Method and system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting

Abstract

The present invention discloses a method and system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting. The method includes: acquiring a resistivity profile or three-dimensional resistivity slice to form a resistivity data volume; for the resistivity data volume, calculating the gradient of each point using the position and resistivity value of each node to obtain a gradient data volume; normalizing the resistivity data volume and the gradient data volume; calculating the anomaly weighting of each node to obtain an anomaly weighting data volume; performing secondary normalization on the anomaly weighting data volume to obtain the anomaly index of each node and forming an anomaly index data volume; extracting the anomaly index, plotting an anomaly index result graph, and dividing the tunnel anomaly level according to the numerical size. This application improves the accuracy of anomaly interpretation in tunnel electrical prospecting by weighted summing the resistivity value and resistivity gradient after removing anomaly points, forming an anomaly index after secondary normalization, and intuitively reflecting the anomaly degree through the anomaly index value.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel exploration, and particularly to a method and system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting. Background Art

[0002] When using electrical methods for tunnel exploration, currently, the resistivity contour map is still mainly used to visually reflect the resistivity changes in the profile or area. However, years of construction experience have shown that at the locations where the resistivity contours change drastically or the resistivity values are extremely low in tunnel electrical exploration, these are the positions that are closest to the possible locations of groundwater or water-saturated soft rock and soil masses, and geological disasters such as cave-ins, roof falls, water inrushes, and mud inrushes are likely to occur. Therefore, the low-resistivity areas and the areas where the resistivity contours change drastically have become the key concerns of technicians. However, there are two problems that cannot be ignored when using only resistivity result maps for anomaly delineation. One is that due to lithological changes or geological structures, there may be regional low resistivity in the resistivity result map, but it is difficult to determine whether this low-resistivity area is a geological anomaly. The other is that the contour interval in the resistivity result map is usually set by technicians themselves to ensure the aesthetics of the map, without a unified standard. Judging the degree of drastic change of the contours only based on the resistivity result map is highly subjective and does not start from the data itself, which is prone to misjudgment or missed judgment. The root cause of these two problems is that currently, the anomaly division in geophysical tunnel exploration mainly relies on the subjective judgment of technicians' experience, and no quantitative indicators have been established. Therefore, how to comprehensively consider the resistivity value and the gradient change and convert them into objective indicators for delineating geological anomalies has become an urgent issue to be studied and verified. In the existing patent (application number 201510870801.8), wavelet transform is used to detect singular points and superimposed on the resistivity contour map, and its fundamental purpose is to comprehensively use the resistivity value and the resistivity gradient value for anomaly delineation. In the existing patent (application number 202211518847.X), the interpolated resistivity is multiplied by the normalized resistivity gradient to obtain the normalized resistivity value. Both of the above two anomaly delineation methods comprehensively consider the resistivity and the resistivity gradient, but there are also certain defects. The former only shows the two factors separately and does not convert them into a unified indicator; the latter uses multiplication for synthesis and is greatly affected by the absolute value. Therefore, it is necessary to propose a more perfect method for tunnel anomaly interpretation. Summary of the Invention

[0003] Therefore, the purpose of the present invention is to provide a method and system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting. By weighted summing the resistivity value and the resistivity gradient after removing anomaly points and performing secondary normalization to form an anomaly index, the anomaly degree can be intuitively reflected through the anomaly index value, so as to improve the accuracy of anomaly interpretation in tunnel electrical prospecting.

[0004] To achieve the above purpose, a method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting provided by the present invention includes the following steps:

[0005] S1. Obtain a resistivity profile or a three-dimensional resistivity slice to form a resistivity data volume; specifically, it includes steps such as data acquisition and inversion to obtain the resistivity profile or three-dimensional resistivity slice of the work area, and extract the resistivity of each survey line or survey network to form the resistivity data volume ;

[0006] S2. For the resistivity data volume, use the position and resistivity value of each node to calculate the gradient of each point , and obtain a gradient data volume .

