Magnetotelluric data imaging processing method based on correlation reflection coefficient

Abstract

The present invention discloses a method for imaging and processing magnetotelluric data based on correlation reflection coefficients, comprising: acquiring magnetotelluric data to be processed from a plurality of measuring points; preprocessing the magnetotelluric data; obtaining an initial reflection coefficient K1 of the magnetotelluric data; correcting the initial reflection coefficient K1 based on the positive or negative value of the initial reflection coefficient K1 to obtain a second reflection coefficient K2; correcting the second reflection coefficient K2 based on the correlation between adjacent measuring points of the magnetotelluric data to obtain a third reflection coefficient K3; and correcting the resistivity R1 inverted from the magnetotelluric data based on the value of the third reflection coefficient K3 to obtain the resistivity R2, and then imaging. The present invention has the advantages of simple logic and strong anti-interference capability, and has high practical value and promotional value in the field of magnetotelluric data processing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetotelluric data processing, in particular to a magnetotelluric data imaging processing method based on correlation reflection coefficient. Background Art

[0002] Among geophysical exploration methods, magnetotellurics (MT) is one of the most widely used, playing a crucial role in mineral resource exploration, deep structural detection, hydrogeological surveys, and engineering geological surveys. The basic principle of this method is that, based on the fact that electromagnetic waves of different frequencies propagate through a medium with different skin depths, the method measures the Earth's electromagnetic response sequence from high to low frequencies at the surface. Through relevant data processing and analysis, the electrical structure of the Earth, from shallow to deep, is obtained. Although the data processing and interpretation technology for this method is very mature, because it observes natural electromagnetic field sources and the signals are unstable and irregular, they are susceptible to interference from industrial noise. In some areas with strong interference, data processing results are still unsatisfactory, and data interpretation is difficult. Therefore, practitioners and scholars have been committed to researching MT data processing technology to improve the accuracy and resolution of MT exploration.

[0003] Sun Jingrong's article "A Brief Introduction to Reflection Coefficient Electrical Exploration (K Profile)" proposes the K-coefficient method, also known as the reflection coefficient method. This method was originally designed for direct current (DC) sounding (magnetotellurics is a frequency electromagnetic sounding method). By calculating the logarithmic ratio of the apparent resistivity measured at different pole spacings and the logarithmic ratio of the pole spacing, a reflection coefficient profile is obtained. The reflection coefficient is then corrected and superimposed on the apparent resistivity to produce a pseudo-true resistivity profile. The K-coefficient method has numerous applications in the industry, initially limited to DC sounding (such as the high-density method). Later, its application was expanded to transient electromagnetic (TEM) methods, achieving promising results and culminating in numerous publications. However, the current K-coefficient method also has some limitations, such as overly simplistic theoretical formulas and the fact that the calculated parameters are simply conversions of apparent resistivity. In some cases, it still fails to achieve ideal results.

[0004] In the Chinese patent application, published with publication number CN114114436A and titled "Method for Fusion of Refraction and Reflection Images for Coal and Rock Exploration Using Radio-Electromagnetic Waves in Mine," the phase attenuation coefficient and amplitude attenuation coefficient of electromagnetic waves propagating in a lossy medium are first solved based on Maxwell's equations. The reflection and refraction coefficients of a three-layer coal and rock model are then calculated. The three-layer coal and rock model is then solved based on the reflection and refraction coefficients. The reflected and refracted wave intensities at the interface between the second and third layers are then calculated based on the phase and amplitude attenuation coefficients. Reflected and refracted wave images are then obtained based on the reflected and refracted wave intensities, respectively. The reflected and refracted images are then grayscaled, denoised, and fused to form a fused image. This invention improves the real-time performance of coal and rock image recognition by improving the original transient electromagnetic emission imaging and construction methods.

[0005] The above techniques demonstrate that existing technologies primarily target transient electromagnetic data within mines. Furthermore, the reflection coefficient described in this patent is calculated in a completely different manner from the K-coefficient method. Currently, the K-coefficient method is widely used in both DC and transient electromagnetic methods. Because these exploration methods are active field methods with strong signals and high signal-to-noise ratios, they are not widely used in magnetotelluric methods, which are passive field methods with weaker signals and are susceptible to interference.

