4.2) The resistivity average value of each lithology in each stratum is weighted by thickness to obtain the resistivity value of each stratum. Calculation formula: ; In the above formula, n represents the number of lithologies in each stratigraphic unit. Let the thickness of the nth lithology be within each stratum. The average resistivity of the nth lithology; (5.4.3) Statistically analyze the distribution of magnetotelluric sounding points in different exposed strata of adjacent blocks, based on the resistivity values ​​of the exposed strata. Determine the correct location of the first branch of the apparent resistivity curve at each magnetotelluric sounding point; (5.4.4) Without changing the shape of the apparent resistivity curve of the magnetotelluric sounding point, the second frequency point of the apparent resistivity curve of the magnetotelluric sounding point in different exposed strata areas is shifted to the correct position of the first branch, and other frequency points are shifted as a whole based on the second frequency point. (5.4.5) Based on this standard, the measuring points that exhibit static displacement are corrected to eliminate the influence of static displacement caused by near-surface electrical inhomogeneities.

11. The magnetotelluric sounding data stitching and processing method as described in claim 1, characterized in that, The specific process of analyzing the depth domain resistivity data volume in step (8) is as follows: (8.1) The "geological capping method" is adopted to track the transverse variation trend of the electrical layer along the profile based on the geoelectric correspondence and the surface geology and well logging data. The depth domain resistivity data volume is calibrated based on the surface geology and well logging data. (8.2) If gravity and magnetic exploration data exist in adjacent blocks, extract the depth domain resistivity data volume at the corresponding location of the gravity and magnetic survey line to verify whether the regional characteristics match. (8.3) If there is seismic exploration data in adjacent blocks, extract the resistivity profile at the corresponding location of the seismic survey line and compare it with the seismic reflection interface to verify whether the vertical resolution matches. (8.4) If the resistivity variation characteristics do not match the regional characteristics reflected by gravity and magnetic fields and deviate from the seismic interface, the correction parameters need to be re-evaluated.

12. A magnetotelluric sounding profile data stitching and processing system, based on the magnetotelluric sounding data stitching and processing method according to any one of claims 1-11; characterized in that: The processing system includes a data acquisition unit, a data quality inspection and evaluation unit, a data standardization unit, a data preprocessing unit, a data normalization unit, a data splicing unit, and a data inversion unit; The data acquisition unit is used to acquire magnetotelluric sounding data and regional rock electrical parameter data corresponding to different measuring points in adjacent blocks. The data quality inspection and evaluation unit is used to inspect, evaluate and classify the magnetotelluric sounding data, and remove invalid data. The data standardization unit is used to correct magnetotelluric sounding data to the same coordinate system, unify the frequency range of measuring points, align frequency points, convert data to a specific direction, unify the data format, ensure that all data files are in the same format, and optimize data quality. The data preprocessing unit is used to preprocess the magnetotelluric sounding data to ensure data quality and avoid error accumulation after splicing. The data normalization unit is used to eliminate non-geological signal interference caused by adverse factors, so that the data from different blocks have consistency and comparability when splicing or jointly inverting. The data stitching unit is used to stitch together magnetotelluric sounding data, and to achieve smooth data transition by using weighted average or sliding window methods for data in overlapping areas of adjacent blocks. It also checks the residual distribution of data before and after stitching to ensure that no systematic bias is introduced during stitching. The data inversion unit uses the Occam optimal algorithm to perform joint inversion on the stitched data to obtain a stitched depth domain resistivity data volume model; the depth domain resistivity data volume is analyzed to avoid over-correction and ensure that the stitching results are consistent with known geological structures.

13. The magnetotelluric sounding profile data stitching and processing system as described in claim 12, characterized in that: The data quality inspection and evaluation unit includes a quality inspection module and a quality evaluation module; the quality inspection module is used to inspect the quality of magnetotelluric sounding data; the quality evaluation module is used to evaluate and classify the magnetotelluric sounding data and remove invalid data.

14. The magnetotelluric sounding profile data stitching and processing system as described in claim 12, characterized in that: The data standardization unit includes a measurement point coordinate unification module, a measurement point frequency range and frequency alignment module, a data conversion to a specific direction module, and a data format unification module. The unified coordinate module for measuring points is used to correct the coordinates of magnetotelluric sounding points in adjacent blocks to the same coordinate system. The unified measurement point frequency range and frequency point alignment module is used to check the frequency range and sampling interval of magnetotelluric sounding data in adjacent blocks, unify the measurement point frequency range, and align the measurement point frequencies. The data conversion to a specific direction module is used to convert magnetotelluric sounding data to a specific direction, optimize data quality, and eliminate impedance tensor deviation caused by differences in measurement orientation. The data format unification module is used to convert data collected by different magnetotelluric sounding instruments into a unified EDI standard data format, ensuring the consistency of the main magnetotelluric sounding data parameters, namely apparent resistivity, impedance phase, and impedance tensor.

15. The magnetotelluric sounding profile data stitching and processing system as described in claim 12, characterized in that: The data preprocessing unit includes a noise reduction and filtering module, a polarization pattern recognition module, a terrain correction module, and a static displacement correction module. The denoising and filtering module uses median filtering, average filtering and Gaussian filtering methods to denoise and filter the magnetotelluric sounding data, eliminating high-frequency noise and low-frequency interference. The polarization pattern recognition module is used to perform polarization pattern recognition on magnetotelluric sounding data, identify and correct abnormal data caused by instrument errors or environmental interference, and improve the overall data reliability. The terrain correction module uses the finite element or finite difference method to simulate the effects of terrain and eliminate electromagnetic field distortion caused by terrain. The static displacement correction module is used to obtain the position of the first branch of the apparent resistivity curve, translate the entire apparent resistivity curve so that the first branch of the measuring point curve reaches the correct position, correct the measuring points that have static displacement, and eliminate the static displacement effect caused by near-surface electrical inhomogeneities.

16. The magnetotelluric sounding profile data stitching and processing system as described in claim 12, characterized in that: The data normalization unit includes an apparent resistivity correction module and an impedance phase correction module; The apparent resistivity correction module is used to calculate the ratio of the average apparent resistivity of common measuring points and perform ratio correction. The impedance phase correction module is used to calculate the impedance phase difference at the common measurement point and perform phase difference correction.

17. The magnetotelluric sounding profile data stitching and processing system as described in claim 12, characterized in that: The data splicing unit includes a data splicing module and a data inspection module; The data stitching module is used to stitch data in overlapping areas of adjacent blocks using a weighted average or sliding window method to achieve a smooth data transition. The data inspection module is used to check the residual distribution of the data before and after splicing to ensure that no systematic bias is introduced during splicing.

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