[0007] S3. Normalize the resistivity data volume and the gradient data volume to obtain an inverse-normalized resistivity data volume and a normalized gradient data volume ;

[0008] S4. According to the inverse-normalized resistivity data volume and the normalized gradient data volume , calculate the anomaly weighting of each node to obtain an anomaly weighting data volume ;

[0009] S5. Conduct secondary normalization on the anomaly weighting data volume to obtain the anomaly index of each node and form an anomaly index data volume ;

[0010] S6. Extract the anomaly index, draw an anomaly index result map, and classify the tunnel anomaly level according to the numerical size.

[0011] Furthermore, preferably, in S1, the resistivity data volume is a two-dimensional array, including the plane coordinates, elevation, and resistivity value of each node.

[0012] Furthermore, preferably, if a three-dimensional resistivity slice is used to form the resistivity data volume, when calculating the gradient of each point using the position and resistivity value of each node , it includes:

[0013] S201. Respectively obtain the gradients along the survey line, perpendicular to the survey line, and along the depth direction ( , and directions);

[0014] S202. For the data nodes at non-boundary locations , whose three-dimensional coordinates are , calculate the absolute value of its gradient as:

[0015]

[0016] Among them, respectively represent data nodes at the three-dimensional coordinates of the resistivity data value at this point, , , respectively represent data nodes at the three-dimensional coordinates of the resistivity gradient value at this point; , , respectively represent data nodes the coordinates at the next position. Note that , , are for convenient representation. At node it represents the same resistivity value.

[0017] Furthermore, preferably, if a two-dimensional resistivity profile is adopted to form a resistivity data volume, then using the positions and resistivity values of each node to calculate the gradient at each point, it includes: it is necessary to obtain the gradients along the survey line and in the depth direction ( and directions). For the data nodes at non-boundary positions, whose two-dimensional coordinates are , calculate the absolute value of its gradient as:

[0018]

[0019] wherein, , respectively represent data nodes at the coordinates of the resistivity data value at this point, , respectively represent data nodes at the coordinates of the resistivity gradient value at this point; , respectively represent data nodes the coordinates at the next position. Note that , are for convenient representation. At node it represents the same resistivity value.

[0020] Furthermore, preferably, in S3, the normalization of the resistivity data volume and the gradient data volume includes:

[0021] Calculating the mean and standard deviation for the resistivity data volume and the gradient data volume;

[0022] According to the actual data distribution, the values exceeding n times the standard deviation of the mean are removed as extreme values;

[0023] The Min-Max normalization method is used for normalization, making the minimum value of each data body 0 and the maximum value 1.

[0024] Further, preferably, it also includes obtaining an inverse-normalized resistivity data body with opposite weights by using 1 minus each normalized value for the resistivity data body after normalization processing .

[0025] Further, preferably, in S4, for the data node , according to the inverse-normalized resistivity data body and the normalized gradient data body calculate that the abnormal weighted value at this point is

[0026]

[0027] wherein, is the inverse-normalized resistivity data value at the data node , and ; , , are respectively , and the normalized resistivity gradient values in three directions; , and are respectively , and the weights in three directions, and the weight ratio is independently selected according to the exploration target.

[0028] Further, preferably, in S5, the secondary normalization of the abnormal weighted data body includes:

[0029] For the abnormal weighted data body , calculate the mean and variance, remove the data outside the 99% confidence interval, and perform normalization processing on the remaining data using the Min-Max normalization method, making the minimum value of each data body 0 and the maximum value 1; form an abnormal index data body .

[0030] Further, preferably, in S6, for the data node in the abnormal index data body , according to the abnormal index value of the data node perform abnormal level classification:

[0031] When the rock mass belongs to Class V anomaly, corresponding to a complete rock mass, underdeveloped or closed structural planes, and good stability;

[0032] When the rock mass belongs to Class IV anomaly, corresponding to a relatively complete rock mass, relatively developed structural planes, and relatively good stability;

[0033] When the rock mass belongs to Class III anomaly, corresponding to a relatively fractured rock mass, relatively developed structural planes, and general stability;

[0034] When the rock mass belongs to Class II anomaly, corresponding to a fractured rock mass, developed structural planes, and poor stability;

[0035] When the rock mass belongs to Class I anomaly, corresponding to an extremely fractured rock mass, extremely developed structural planes, and poor stability.