[0006] Therefore, there is an urgent need to propose a magnetotelluric data imaging processing method based on correlation reflection coefficients that has simple logic and strong anti-interference ability. Summary of the Invention

[0007] In view of the above problems, the present invention aims to provide a method for processing magnetotelluric data imaging based on correlation reflection coefficients. The technical solutions adopted by the present invention are as follows:

[0008] The method for processing magnetotelluric data imaging based on correlation reflection coefficient comprises the following steps:

[0009] Obtain the magnetotelluric data to be processed at several measuring points, and mark the inverted resistivity in the magnetotelluric data as R1;

[0010] Preprocessing the magnetotelluric data; the preprocessing includes ground uniform gridding and interpolation processing;

[0011] Obtain the initial reflection coefficient K1 of the preprocessed magnetotelluric data;

[0012] According to the positive or negative value of the initial reflection coefficient K1, the value of the initial reflection coefficient K1 is corrected to obtain a second reflection coefficient K2;

[0013] According to the correlation between adjacent measurement points of the magnetotelluric data, the value of the second reflection coefficient K2 is corrected to obtain the third reflection coefficient K3;

[0014] The resistivity R1 inverted from the magnetotelluric data is corrected according to the value of the third reflection coefficient K3 to obtain the resistivity R2; and the resistivity R2 is imaged.

[0015] Furthermore, magnetotelluric data to be processed of several measuring points are obtained, and an inversion dimension is selected; the magnetotelluric data are used as inversion data; and the inversion data include depth values ​​and resistivity values.

[0016] Furthermore, the initial reflection coefficient K1 of the preprocessed magnetotelluric data is obtained, which is expressed as:

[0017]

[0018] in, Indicates the inversion depth value of the nth layer; Indicates the inversion depth value of the n+1th layer; Indicates the resistivity value corresponding to the depth of the nth layer; Indicates the resistivity value corresponding to the depth of the n+1th layer; Indicates the initial reflection coefficient corresponding to the depth of the nth layer.

[0019] Furthermore, according to the positive or negative value of the initial reflection coefficient K1, the value of the initial reflection coefficient K1 is corrected to obtain a second reflection coefficient K2, which includes:

[0020] If the initial reflection coefficient is greater than or equal to 0, then K2(n)=K1(n); K2(n) represents the second reflection coefficient corresponding to the depth of n layers;

[0021] If the initial reflection coefficient is less than 0, then .

[0022] Furthermore, according to the correlation between adjacent measurement points of the magnetotelluric data, the value of the second reflection coefficient K2 is corrected to obtain the third reflection coefficient K3, including:

[0023] Obtain the second reflection coefficient K2 of all measuring points at the same depth and calculate the standard deviation Std1;

[0024] Arrange all measuring points at the same depth according to their positional relationship, select a measuring point window with a length of A, and calculate the standard deviation Std2 corresponding to the second reflection coefficient K2 of the measuring points within the measuring point window; A is a natural number greater than 0;

[0025] If the standard deviation Std2 is greater than the standard deviation Std1, the average value of the second reflection coefficients K2 of the measuring points in the measuring point window is obtained, and the average value is used as the second reflection coefficient K2 of the central measuring point in the measuring point window;

[0026] If the standard deviation Std2 is less than the standard deviation Std1, the second reflection coefficient K2 of the central measuring point in the measuring point window remains unchanged;

[0027] The third reflection coefficient K3 is obtained.

[0028] Furthermore, the resistivity R1 inverted from the magnetotelluric data is corrected according to the value of the third reflection coefficient K3 to obtain the resistivity R2; the resistivity R2 is imaged; the expression of the resistivity R2 is:

[0029]

[0030] in, represents the resistivity corresponding to the depth of the nth layer; represents the resistivity corresponding to the depth of the n-1th layer; Indicates the third reflection coefficient corresponding to the nth layer depth.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] The present invention proposes a correlation reflection coefficient method and introduces it into the magnetotelluric data imaging processing, making it suitable for the magnetotelluric method and able to effectively improve the magnetotelluric data processing accuracy. The obtained result parameters can increase the diversity of magnetotelluric data interpretation parameters.

