Post-stack seismic fracture attribute enhancement method and device based on multi-scale Hessian matrix

Abstract

The invention discloses a post-stack seismic fracture attribute enhancement method and device based on a multi-scale Hessian matrix. The method comprises the steps of performing regularization processing on post-stack seismic fracture attribute slices, and obtaining a plurality of attribute slices with different scales through a Gaussian function convolution method; for each attribute point in the attribute slice of each scale, constructing a Hessian matrix, determining a first feature value, a second feature value and a first feature vector of the Hessian matrix, and judging whether the attribute point meets an enhancement condition of a filtering function or not according to the larger first feature value and the first feature vector; if yes, determining an enhanced attribute value through a filtering function according to the first characteristic value and the second characteristic value, and pre-establishing the filtering function based on a Frangi filtering function and a Steger filtering function; if not, determining that the enhanced attribute value is 0; and obtaining an enhanced attribute slice by taking maximized enhancement as a principle. According to the method, the enhanced crack line has the characteristics of being thin and narrow.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum exploration, and in particular to a method and a device for enhancing post-stack seismic fracture attributes based on a multi-scale Hessian matrix. Background Art

[0002] As the migration channel and storage space of oil and gas resources, fractures are extremely important in the entire process of oil and gas exploration and development. In the interpretation of seismic data, fractures are the key research objects, and the detailed characterization of fractures is very important. Usually, a variety of seismic attributes (coherence, curvature, variance body, etc.) can be used to identify fractures. Due to the resolution of seismic data or the limitations of the fracture detection method itself, the fracture attributes have a certain width, which reduces the signal-to-noise ratio and resolution of the fractures. Usually, the signal-to-noise ratio and resolution of fracture attributes can be improved by fracture enhancement technology.

[0003] Commonly used fracture enhancement techniques are mainly divided into two categories. The first category is to enhance fractures for seismic data bodies, such as the commonly used structural guidance filtering. On the premise of obtaining the fracture strike and dip, filtering is performed along the fracture direction to enhance fracture information (Hale, 2009; Feng and Li, 2012, Wu and Zhu, 2017), so that the fracture attributes have a higher signal-to-noise ratio and resolution. This type of method can highlight fracture information, but it cannot eliminate the resolution and signal-to-noise ratio of fracture attributes caused by the limitations of the fracture detection method itself. The second type is to directly enhance the fracture attributes of post-stack fractures, such as the commonly used edge enhancement techniques (Bakker et al., 1999; Yang Peijie et al., 2010; Yu, 2016). This type of method enhances the noise while enhancing the fracture attributes. The present invention mainly adopts the second type of fracture enhancement method, and improves the multi-scale blood vessel enhancement filtering method proposed by Frangi et al. (1998) for seismic fracture attribute enhancement filtering. Summary of the invention

[0004] The inventors found that although the Frangi filter method in the prior art can highlight the crack information, after the enhanced filtering, the crack still has a certain width and cannot reflect the thin and narrow characteristics of the crack line. In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention, and through specific implementation methods, provide a post-stack seismic crack attribute enhancement method and device based on a multi-scale Hessian matrix, which well realizes crack enhancement and refinement.

[0005] In a first aspect, an embodiment of the present invention provides a post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix, comprising:

[0006] The post-stack seismic fracture attribute slices are regularized, and multiple attribute slices of different scales are obtained by convolution with Gaussian function.

[0007] For each attribute point in the attribute slice of each scale: construct a Hessian matrix, determine the first eigenvalue, the second eigenvalue and the first eigenvector corresponding to the Hessian matrix, the first eigenvalue is greater than the second eigenvalue, and judge whether the attribute point meets the enhancement condition of the filter function according to the first eigenvalue and the first eigenvector; if so, determine the enhanced attribute value through the filter function according to the first eigenvalue and the second eigenvalue, and the filter function is pre-established based on the Frangi filter function and the Steger filter function; if not, determine that the enhanced attribute value is 0;

[0008] Based on the principle of maximizing enhancement, the enhanced attribute slices are obtained.