[0036] The present invention also provides a system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting, which is used to implement the steps of the method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting, including:

[0037] A data acquisition module, which acquires resistivity profiles or three-dimensional resistivity slices to form a resistivity data volume;

[0038] A gradient calculation module, which calculates the gradient of each point using the position and resistivity value of each node for the resistivity data volume to obtain a gradient data volume ;

[0039] A normalization module, which normalizes the resistivity data volume and the gradient data volume;

[0040] An anomaly weighting module, which calculates the anomaly weighting of each node according to the inverse-normalized resistivity data volume and the normalized gradient data volume to obtain an anomaly weighting data volume ;

[0041] A secondary normalization module, which performs secondary normalization on the anomaly weighting data volume to obtain the anomaly index of each node and form an anomaly index data volume ;

[0042] An interpretation module for graphic results, which extracts the anomaly index, draws an anomaly index result graph, and classifies the tunnel anomaly level according to the numerical size.

[0043] A method and system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting disclosed in the present application has at least the following advantages compared with the prior art:

[0044] In this application, the resistivity data volume and the gradient data volume are normalized after processing to remove abnormal points, and a normalized data volume is obtained. The normalized resistivity data volume and the normalized gradient data volume are weighted and summed, and secondary normalization is carried out to obtain an anomaly index data volume. The anomaly index is extracted, a normalized anomaly result map is drawn, and tunnel anomalies are delineated according to the anomaly division principle. By weighting and summing the resistivity data anomaly and the gradient anomaly, the present invention can digitalize the anomaly index, effectively improving the ability to identify tunnel geological anomalies. The multiple effects of resistivity and resistivity gradient are comprehensively considered, avoiding the absolute influence of a single factor, and the obtained tunnel anomaly interpretation result is more objective and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of a method for improving the anomaly interpretation accuracy of tunnel electrical prospecting provided by the present invention.

[0046] Figure 2 It is a schematic structural diagram of a system for improving the anomaly interpretation accuracy of tunnel electrical prospecting provided by the present invention.

[0047] Figure 3 It is a resistivity isogram of an embodiment of the present invention.

[0048] Figure 4 It is an anomaly index isogram of an embodiment of the present invention.

[0049] Figure 5 It is a tunnel anomaly grade map of the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following further illustrates the present invention by taking the extraction of the anomaly index of a two-dimensional resistivity profile and the identification of geological anomalies as examples.

[0051] As Figure 1 shown, an embodiment of the present invention provides a method for improving the anomaly interpretation accuracy of tunnel electrical prospecting, which adopts a geological anomaly delineation method based on resistivity and resistivity gradient to construct an anomaly index to improve the anomaly interpretation accuracy of tunnel electrical prospecting. The specific steps are as follows:

[0052] S1. Obtain a resistivity profile or a three-dimensional resistivity slice to form a resistivity data volume; as Figure 2 shown, specifically, through steps such as data acquisition and inversion, a resistivity profile or a three-dimensional resistivity slice of the work area can be obtained, and the resistivity of each survey line or survey network is extracted to form a resistivity data volume . In S1, the resistivity data volume It is a two-dimensional array containing the plane coordinates, elevation, and resistivity values of each node. It should be noted that since the depth division corresponding to each measurement point is the same, therefore, it can be considered that the resistivity of each measurement point conforms to the grid division. Thus, the nodes of each grid are recorded as the nodes of the resistivity data volume.

[0053] S2. For the resistivity data volume, use the positions and resistivity values of each node to calculate the gradient of each point , and obtain the gradient data volume .

