A method for identifying and positioning abnormal boundaries of loose poor geological bodies

Abstract

The application discloses a method for identifying and positioning abnormal boundaries of loose bad geological bodies, which comprises the following steps: obtaining geophysical data corresponding to the loose bad geological bodies and pre-processing the data; obtaining multi-step gradient data of the pre-processed geophysical data according to the attributes of the geophysical data, and converting the data into a gray-scale image; performing mapping processing on the pre-processed geophysical data to obtain a first image; performing edge detection on the first image to obtain a second image corresponding to abnormal boundaries and abnormal regions; jointly using the gray-scale image and the second image to obtain the degree of coincidence of the gray-scale values in the numerical domain and the image domain, and obtaining the abnormal boundaries of the loose bad geological bodies. The method has the advantages of simple logic, accuracy and reliability, and has high practical value and popularization value in the field of geophysical exploration technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical exploration, and particularly relates to a method for identifying and positioning abnormal boundaries of loose poor geological bodies. BACKGROUND

[0002] In the field of geophysical exploration, the identification and interpretation of geophysical anomalies (especially the identification and positioning of abnormal boundaries) are difficult and demanding due to the multi-solution nature of geophysical results. For a long time, the identification and interpretation of geophysical anomalies have been manually performed by professional personnel with rich experience. However, this approach has the following disadvantages: first, the professional personnel engaged in this work are required to have high expertise, and the identification and interpretation results are greatly affected by the subjectivity of the personnel. Second, when the geophysical results are numerous in large-area exploration, the manual identification and interpretation are time-consuming and labor-intensive. Finally, in multi-method joint exploration, different methods have different parameters and different geological properties, and thus different professional personnel are required to perform the identification and interpretation, resulting in high labor costs. Therefore, it is of great research value and practical significance to develop a method for identifying and positioning geological anomalies of different geophysical results.

[0003] At present, the existing technology such as the patent with the publication number CN108952674 and the name of a method and system for analyzing and evaluating detection of poor geological bodies in soil-rock combination parts discloses a method and system for analyzing and evaluating detection of poor geological bodies in soil-rock combination parts. The patent includes constructing a detection result map of a to-be-detected area according to the electromagnetic wave CT detection results of the detection area and the positions of the corresponding detection drill holes; obtaining a first bedrock boundary line of the detection result map according to the electromagnetic wave absorption rates of the detection drill holes in the electromagnetic wave CT detection results of the to-be-detected area, and regarding the area between the first bedrock boundary line and a second bedrock boundary line of the result map obtained through drilling results as a warning area; if it is determined that the warning area is a karst underdeveloped area, the warning area has a fracture zone or other poor geological body; and if it is determined that the warning area is a karst developed area, the warning area has karst.

[0004] It should be noted that although the patent overcomes the problem of difficulty in identifying geological anomalies caused by strong absorption of electromagnetic waves in soil-rock combination parts, the method disclosed in the patent still mainly relies on manual identification and interpretation, and thus has the above-mentioned problems.

[0005] In addition, in the paper of Wang Chao, a method for identifying boundaries of magnetotelluric anomalies, the identification effect of numerical boundary identification methods on magnetotelluric anomaly boundaries is studied. The research results of the paper show that the numerical boundary identification method can be applied to magnetotelluric sounding method, and can accurately reflect the boundaries and positions of abnormal bodies, but there is still a deviation from the true positions of the abnormal bodies.

[0006] Therefore, it is urgent to provide a loose adverse geological body abnormal boundary identification positioning method with simple logic, accuracy and reliability. SUMMARY

[0007] In view of the above problems, the purpose of the present application is to provide a loose adverse geological body abnormal boundary identification positioning method, and the technical solution adopted by the present application is as follows:

[0008] A loose adverse geological body abnormal boundary identification positioning method, comprising the following steps:

[0009] Obtaining geophysical data corresponding to the loose adverse geological body and pre-processing;

[0010] According to the attribute of the geophysical data, the multi-order gradient data of the pre-processed geophysical data is obtained, and is converted into a gray scale image;

[0011] The pre-processed geophysical data is processed to obtain a first image;

[0012] The first image is detected by edge detection to obtain a second image corresponding to the abnormal boundary and the abnormal area;

[0013] The gray scale image and the second image are combined to obtain the degree of coincidence of the numerical domain and the image domain, and the abnormal boundary of the loose adverse geological body is obtained.