[0009] In a second aspect, an embodiment of the present invention provides a post-stack seismic fracture attribute enhancement device based on a multi-scale Hessian matrix, comprising:

[0010] The multi-scale attribute slice establishment module is used to perform regularization processing on the attribute slices of post-stack seismic fractures, and obtain attribute slices of multiple different scales of the attribute slices after regularization processing by convolution with Gaussian function;

[0011] A multi-scale attribute value determination module is used for: for each attribute point in the attribute slice of each scale: constructing a Hessian matrix, determining a first eigenvalue, a second eigenvalue and a first eigenvector corresponding to the Hessian matrix, wherein the first eigenvalue is greater than the second eigenvalue, and judging whether the attribute point meets the enhancement condition of the filter function according to the first eigenvalue and the first eigenvector; if so, determining an enhanced attribute value through the filter function according to the first eigenvalue and the second eigenvalue, wherein the filter function is pre-established based on the Frangi filter function and the Steger filter function; if not, determining that the enhanced attribute value is 0;

[0012] The enhanced attribute slice establishment module is used to obtain the enhanced attribute slice based on the principle of maximizing enhancement.

[0013] In a third aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix is ​​implemented.

[0014] In a fourth aspect, an embodiment of the present disclosure provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix is ​​implemented.

[0015] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0016] (1) The post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix provided in the embodiment of the present invention pre-establishes a filter function based on the Frangi filter function and the Steger filter function, improves the signal-to-noise ratio and resolution of the post-stack fracture attributes, realizes fracture enhancement, and achieves the purpose of thinning and narrowing the fracture line.

[0017] (2) Frangi filtering method has many parameters, and it is necessary to repeatedly test the parameters in the application process to obtain a better enhancement result. The post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix provided by the embodiment of the present invention only requires input parameters related to determining multiple scale factors, so it is easy to operate and has strong feasibility.

[0018] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0019] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0021] Figure 1 This is a flow chart of a post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix in Embodiment 1 of the present invention;

[0022] Figure 2 is the post-stack coherence attribute in the second embodiment of the present invention;

[0023] Figure 3 For Figure 1 The result after regularization of post-stack coherence attributes in ;

[0024] Figure 4 For Figure 2 The regularized attribute slices in the result of Frangi filtering;

[0025] Figure 5 For Figure 2 The regularized attribute slice in the embodiment of the present invention is used to enhance the filtering result;

[0026] Figure 6 Schematic diagram of the structure of a post-stack seismic fracture attribute enhancement device based on a multi-scale Hessian matrix in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0028] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0029] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0030] The embodiment of the present invention provides a method and device for enhancing post-stack seismic fracture properties based on a multi-scale Hessian matrix, so that the enhanced fracture line has the characteristics of being thin and narrow.

[0031] Embodiment 1

[0032] Embodiment 1 of the present invention provides a post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix, and the process thereof is as follows Figure 1 As shown, the following steps are included:

[0033] Step S11: performing regularization processing on the post-stack seismic fracture attribute slices, and obtaining multiple attribute slices of different scales of the regularized attribute slices by convolution with a Gaussian function.

[0034] Since the ranges and types of different post-stack fracture attribute values ​​are inconsistent, such as small values ​​in the coherence attribute represent fracture information, while large values ​​in the curvature attribute represent fracture information. Therefore, in this step, the data is regularized according to the type characteristics of the data.

[0035] If the attribute value of the post-stack seismic fracture attribute slice is larger, it means that the fracture is more developed. The post-stack seismic fracture attribute slice is regularized by formula (1):

[0036]

[0037] If the smaller the attribute value of the post-stack seismic fracture attribute slice is, the more developed the fracture is, the post-stack seismic fracture attribute slice is regularized by formula (2):

[0038]

[0039] In formula (1) and formula (2), I(x i ) and I norm (x i ) are the i-th attribute point x i Among the attribute values ​​in the post-stack seismic fracture attribute slice and the attribute values ​​in the attribute slice after regularization processing, max[I(x)] is the maximum attribute value in the post-stack seismic fracture attribute slice, and min[I(x)] is the minimum attribute value in the post-stack seismic fracture attribute slice.