[0054] Furthermore, in S2, if a three-dimensional resistivity slice is adopted to form the resistivity data volume, then when using the positions and resistivity values of each node to calculate the gradient of each point , it includes:

[0055] S201. Respectively obtain the gradients along the survey line, perpendicular to the survey line, and along the depth direction ( , and direction);

[0056] S202. For the data nodes at non-boundary locations , whose three-dimensional coordinates are , calculate the absolute value of its gradient as:

[0057]

[0058] where, , , respectively represent the resistivity data values of the data node at the three-dimensional coordinates , , , respectively represent the resistivity gradient values of the data node at the three-dimensional coordinates ; , , respectively represent the coordinates of the data node at the next position. Note that , , are for convenient representation. At the node , they represent the same resistivity value.

[0059] As a preferred embodiment, if a two-dimensional resistivity profile is adopted to form the resistivity data volume, then when using the positions and resistivity values of each node to calculate the gradient of each point , it includes: Then it is necessary to obtain the gradients along the survey line and along the depth direction ( and Gradient in the () direction, for data nodes at non-boundary positions , whose two-dimensional coordinates are , calculate the absolute value of its gradient as:

[0060]

[0061] Wherein, and respectively represent the resistivity data values of the data node at the coordinate ; , respectively represent the resistivity gradient values of the data node at the coordinate ; and respectively represent the two-dimensional coordinates of the data node at the next position. Note that and are for convenient representation, and at the node , represent the same resistivity value.

[0062] Further preferably, in S2, for data points at the boundary , on the basis of obtaining the gradients of non-boundary points, interpolation methods such as spline interpolation and Hermite interpolation are respectively used for extrapolation in each direction to obtain gradient values.

[0063] S3. Normalize the resistivity data volume and the gradient data volume to obtain an inverse-normalized resistivity data volume and a normalized gradient data volume ;

[0064] Since the variation range of resistivity values is often large, ranging from as small as a few to as large as tens of thousands , if the resistivity values and gradient values are directly superimposed, it is often difficult to grasp the order of magnitude between the resistivity values and gradient values, resulting in an overly large proportion of a certain item with a large value when performing anomaly weighting. Therefore, in the present invention, the resistivity data volume and the gradient data volume are first normalized to obtain a normalized resistivity data volume and a gradient data volume .

[0065] Further, in S3, considering that the resistivity data and gradient data as a whole both obey or basically obey the normal distribution, the present invention first calculates the mean and standard deviation for the resistivity data volume and gradient data volume, and then, according to the actual distribution of the data, the values exceeding n times the standard deviation of the mean are removed as extreme values. After removing the outliers and extreme values, the Min-Max normalization method is used for normalization, so that the minimum value of each data volume is 0 and the maximum value is 1. In S3, for the resistivity data volume after standardization processing, 1 minus each standardized value is used to obtain a normalized resistivity data volume with opposite weights 。

[0066] S4. According to the inverse-normalized resistivity data volume and the normalized gradient data volume , calculate the anomaly weighting of each node to obtain an anomaly weighting data volume ;

[0067] In S4, for the data node , according to the inverse-normalized resistivity data volume and the normalized gradient data volume, the anomaly weighting value at this point is calculated as

[0068]

[0069] Wherein is the inverse-normalized resistivity data value at the data node , and ; 、 、 are respectively 、 and the normalized resistivity gradient values in three directions; 、 and are respectively 、 and the weights in three directions. The weight ratio is independently selected according to the exploration target. For example, when detecting target geological bodies such as boulders, the same weight can be used in each direction; when stratifying the strata, the weight in the direction can be appropriately increased ; if it is to find faults, then the weights in the horizontal direction can be made larger, for example and β is taken as 0.5, and α is taken as 2.

[0070] S5. Perform secondary normalization on the anomaly weighting data volume to obtain the anomaly index of each node and form an anomaly index data volume 。

[0071] Further preferably, in S5, for the anomaly weighting data volume , calculate the mean and variance, and remove the data outside the 99% confidence interval. Then, the newly formed data body is normalized using the Min-Max normalization method so that the minimum value of each data body is 0 and the maximum value is 1, and the abnormal index data body is constructed. .

[0072] S6. Extract the anomaly index, draw an anomaly index result map, and classify the tunnel anomaly level according to the numerical value.