[0014] Preferably, the geophysical data corresponding to the loose adverse geological body is obtained and pre-processed, which includes processing the geophysical data by interpolation regularization, and the expression is:

[0015]

[0016] Wherein, x represents the original data sequence; y(x) represents the interpolated data sequence; n represents the sequence number; x i represents the i-th original data; x j represents the j-th original data; y k represents the Lagrange operator.

[0017] Preferably, the pre-processing includes data discretization, interpolation regularization and normalization.

[0018] Further, according to the attribute of the geophysical data, the multi-order gradient data of the pre-processed geophysical data is obtained; if the geophysical data is two-dimensional data, the total horizontal gradient THDR(x,z) and the vertical gradient VDR(x,z) corresponding to the two-dimensional data of the geophysical data are obtained in the horizontal and vertical directions, and the expression is:

[0019]

[0020]

[0021] wherein p(x, z) represents two-dimensional geophysical data; p x represents the lateral value of the geophysical data; p z represents the vertical value of the geophysical data.

[0022] Further, according to the attribute of the geophysical data, the multi-order gradient data of the pre-processed geophysical data is obtained; if the geophysical data is three-dimensional data, the total horizontal gradient THDR(x, y, z) and the vertical gradient VDR(x, y, z) corresponding to the three-dimensional data are obtained, which are represented as:

[0023]

[0024]

[0025] wherein p(x, y, z) represents three-dimensional geophysical data; p x represents the lateral value of the geophysical data; p y represents the longitudinal value of the geophysical data; p z represents the vertical value of the geophysical data.

[0026] Further, the first image is detected by using edge detection to obtain a second image corresponding to the abnormal boundary and the abnormal area, including:

[0027] The first image is smoothed by using a plurality of Gaussian filter kernels with different sizes;

[0028] The gradient amplitude and direction of the pixels of the smoothed first image are calculated by using a first-order finite difference algorithm;

[0029] The local maximum value is obtained, and the gray value corresponding to the non-maximum value is set as a background pixel point;

[0030] The maximum threshold value and the minimum threshold value are preset;

[0031] Any pixel point of the smoothed first image is extracted and compared with the maximum threshold value and the minimum threshold value, and the second image is obtained by screening.

[0032] Further, the screening includes the following steps:

[0033] Any pixel point of the smoothed first image is extracted;

[0034] If the amplitude of the pixel point is greater than the maximum threshold value, the pixel point is retained;

[0035] If the amplitude of the pixel point is less than the minimum threshold value, the pixel point is removed;

[0036] If the amplitude of the pixel point is between the maximum threshold value and the minimum threshold value, eight adjacent pixel points around the pixel point are obtained, if at least one of the eight pixel points is greater than the maximum threshold value, the pixel point is retained, otherwise the pixel point is removed.

[0037] Further, the first image after smoothing is processed by using a first-order finite difference algorithm to calculate the picture pixel gradient amplitude and direction, and the expression is:

[0038] P(i,j)=[g(i,j+1)-g(i,j)]+[g(i+1,j+1)-g(i+1,j)]

[0039] Q(i,j)=[g(i+1,j)-g(i,j)]+[g(i+1,j+1)-g(i,j+1)]

[0040]

[0041] θ(i,j)=arctan(Q(i,j),P(i,j))

[0042] Wherein, P(i,j) represents the longitudinal gradient vector; Q(i,j) represents the transverse gradient vector; M(i,j) represents the gradient amplitude; θ(i,j) represents the gradient direction; g(i,j) represents the image pixel point of the i row j column; g(i,j+1) represents the image pixel point of the i row j+1 column; g(i+1,j+1) represents the image pixel point of the i+1 row j+1 column; g(i+1,j) represents the image pixel point of the i+1 row j column.