[0033] The correlation reflection coefficient method proposed in the present invention first converts its calculation formula, then adds correlation calculation in the present invention, and finally processes the parameters so that they have continuity at positions with high correlation and retain their original values ​​at positions with low correlation. This not only ensures the authenticity of the calculated parameters, but also improves the stratification of the calculated parameters, and completely solves the technical problem of susceptibility to interference in the magnetotelluric method.

[0034] In summary, the present invention has the advantages of simple logic and strong anti-interference ability, and has high practical value and promotion value in the field of magnetotelluric data processing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope of protection. For those skilled in the art, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 It is a logic flow chart of the present invention.

[0037] Figure 2 This is a real model diagram of the present invention.

[0038] Figure 3 This is a diagram showing the resistivity R1 result of the inversion of the present invention.

[0039] Figure 4 This is a result diagram of the second reflection coefficient K2 of the present invention.

[0040] Figure 5 This is a result diagram of the third reflection coefficient K3 of the present invention.

[0041] Figure 6 The resistivity R2 result diagram of the present invention is shown in FIG. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions, and advantages of this application more clear, the present invention is further described below with reference to the accompanying drawings and examples. Implementation methods of the present invention include, but are not limited to, the following examples. All other embodiments obtained by persons of ordinary skill in the art based on the examples in this application without creative effort are within the scope of protection of this application.

[0043] In this embodiment, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0044] In the description and claims of this embodiment, the terms "first" and "second" are used to distinguish different objects rather than to describe a specific order of objects. For example, a first target object and a second target object are used to distinguish different objects rather than to describe a specific order of objects.

[0045] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0046] In the description of the embodiments of this application, unless otherwise specified, "multiple" means two or more. For example, "multiple processing units" means two or more processing units; "multiple systems" means two or more systems.

[0047] like Figures 1 to 6 As shown, this embodiment provides a magnetotelluric data imaging processing method based on correlation reflection coefficients, which corrects the calculation parameters according to the correlation between adjacent magnetotelluric measurement points. At the same time, it does not blindly pursue continuity, but rather compromises between continuity and authenticity, so that it can meet the interpretation needs to the greatest extent.

[0048] The inversion data of this embodiment can be either inversion data generated by a theoretical model or measured data. In this embodiment, theoretical model data is used for further explanation, so the process of generating theoretical data is explained here. If measured data is used, it can be directly processed using the present invention after inversion processing.

[0049] First, a geological model is established. The geological model is a four-layered geological model, such as Figure 2 As shown, the profile is 10 km long and 5 km deep. The resistivities from top to bottom are 10Ω, 1000Ω, 200Ω, and 5000Ω, respectively, exhibiting an overall low-high-low-high resistivity pattern. This example performs forward modeling on this geological model. The specific forward modeling parameters are not detailed here. After obtaining the forward modeling data, the forward modeling data is then inverted.

[0050] The first step is to obtain the magnetotelluric data to be processed from several measurement points, and mark the inverted resistivity in the magnetotelluric data as R1. Then, an inversion dimension is selected, and the magnetotelluric data is used as the inversion data; the inversion data includes depth values ​​and resistivity values.

[0051] Inversion can be performed in one, two, or even three dimensions. The inversion result is typically three columns of data, including measurement point mileage, depth, and resistivity. Note that the inversion data size is M×N, where M is the number of layers being inverted and N is the number of measurement points.

[0052] In this embodiment, the NLCG two-dimensional inversion method is used to invert it, and the inversion results are as follows: Figure 3 As shown in Figure 2, it can be seen that due to the volume effect, the inversion results do not reflect the low-high-low-high resistivity four-layer characteristics, and the overall resolution is not high.

[0053] The second step is to preprocess the magnetotelluric data, that is, to further uniformly grid and interpolate the inversion data (depth value and resistivity value) of each measuring point.