[0040] After regularization, the attribute values ​​of the attribute slices are in the range of [0, 1], and the types are unified as large values ​​representing cracks.

[0041] In the framework of linear scale space theory, in order to construct fracture attribute slices of different scales, the convolution representation of Gaussian function and post-stack fracture attribute slices is used.

[0042] Specifically, for each scale factor, the attribute slice of the corresponding scale of the processed attribute slice is obtained by the following formula (3):

[0043]

[0044] In formula (3), I norm (x i )) and I norm (x i ; σ j ) are the i-th attribute point x i The attribute value and scale factor in the attribute slice after regularization are σ j The attribute value in the attribute slice of G(x), i and j represent the serial number of the attribute point and the serial number of the scale factor respectively; i ; σ j) is the i-th attribute point x in the attribute slice after regularization i The scale factor is σ j Gaussian function when ; “*” is the convolution operation.

[0045] Scale factor σ j satisfy:

[0046] σ j ∈[σ1,σ2], and σ1<σ2 (4)

[0047] In formula (4), σ1 and σ2 are the minimum and maximum values ​​of the scale factor, respectively. The minimum value of the scale factor σ1 is preset according to the width of the crack in the attribute slice after regularization.

[0048] Step S12: For each attribute point in the attribute slice of each scale: construct a Hessian matrix, determine the first eigenvalue, the second eigenvalue and the first eigenvector of the Hessian matrix, and judge whether the attribute point meets the enhancement condition of the filtering function according to the first eigenvalue and the first eigenvector; if so, determine the enhanced attribute value through the filtering function according to the first eigenvalue and the second eigenvalue; if not, determine that the enhanced attribute value is 0.

[0049] Furthermore, the first eigenvalue in step S12 is greater than the second eigenvalue, and the first eigenvector is the eigenvector corresponding to the first eigenvalue; the filter function is pre-established based on the Frangi filter function and the Steger filter function, and the specific establishment process will be described in detail later.

[0050] in I norm (x i ; σ j ) of the domain Δx i The second-order Taylor approximation in is:

[0051]

[0052] in and H are respectively i The gradient tensor and Hessian matrix at .

[0053] I norm (x i ; σ j )The corresponding Hessian matrix is:

[0054]

[0055] Therefore, for each attribute point in the attribute slice of each scale, the Hessian matrix can be constructed by the above formula (6).

[0056] According to the different scale enhancement filtering results proposed by Frangi et al. (1998), the results are:

[0057]

[0058] In formula (7), λ1 is the first eigenvalue, λ2 is the second eigenvalue; β and c are filter sensitivity threshold parameters for controlling the linear structure (specific values ​​need to be repeatedly debugged).

[0059] After the enhancement and filtering by the above method, the crack line still has a certain width. Therefore, in order to make the enhanced result thin and narrow, the Steger algorithm widely used in light extraction and blood vessel extraction is combined in this step. This algorithm mainly obtains the ridge line of the crack through the eigenvalue and eigenvector of the Hessian matrix to achieve the purpose of crack thinning.

[0060] The filter function pre-established based on the Frangi filter function and the Steger filter function is:

[0061]

[0062]

[0063] In formula (8) and formula (9), I norm (x i ; σ j ) and v(x i ; σ j ) are the i-th attribute point x i When the scale factor is σ j The attribute value in the attribute slice and the filtered attribute value, i and j represent the sequence number of the attribute point and the sequence number of the scale factor respectively; Among them, λ1 is the first eigenvalue, λ2 is the second eigenvalue; t is the vector scaling factor, e 1x and e 1y are the components of the first eigenvector in the corresponding coordinate directions; i and i are the i-th attribute point x in the attribute slice after regularization. i The corresponding coordinate values ​​at .