[0043] Preferably, the gray scale and the second image are combined to obtain the degree of matching of the gray scale value in the numerical domain and the image domain, and further comprising:

[0044] If the gray scale value of the same pixel point in the numerical domain and the image domain is 1, the pixel point is a strong boundary;

[0045] If the gray scale value of the same pixel point in the numerical domain or the image domain is 1, the pixel point is a weak boundary;

[0046] If the gray scale value of the same pixel point in the numerical domain and the image domain is 0, the pixel point is a background pixel.

[0047] Further, the degree of matching of the gray scale value in the numerical domain and the image domain is obtained, which is expressed as:

[0048]

[0049] Wherein, m represents the number of pixel points with the gray value of 1 in the numerical domain and the image domain; b represents the number of pixel points with the gray value of 1 in the numerical domain or the image domain; M represents the total sum of all pixel points.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] (1) The present application combines the algorithm of the numerical domain and the edge detection of the image domain, and utilizes the matching degree of the gray values of the numerical domain and the image domain to realize the quick and accurate identification and positioning of the abnormal boundary of the loose bad geological body, and meanwhile, the identified abnormal boundary is subjected to quantitative analysis and evaluation, thereby providing technical reference for the subsequent work.

[0052] (2) The present application does not need to rely on artificial identification and explanation by technical experts, and has higher accuracy and reliability, and it utilizes the gray values of the pixel points to make reliable judgment.

[0053] (3) The present application ingeniously obtains the total horizontal gradient and the vertical gradient of the geophysical data, the total horizontal gradient is determined by calculating the maximum value of the horizontal gradient to determine the position of the abnormal body boundary, in addition, the vertical gradient is mainly determined by the zero value position to determine the abnormal boundary, the vertical gradient can distinguish the mutually superimposed abnormal bodies and reduce the influence of the surrounding rock on the identification of the abnormal boundary. Meanwhile, when the order of the vertical derivative increases, the abnormal attenuation speed is accelerated, and therefore, the high-order gradient has stronger resolution ability of the abnormality.

[0054] (4) The present application ingeniously utilizes the edge detection algorithm to identify, and under normal circumstances, there is a physical property difference between the geological abnormal body and the surrounding rock, and this difference in physical property will be reflected on the geophysical result map, such as the high and low resistivity, etc., the present application utilizes this feature, combines the commonly used edge detection algorithm in image processing, and detects, identifies and positions the geological abnormal interface in the geophysical result map.

[0055] (5) The present application combines the algorithm of the numerical domain and the edge detection of the image domain, and obtains the matching degree of the gray values of the numerical domain and the image domain, thereby ensuring the detection accuracy and reliability.

[0056] In summary, the present application has the advantages of simple logic, accuracy and reliability, and has high practical value and popularization value in the field of geophysical exploration technology. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical scheme of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation to the protection scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0058] Figure 1 A logic flow chart of the present application.

[0059] Figure 2 A geophysical data original result chart of the present application.

[0060] Figure 3 A numerical domain-geological anomaly gray scale chart of the present application.

[0061] Figure 4 An image domain-geological anomaly gray scale chart of the present application.

[0062] Figure 5 A dual domain superimposed gray scale chart of the present application.

[0063] Figure 6 A geological anomaly boundary identification chart of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described below in combination with the drawings and embodiments. The embodiments of the present application include but are not limited to the following embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor shall fall within the scope of protection of the present application.

[0065] In the present embodiment, the term "and / or" is merely used to describe the association relationship of the associated objects, and can represent the existence of three relationships, for example, A and / or B can represent the existence of A alone, the existence of A and B simultaneously, and the existence of B alone.