[0054] Specifically, because the data grid in the first inversion step is not uniform, the grid size is typically smaller in shallow areas and increases with depth. If the subsequent calculations are performed according to the inverted data grid, the results at depth may be poor. Therefore, the inverted depth data needs to be re-gridded. In this implementation, a depth grid size of 10m is selected based on actual conditions.

[0055] Furthermore, after re-gridding, the resistivity data of the new depth grid nodes must be interpolated to match them. In this implementation, the spline curve interpolation method is used to interpolate the resistivity.

[0056] The third step is to obtain the initial reflection coefficient K1 of the preprocessed magnetotelluric data, which is expressed as: in, Indicates the inversion depth value of the nth layer; Indicates the inversion depth value of the n+1th layer; Indicates the resistivity value corresponding to the depth of the nth layer; Indicates the resistivity value corresponding to the depth of the n+1th layer; Indicates the initial reflection coefficient corresponding to the depth of the nth layer.

[0057] The fourth step is to correct the value of the initial reflection coefficient K1 according to its positive or negative value to obtain the second reflection coefficient K2. Specifically:

[0058] If the initial reflection coefficient is greater than or equal to 0, then K2(n)=K1(n); K2(n) represents the second reflection coefficient corresponding to the depth of n layers;

[0059] If the initial reflection coefficient is less than 0, then .

[0060] At the same time, it can be seen from the calculation formula that the second reflection coefficient K2 cannot be calculated at the last depth. Therefore, the K2 value calculated at the second to last depth is used as the K2 value of the last depth. Here, the K2 value of each measuring point at each depth value is calculated, such as Figure 4 As shown in the figure, there are three obvious interfaces (dashed lines), but the continuity is poor at deeper positions.

[0061] In the fifth step, the second reflection coefficient, K2, is corrected based on the correlation between adjacent measurement points in the magnetotelluric data to obtain the third reflection coefficient, K3. Specifically, the original K coefficient method, for electrical depth sounding with smaller measurement point spacing, calculates a separate K value for each electrical depth curve to avoid lateral influences. However, for magnetotelluric methods with larger measurement point spacing, lateral continuity is more desirable to better delineate horizons. Therefore, K2 needs to be further processed based on the correlation between adjacent measurement points.

[0062] In this embodiment, correction processing is performed according to the value of the second reflection coefficient K2 to obtain the third reflection coefficient K3, which includes:

[0063] Obtain the second reflection coefficient K2 of all measuring points at the same depth and calculate the standard deviation Std1.

[0064] All measuring points at the same depth are arranged according to their positional relationship, and a measuring point window with a length of A is selected, and the standard deviation Std2 corresponding to the second reflection coefficient K2 of the measuring points within the measuring point window is obtained; wherein A is a natural number greater than 0.

[0065] If the standard deviation Std2 is greater than the standard deviation Std1, the average value of the second reflection coefficients K2 of the measuring points in the measuring point window is obtained, and the average value is used as the second reflection coefficient K2 of the central measuring point in the measuring point window.

[0066] If the standard deviation Std2 is smaller than the standard deviation Std1, the second reflection coefficient K2 of the central measuring point in the measuring point window remains unchanged.

[0067] The third reflection coefficient K3 is obtained as Figure 5 As shown, after correlation processing, it can be seen that the third reflection coefficient K3 and the second reflection coefficient K2 also have relatively obvious stratification, but are more continuous in deep positions and have better stratification.

[0068] The sixth step is to correct the resistivity R1 inverted from the magnetotelluric data according to the value of the third reflection coefficient K3 to obtain the resistivity R2; and image the resistivity R2. The expression of the resistivity R2 is:

[0069]

[0070] in, represents the resistivity corresponding to the depth of the nth layer; represents the resistivity corresponding to the depth of the n-1th layer; Indicates the third reflection coefficient corresponding to the nth layer depth.