[0064] From formula (8), we can know that the first eigenvalue and the first eigenvector are used to determine whether the attribute point meets the enhancement condition of the filter function, that is, to determine whether λ1≤0 and |t·e 1x |≤0.5 and |t·e 1y |≤0.5 is true.

[0065] Step S13: Based on the principle of maximizing enhancement, an enhanced attribute slice is obtained.

[0066] For each attribute point, the maximum value of the enhanced attribute values ​​corresponding to each scale is determined as the enhanced attribute value E(x i ), and get the enhanced attribute slice:

[0067] E(x i )=max[v(x i ;σ)] σ∈[σ1,σ2] (10)

[0068] The post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix provided in the embodiment of the present invention pre-establishes a filter function based on the Frangi filter function and the Steger filter function, improves the signal-to-noise ratio and resolution of the post-stack fracture attributes, realizes fracture enhancement, and achieves the purpose of making the fracture line thin and narrow.

[0069] (2) Frangi filtering method has many parameters, and it is necessary to repeatedly test the parameters in the application process to obtain a better enhancement result. The post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix provided by the embodiment of the present invention only requires input parameters related to determining multiple scale values, so the operation is simple and the feasibility is strong.

[0070] The process of establishing the filter function based on the Frangi filter function and the Steger filter function is as follows:

[0071] According to Frangi (1998), the relationship between eigenvalues ​​and image features is as follows:

[0072] Table 1 Relationship between eigenvalues ​​and image features

[0073]

[0074] Note: The “+ / -” sign in the table indicates the positive or negative of the characteristic value, High indicates a large value, and Low indicates a small value.

[0075] Because, in the first step of the embodiment of the present invention, the attribute is changed to a large value indicating a crack through data regularization, so λ1≤0 needs to be satisfied.

[0076] The crack attributes have a similar structure to blood vessels in medical images and present a linear structure. Therefore, the crack attributes are recharacterized with reference to the blood vessel measurement method proposed by Frangi:

[0077]

[0078] max(|I(x;σ j )|) is the scale factor σ jThe maximum absolute value of the attribute value in the attribute slice of . Since the maximum value is equal to 1, equation (11) can be simplified to the above equation (8).

[0079] The eigenvalue of the Hessian matrix describes the concavity of the eigenvector direction near the point. The larger the eigenvalue, the stronger the convexity. Therefore, the eigenvector corresponding to the eigenvalue with the largest absolute value of the Hessian matrix is ​​the direction in which the light intensity changes fastest. We can look for the pixel point of the crack attribute ridge (that is, the point on the center line) in this place, thus realizing the crack refinement function.

[0080] Assume that the maximum eigenvalue λ1 of the Hessian matrix, the corresponding eigenvector is e1=(e 1x , e 1y ) to calculate the point (x i ,y i ) base point, and then perform a second-order Taylor expansion on the crack normal direction, then the point on the crack normal (x i +t·e 1x ,y i +t·e 1y ) is:

[0081]

[0082] Where t is the vector scaling factor, H is the Hessian matrix, and L is:

[0083]

[0084] The above formula can be regarded as a function of t, so we can find the derivative of t, and the position where the derivative is 0 is the pixel point of the ridge line. That is:

[0085]

[0086] Written in matrix form:

[0087]

[0088] From this we can get:

[0089]

[0090] If t·e 1x and t.e 1y are all less than 0.5, that is, point (x i +t·e 1x ,y i +t·e 1y ) does not exceed the current calculation point, it means that the extreme point is located within the current calculation point. At this time, the calculation point is the ridge line, and the attribute refinement can be achieved.