[0066] In the present embodiment, the terms "first" and "second" and the like in the description and claims are used to distinguish different objects, and are not used to describe the specific order of the objects. For example, the first target object and the second target object are used to distinguish different target objects, and are not used to describe the specific order of the target objects.

[0067] In the present embodiment, the words "exemplary" or "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "exemplary" or "for example" in the present embodiment should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. On the contrary, the words "exemplary" or "for example" are intended to present the relevant concept in a specific manner.

[0068] In the description of the present embodiment, unless otherwise specified, the meaning of "a plurality of" is two or more. For example, a plurality of processing units means two or more processing units; a plurality of systems means two or more systems.

[0069] As Figures 1 to 6As shown, the embodiment provides a loose adverse geological body abnormal boundary identification positioning method, which combines the algorithm of numerical domain and edge detection of image domain innovatively, realizes the rapid and accurate identification and positioning of the abnormal boundary of the loose adverse geological body, and performs quantitative analysis and evaluation on the identified abnormal boundary, thereby providing technical reference for subsequent work.

[0070] Specifically, the embodiment includes the following steps:

[0071] In the first step, the geophysical data corresponding to the loose adverse geological body is acquired and preprocessed, which includes but is not limited to data discretization, interpolation regularization, normalization and the like.

[0072] In the embodiment, generally, the geophysical result data is irregular two-dimensional or three-dimensional data body, or part of the data is missing, therefore, the geophysical result data is first interpolated and regularized, preferably, the interpolation method can be Lagrange interpolation method, and specifically, the expression is as follows:

[0073]

[0074] Wherein, x represents the original data sequence; y(x) represents the interpolated data sequence; n represents the sequence number; x i represents the i-th original data; x j represents the j-th original data; y k represents the Lagrange operator.

[0075] According to the specific attributes of the geophysical result data, such as resistivity, wave velocity and the like, the geophysical result data is normalized to convert the data into dimensionless data, so as to simplify the subsequent calculation, and preferably, linear normalization can be adopted, and the specific formula is as follows:

[0076]

[0077] Wherein, x' represents the data after normalization processing; max(x) and min(x) represent the maximum value and the minimum value of linear normalization respectively.

[0078] In the second step, according to the attributes of the geophysical data, the multi-order gradient data of the preprocessed geophysical data in each direction (horizontal, vertical) is obtained, and is converted into a gray image.

[0079] (1) If the geophysical result data is two-dimensional data, the total horizontal gradient and the vertical gradient of the geophysical data in the horizontal direction (mileage direction) and the vertical direction (depth direction) need to be calculated, the total horizontal gradient is determined by calculating the maximum value of the horizontal gradient to determine the position of the abnormal body boundary, and the total horizontal gradient is divided into two steps, one is to calculate the first-order gradient of the horizontal component, and then the square sum of the first-order gradient of the horizontal component is obtained to get the total horizontal gradient, which is expressed as:

[0080]

[0081] In addition, the expression of the vertical gradient is:

[0082]

[0083] Wherein, p(x,z) represents two-dimensional geophysical data; p x represents the lateral value of the geophysical data; p z represents the vertical value of the geophysical data.

[0084] The vertical gradient of the embodiment mainly determines the abnormal boundary through the zero value position, can distinguish the mutually superimposed abnormal body, and reduces the influence of the surrounding rock on the abnormal boundary identification. Meanwhile, when the order of the vertical derivative increases, the abnormal attenuation speed increases, so that the high-order gradient has stronger resolution abnormality.

[0085] (2) If the geophysical data is three-dimensional data, the total horizontal gradient THDR(x,y,z) and the vertical gradient VDR(x,y,z) corresponding to the three-dimensional data are obtained, which are represented as:

[0086]

[0087]

[0088] Wherein, p(x,y,z) represents three-dimensional geophysical data; p x represents the lateral value of the geophysical data; p y represents the longitudinal value of the geophysical data; p z represents the vertical value of the geophysical data.