[0071] At the same time, the resistivity R2 calculated by the above formula has false values, which far exceed the true values. Therefore, it is necessary to process the maximum value of the resistivity R2. The specific method is as follows:

[0072] First, the maximum value of the resistivity R2 is determined based on the actual situation. Furthermore, any values ​​exceeding the maximum value in the resistivity R2 are forcibly changed to the maximum value. It is worth noting that despite the further processing of the resistivity R2, R2 is still not the actual resistivity value, but rather a reference value that can better delineate the layers.

[0073] In this embodiment, the resistivity R2 is as follows: Figure 6 As shown, compared with Figure 3 The inverted resistivity R1 and resistivity R2 are more consistent with the real model, reflecting the four-layer characteristics of low-high-low-high resistivity. The layer morphology is also closer to the real model, which fully demonstrates the effectiveness of the method proposed in this invention.

[0074] The above embodiments are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any changes that adopt the design principles of the present invention and any changes made through non-creative work on this basis should fall within the scope of protection of the present invention.

Claims

1. A magnetotelluric data imaging processing method based on correlation reflection coefficient, characterized in that: The following steps are involved: Obtain the magnetotelluric data to be processed at several measuring points, and mark the inverted resistivity in the magnetotelluric data as R1; Preprocessing of magnetotelluric data; The preprocessing includes ground uniform gridding and interpolation processing; Obtain the initial reflection coefficient K1 of the preprocessed magnetotelluric data; According to the positive or negative value of the initial reflection coefficient K1, the value of the initial reflection coefficient K1 is corrected to obtain a second reflection coefficient K2; According to the correlation between adjacent measurement points of the magnetotelluric data, the value of the second reflection coefficient K2 is corrected to obtain the third reflection coefficient K3, which includes: Obtain the second reflection coefficient K2 of all measuring points at the same depth and calculate the standard deviation Std1; Arrange all measuring points at the same depth according to their positional relationship, select a measuring point window with a length of A, and calculate the standard deviation Std2 corresponding to the second reflection coefficient K2 of the measuring points within the measuring point window; A is a natural number greater than 0; If the standard deviation Std2 is greater than the standard deviation Std1, the average value of the second reflection coefficients K2 of the measuring points in the measuring point window is obtained, and the average value is used as the second reflection coefficient K2 of the central measuring point in the measuring point window; If the standard deviation Std2 is less than the standard deviation Std1, the second reflection coefficient K2 of the central measuring point in the measuring point window remains unchanged; Obtain the third reflection coefficient K3; The resistivity R1 inverted from the magnetotelluric data is corrected according to the value of the third reflection coefficient K3 to obtain the resistivity R2; the resistivity R2 is imaged; the expression of the resistivity R2 is: ,in, represents the resistivity corresponding to the depth of the nth layer; represents the resistivity corresponding to the depth of the n-1th layer; represents the third reflection coefficient corresponding to the depth of the nth layer; Indicates the initial reflection coefficient corresponding to the depth of the nth layer.

2. The method for processing magnetotelluric data imaging based on correlation reflection coefficient according to claim 1, characterized in that: The magnetotelluric data to be processed of a plurality of measuring points are obtained, and an inversion dimension is selected; the magnetotelluric data are used as inversion data; and the inversion data include depth values ​​and resistivity values.

3. The method for processing magnetotelluric data imaging based on correlation reflection coefficient according to claim 1, characterized in that: The initial reflection coefficient K1 of the preprocessed magnetotelluric data is obtained, which is expressed as: in, Indicates the inversion depth value of the nth layer; Indicates the inversion depth value of the n+1th layer; Indicates the resistivity value corresponding to the depth of the nth layer; Indicates the resistivity value corresponding to the depth of the n+1th layer.

4. The method for processing magnetotelluric data imaging based on correlation reflection coefficient according to claim 3, characterized in that: According to the positive or negative value of the initial reflection coefficient K1, the value of the initial reflection coefficient K1 is corrected to obtain the second reflection coefficient K2, which includes: If the initial reflection coefficient is greater than or equal to 0, then K2(n)=K1(n); K2(n) represents the second reflection coefficient corresponding to the depth of n layers; If the initial reflection coefficient is less than 0, then .

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