[0091] In formula (11), The value in the background area without structure will be small, and when the contrast is low, the eigenvalue is small, making S small. Compared with the background area, the area with higher contrast will have a large eigenvalue, making S large. is a measure of the speckle structure, which describes the eccentricity of the second-order ellipse. Compared with equation (7), equation (11) and the simplified equation (8) remove the parameters β and c. will increase, improving the contrast between the crack attributes and the background and enhancing the denoising effect.

[0092] Embodiment 2

[0093] Embodiment 2 of the present invention provides a specific application of a post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix, comprising the following steps:

[0094] (1) Input post-stack fracture attributes (such as coherence, curvature, etc.) I(x), the abnormal value characteristics of fracture attributes (large values ​​represent fractures or small values ​​represent fractures), the minimum scale factor σ1, the scale factor interval dσ, and the number of scale factors nσ, from which the maximum scale factor σ2 = σ1 + nσ × dσ can be calculated;

[0095] (2) According to the characteristics of the outliers, select formula (1) or formula (2) to regularize the crack attributes. The regularization result is I norm (x);

[0096] (3) Calculate the Gaussian function corresponding to the current scale σ∈[σ1,σ2] according to formula (3);

[0097] (4) Calculate the crack attribute I(x; σ) corresponding to the current scale σ according to formula (3);

[0098] (5) Calculate the Hessian matrix corresponding to the current scale σ according to formula (6);

[0099] (6) Solve the eigenvalues ​​and eigenvectors of the Hessian matrix;

[0100] (7) Calculate the first-order partial derivatives of I(x;σ) with respect to x and y respectively;

[0101] (8) Calculate the enhancement result corresponding to the current scale σ according to formula (8);

[0102] (9) Whether the calculation of all scales is completed. If not, repeat steps (3) to (8). If completed, the final enhancement result E(x) can be obtained according to formula (10).

[0103] The embodiment of the present invention uses the above algorithm flow to develop a program. In order to verify the feasibility and practicality of the content of the embodiment of the present invention and the correctness of the calculation program, the coherent attributes of the post-stack fractures in the xx work area (see Figure 2 ) to carry out the processing experiment. First, use formula (2) to Figure 2 The coherence properties shown are normalized to obtain Figure 3 The regularized data shown in Figure 2. According to σ1 = 1, dσ = 0.5, nσ = 7, the crack enhancement results with scale factors between [1, 4] are calculated, see Figure 5 To illustrate the computational effect of the present invention, we compared the results with those of Frangi multi-scale enhancement filtering, where the parameters of Frangi enhancement filtering are σ1=1, dσ=0.5, nσ=7, β=1, c=15. The Frangi enhancement filtering results are shown in Figure 4 .

[0104] from Figure 5 and Figure 4 From the enhancement results of the embodiment of the present invention, the crack line is thinner and narrower than the enhancement result of Frangi. At the same time, the embodiment of the present invention reduces the parameters of the calculation process.

[0105] The feasibility and practicability of the content of the present invention and the correctness of the calculation program are verified through examples.

[0106] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a post-stack seismic fracture attribute enhancement device based on a multi-scale Hessian matrix. The structure of the device is as follows: Figure 6 As shown, including:

[0107] The multi-scale attribute slice establishment module 61 is used to perform regularization processing on the post-stack seismic fracture attribute slices, and obtain multiple attribute slices of different scales of the attribute slices after regularization processing by convolution with Gaussian function;

[0108] The multi-scale attribute value determination module 62 is used for: for each attribute point in the attribute slice of each scale: constructing a Hessian matrix, determining a first eigenvalue, a second eigenvalue and a first eigenvector corresponding to the Hessian matrix, wherein the first eigenvalue is greater than the second eigenvalue, and judging whether the attribute point meets the enhancement condition of the filter function according to the first eigenvalue and the first eigenvector; if so, determining an enhanced attribute value through the filter function according to the first eigenvalue and the second eigenvalue, wherein the filter function is pre-established based on the Frangi filter function and the Steger filter function; if not, determining that the enhanced attribute value is 0;

[0109] The enhanced attribute slice establishing module 63 is used to obtain the enhanced attribute slice based on the principle of maximizing enhancement.