[0089] Thirdly, in the image domain anomaly identification processing, the measured geophysical data is directly processed into a map, and according to the specific properties of the geophysical data and the specific embodiment of the anomaly in the result map, the edge detection technology is used to locate and identify the abnormal boundary and range.

[0090] Generally, there is a physical property difference between the geological anomaly body and the surrounding rock, and this difference in physical property will be reflected in the geophysical result map, such as resistivity high and low, etc. The present application utilizes this characteristic, and combines the edge detection algorithm commonly used in image processing, to detect, identify and locate the geological anomaly interface in the geophysical result map.

[0091] Specifically, first, the geophysical result map needs to be denoised. The rapid change of color in the map belongs to high-frequency information, and the slow change of color belongs to low-frequency information. The noise part in the map also belongs to high-frequency information, so the noise in the result map needs to be removed. Here, a 5*5 Gaussian filter kernel is used to smooth the image and denoise.

[0092] In the embodiment, the first image after smoothing is processed by using a first-order finite difference algorithm to calculate the pixel gradient amplitude and direction, and the expression is:

[0093] P(i,j) = [g(i,j+1) - g(i,j)] + [g(i+1,j+1) - g(i+1,j)]

[0094] Q(i,j) = [g(i+1,j) - g(i,j)] + [g(i+1,j+1) - g(i,j+1)]

[0095]

[0096] θ(i,j) = arctan(Q(i,j),P(i,j))

[0097] In the embodiment, the local maximum value is obtained, and the gray value corresponding to the non-maximum value is set as a background pixel point. The pixel adjacent region satisfying the local optimal value of the gradient value is judged as the edge of the pixel. That is, the pixel point with large gradient is retained, and the stray point beside the edge has relatively small gradient, and the stray point is removed by using non-maximum suppression.

[0098] In addition, by setting the maximum and minimum threshold values, if the amplitude of a certain pixel position exceeds the maximum threshold value, the pixel is retained as an edge pixel, if the amplitude of a certain pixel position is less than the minimum threshold value, the pixel is excluded, and if the amplitude of a certain pixel position is between the two threshold values, the pixel is only retained when connected to a pixel higher than the maximum threshold value.

[0099] In the fourth step, the gray scale of the numerical value domain is compared with the abnormal result of the image domain recognition, and the abnormal boundary recognition result is obtained by comprehensively combining the two results. Here, if the gray values of the same pixel point in the numerical value domain and the image domain are both 1, the pixel point is a strong boundary; if the gray values of the same pixel point in the numerical value domain or the image domain are 1, the pixel point is a weak boundary; and if the gray values of the same pixel point in the numerical value domain and the image domain are both 0, the pixel point is a background pixel.

[0100] In the fifth step, in the embodiment, the degree of agreement of the gray values of the numerical value domain and the image domain is calculated, and the reliability of the abnormal boundary recognition result is judged according to the standard. According to experience, the degree of agreement at least reaches 60%, and the final abnormal boundary recognition result is relatively reliable.

[0101] Wherein, the degree of agreement of the gray values of the numerical value domain and the image domain is obtained, and the expression is:

[0102]

[0103] Wherein, m represents the number of pixel points with the gray value of 1 in the value domain and the image domain; b represents the number of pixel points with the gray value of 1 in the value domain or the image domain; M represents the total sum of all pixel points.

[0104] As shown in Figures 2 to 6 , the present embodiment lists an actual case, and the final experimental case is 72.32% in accordance with the method for identification, which is more reliable, and the final abnormal boundary identification result comparison diagram is shown in Figure 5 , it can be seen from the figure that the method can identify the abnormal boundary of the loose poor geological body, and is more accurate and reliable. The present application solves the technical problem of low accuracy of human identification and interpretation, and has outstanding substantial characteristics and significant progress compared with the prior art.

[0105] The above embodiments are only preferred embodiments of the present application, not a limitation on the protection scope of the present application, but any change made by using the design principle of the present application and on the basis of non-creative labor shall belong to the protection scope of the present application.