[0073] In S6, the anomaly index data body It is a data matrix containing the plane coordinates, elevation and anomaly index values ​​of each data point. For this data body, the anomaly result map is drawn using conventional drawing software. Figure 4 shown.

[0074] like Figure 5 As shown, in S6, it also includes data for abnormal index Medium data node , according to the abnormal index value of the point Classify abnormal levels:

[0075] ① , the rock mass belongs to the V-type anomaly, corresponding to the complete rock mass, undeveloped or closed structural planes, and good stability;

[0076] ② , the rock mass belongs to the type IV anomaly, corresponding to a relatively complete rock mass, a relatively developed structural surface, and good stability;

[0077] ③ , the rock mass belongs to the type III anomaly, corresponding to the relatively broken rock mass, relatively developed structural planes, and general stability;

[0078] ④ , the rock mass belongs to type II anomaly, corresponding to rock mass fragmentation, structural surface development, and poor stability;

[0079] ⑤ The rock mass belongs to Class I anomaly, which corresponds to extremely fragmented rock mass, extremely developed structural surfaces and poor stability.

[0080] like Figure 2 As shown, the present invention also provides a system for improving the accuracy of anomaly interpretation in tunnel electrical exploration, which is used to implement the above anomaly index calculation process, including:

[0081] The data acquisition module is used to extract all resistivity values ​​from the resistivity inversion results and form a resistivity data volume.

[0082] The gradient calculation module is used to calculate the gradients of each node in the resistivity data volume along different directions and form a gradient data volume.

[0083] A normalization module, which is used to normalize the resistivity data volume and the resistivity gradient data volume after removing outliers and extreme points by using the Min-Max normalization method.

[0084] An anomaly weighting module, which is used to add the inverse-normalized resistivity data volume and the normalized gradient data volume with different weights to form an anomaly-weighted data volume.

[0085] A secondary normalization module, which is used to normalize the anomaly-weighted data volume after removing outliers and extreme values by using the Min-Max normalization method, so as to obtain an anomaly index data volume.

[0086] A graphical interpretation module, which is used to draw an anomaly index result map according to the anomaly index data volume. The anomaly level is divided and the tunnel anomaly is delineated according to the anomaly index result map.

[0087] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0088] (1) By using the weighted summation after normalizing the resistivity value and the gradient value, it is possible to take into account both the resistivity value anomaly and the gradient anomaly during anomaly division, and it is possible to more accurately identify anomalies and delineate the anomaly positions.

[0089] (2) Removing outliers and extreme values from the anomaly-weighted data volume can effectively avoid the influence of abnormal data on the overall rating; performing secondary normalization makes the anomaly index fall within the range of [0, 1], and the anomaly level can be presented by objective indicators, avoiding the subjectivity of artificially dividing anomalies.

[0090] Obviously, the above embodiments are only examples for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting, characterized in that, Including the following steps: S1. Obtain a resistivity profile or a three-dimensional resistivity slice to form a resistivity data volume ; S2. Using the resistivity data volume, calculate the gradient at each point based on the positions and resistivity values of each node, to obtain a gradient data volume; ; S3. Normalize the resistivity data volume and the gradient data volume to obtain an inverse-normalized resistivity data volume and a normalized gradient data volume ; S4. According to the inverse-normalized resistivity data volume and the normalized gradient data volume , calculate the anomaly weighting of each node to obtain the anomaly weighting data volume ; S5. Perform secondary normalization on the abnormal weighted data volume, obtain the abnormal index of each node, and form an abnormal index data volume ; S6. Extract the anomaly index, draw the anomaly index result graph, and classify the tunnel anomaly level according to the numerical size.

2. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that In S1, the resistivity data volume is a two-dimensional array containing the plane coordinates, elevation, and resistivity values of each node.

3. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that, If a three-dimensional resistivity slice is adopted to form a resistivity data volume, then the gradients of each point are calculated using the positions of each node and the resistivity values. When including: S201. Calculate the gradients along the survey line , perpendicular to the survey line and in the depth direction respectively; S202. For the data nodes at non-boundary locations , whose three-dimensional coordinates are , calculate the absolute value of the gradient as follows: Among them, , , respectively represent the resistivity data values of the data node at the three-dimensional coordinate ; , , respectively represent the resistivity gradient values of the data node at the three-dimensional coordinate ; , , respectively represent the coordinates of the data node at the next position, , , are for convenient representation, and at the node , represent the same resistivity value.

4. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that, If a two-dimensional resistivity profile is adopted to form a resistivity data volume, the gradients at each point are calculated using the positions of each node and the resistivity values. When calculating, it includes: obtaining the gradients along the survey line and along the depth directions as needed. For the data nodes at non-boundary positions , whose two-dimensional coordinates are , the absolute value of the calculated gradient is: Among them, and respectively represent the resistivity data values of the data node at the coordinate . , respectively represent the resistivity gradient values of the data node at the coordinate ; and respectively represent the coordinates of the data node at the next position. and are for convenient representation and represent the same resistivity value at the node .​ 5. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that, In S3, the normalization of the resistivity data volume and the gradient data volume includes: Calculating the mean and standard deviation for the resistivity data volume and the gradient data volume; According to the actual data distribution, removing the values exceeding n times the standard deviation of the mean as extreme values; Using the Min-Max normalization method for normalization processing to make the minimum value of each data volume 0 and the maximum value 1.

6. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 5, characterized in that It also includes obtaining an inverse-normalized resistivity data volume with opposite weights by using 1 minus each normalized value for the normalized resistivity data volume. .

7. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 6, characterized in that, In S4, for the data node , the abnormal weighted value at this point is calculated according to the inverse-normalized resistivity data volume and the gradient data volume of the resistivity as follows: Among them, is the inverse normalized resistivity data value at the data node, and ; ; , , are respectively , and the normalized resistivity gradient values in three directions; , and are respectively , and the weights in three directions, and the weight ratio is independently selected according to the exploration target.

8. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that, In S5, the secondary normalization of the anomaly weighted data volume includes: Weighted data set for anomalies , calculate the mean and variance; and remove the data outside the 99% confidence interval, and use the Min-Max normalization method to normalize the retained data so that the minimum value of the data body is 0 and the maximum value is 1; form an abnormal index data body .

9. The method for improving the accuracy of anomaly interpretation in tunnel electrical prospecting according to claim 1, characterized in that, In S6, for the abnormal index data body the data nodes , according to the data nodes abnormal index value perform abnormal level classification: When the rock mass belongs to Class V anomaly, corresponding to a complete rock mass, undeveloped or closed structural planes, and good stability; When the rock mass belongs to Class IV anomaly, corresponding to relatively intact rock mass, relatively developed structural planes and relatively good stability; When the rock mass belongs to Class III anomaly, corresponding to relatively fractured rock mass, relatively developed structural planes and general stability; When The rock mass belongs to Class II anomaly, corresponding to broken rock mass, developed structural planes and poor stability; When the rock mass belongs to Class Ι anomaly, corresponding to extremely fragmented rock mass, extremely developed structural planes and poor stability.

10. A system for improving the accuracy of anomaly interpretation in tunnel electrical prospecting, characterized in that, A method for improving the anomaly interpretation accuracy of tunnel electrical prospecting according to any one of claims 1-9, including: A data acquisition module that acquires resistivity profiles or three-dimensional resistivity slices to form a resistivity data volume ; Gradient calculation module, which calculates the gradient of each point for the resistivity data volume by using the position and resistivity value of each node , and obtains the gradient data volume ; A normalization module that normalizes the resistivity data volume and the gradient data volume to obtain a normalized gradient data volume and an inverse-normalized resistivity data volume; Anomaly weighting module, which calculates the anomaly weighting of each node based on the inverse-normalized resistivity data volume and the normalized gradient data volume to obtain the anomaly weighting data volume ; The secondary normalization module performs secondary normalization on the anomaly-weighted data volume to obtain the anomaly indices of each node and form an anomaly index data volume ; An interpretation module that extracts the anomaly index, draws the anomaly index result graph, and classifies the tunnel anomaly level according to the numerical size.

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