Claims

1. A method for identifying and locating abnormal boundaries of loose bad geological bodies, characterized in that, The method comprises the following steps: Obtaining geophysical data corresponding to the loose adverse geological body and preprocessing the data; According to the attributes of the geophysical data, the multi-step gradient data of the preprocessed geophysical data is obtained and converted into a gray image; If the geophysical data is two-dimensional data, the total horizontal gradient corresponding to the two-dimensional data is obtained in the lateral and vertical directions of the geophysical data and the vertical gradient The expression is: ； ； wherein represents two-dimensional geophysical data; represents lateral values of the geophysical data; represents vertical values of the geophysical data; If the geophysical data is three-dimensional data, then total horizontal gradients and vertical gradients corresponding to the three-dimensional data are obtained and are expressed as: Gx = (Gx1+ Gx2+ Gx3+ Gx4+ Gx5+ Gx6+ Gx7+ Gx8+ Gx9+ Gx10+ G ； ； wherein represents the three-dimensional geophysical data; represents the longitudinal values of the geophysical data; The preprocessed geophysical data is processed to obtain a first image; The first image is detected by edge detection to obtain a second image corresponding to the abnormal boundary and abnormal area; The gray value coincidence degree of the numerical domain and the image domain is obtained by combining the gray image and the second image, and the abnormal boundary of the loose adverse geological body is obtained, including: If the gray values of the same pixel point in the numerical domain and the image domain are both 1, the pixel point is a strong boundary; If the gray value of the same pixel point in the numerical domain or the image domain is 1, the pixel point is a weak boundary; If the gray values of the same pixel point in the numerical domain and the image domain are both 0, the pixel point is a background pixel; The gray value coincidence degree is expressed as: ； wherein, represents the number of pixel points whose gray value is 1 in the value domain or the image domain; represents the number of pixel points whose gray value is 1 in the value domain or the image domain; represents the sum of all pixel points.

2. The method according to claim 1, wherein, Obtaining geophysical data corresponding to the loose adverse geological body and preprocessing the data, including processing the geophysical data by interpolation regularization, which is expressed as: ； wherein, denotes the original data sequence; denotes the interpolated data sequence; denotes the sequence number; denotes the original data; denotes the original data; denotes the Lagrange operator.

3. The method according to claim 1 or 2, characterized in that, The preprocessing includes data discretization, interpolation regularization and normalization.

4. The method of claim 1, wherein, Detecting the first image by edge detection to obtain a second image corresponding to the abnormal boundary and abnormal area, including: Smoothing the first image by using several different size Gaussian filter kernels; Calculating the image pixel gradient amplitude and direction of the smoothed first image by using a first-order finite difference algorithm; Obtaining the local maximum value and setting the gray value corresponding to the non-maximum value as a background pixel point; Predefining a maximum threshold and a minimum threshold; Extracting any pixel point of the smoothed first image and comparing it with the maximum threshold and the minimum threshold to obtain the second image.

5. The method of claim 4, wherein, The screening includes the following steps: Extracting any pixel point of the smoothed first image; If the amplitude of the pixel point is greater than the maximum threshold, the pixel point is retained; If the amplitude of the pixel point is less than the minimum threshold, the pixel point is removed; If the amplitude of the pixel point is between the maximum threshold and the minimum threshold, eight adjacent pixel points around the pixel point are obtained, if at least one of the eight pixel points is greater than the maximum threshold, the pixel point is retained, otherwise the pixel point is removed.

6. The method of claim 4, wherein, The expression of calculating the image pixel gradient amplitude and direction of the smoothed first image by using a first-order finite difference algorithm is: ； ； ； ； in, Represents the vertical gradient vector; Represents the lateral gradient vector; Indicates the gradient magnitude; Indicates the gradient direction; Indicates the first Line 1 The image pixels in the column; Indicates the first Line 1 +1 column of image pixels; Indicates the first +1 line +1 column of image pixels; Indicates the first +1 line The number of image pixels in a column.

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