[0110] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0111] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix is ​​implemented.

[0112] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned post-stack seismic fracture attribute enhancement method based on the multi-scale Hessian matrix is ​​implemented.

[0113] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0114] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0115] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that an embodiment of the claimed subject matter requires more features than those set forth in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0116] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0117] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0118] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0119] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments can be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to represent "non-exclusive or". The terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance.

Claims

1. A post-stack seismic fracture attribute enhancement method based on multi-scale Hessian matrix, It is characterized in that include: The post-stack seismic fracture attribute slices are regularized, and multiple attribute slices of different scales are obtained by convolution with Gaussian function. For each attribute point in the attribute slice of each scale: construct a Hessian matrix, determine a first eigenvalue, a second eigenvalue of the Hessian matrix, and a first eigenvector corresponding to the first eigenvalue, the first eigenvalue being greater than the second eigenvalue, and determine whether the attribute point satisfies an enhancement condition of a filtering function according to the first eigenvalue and the first eigenvector; If yes, determining the enhanced attribute value by the filtering function according to the first eigenvalue and the second eigenvalue, the filtering function being pre-established based on a Frangi filtering function and a Steger filtering function; If not, the enhanced attribute value is determined to be 0; Based on the principle of maximizing enhancement, the enhanced attribute slices are obtained.

2. The method according to claim 1, It is characterized in that The regularization processing of post-stack seismic fracture attribute slices includes: If the attribute value of the post-stack seismic fracture attribute slice is larger, it means that the fracture is more developed. The post-stack seismic fracture attribute slice is regularized by formula (1): If the smaller the attribute value of the post-stack seismic fracture attribute slice is, the more developed the fracture is, the post-stack seismic fracture attribute slice is regularized by formula (2): In formula (1) and formula (2), I(x i ) and I norm (x i ) are the i-th attribute point x i Among the attribute values ​​in the post-stack seismic fracture attribute slice and the attribute values ​​in the attribute slice after regularization processing, max[I(x)] is the maximum attribute value in the post-stack seismic fracture attribute slice, and min[I(x)] is the minimum attribute value in the post-stack seismic fracture attribute slice.

3. The method according to claim 1, It is characterized in that The method of convolution with Gaussian function is used to obtain multiple attribute slices of different scales of the processed attribute slices, including: For each scale factor, the attribute slice of the corresponding scale of the processed attribute slice is obtained by the following formula (3): In formula (3), I norm (x i ) and I norm (x i ; σ j ) are the i-th attribute point x i The attribute value and scale factor in the attribute slice after regularization are σ j The attribute value in the attribute slice of G(x), i and j represent the serial number of the attribute point and the serial number of the scale factor respectively; i ; σ j ) is the i-th attribute point x in the attribute slice after regularization i The scale factor is σ j Gaussian function when .

4. The method according to claim 3, It is characterized in that Scale factor σ j satisfy: s j ∈[σ 1 ,s 2 ],and 1 <s 2 (4) In formula (4), σ 1 and σ 2 are the minimum and maximum values ​​of the scale factor, respectively. The minimum value of the scale factor σ 1 The width of the crack in the attribute slice after regularization is preset.

5. The method according to claim 1, It is characterized in that The constructing of the Hessian matrix comprises: The Hessian matrix H(x i ; σ j ): In formula (5), I norm (x i ; σ j ) are the i-th attribute point x i When the scale factor is σ j The attribute value in the attribute slice of , i and j represent the sequence number of the attribute point and the sequence number of the scale factor respectively, x i and i are the i-th attribute point x i The corresponding coordinate values ​​at .

6. The method according to claim 1, It is characterized in that The pre-established filter function is: In formula (6) and formula (7), I norm (x i ; σ j ) and v(x i ; σ j ) are the i-th attribute point x i When the scale factor is σ j The attribute value in the attribute slice and the filtered attribute value, i and j represent the sequence number of the attribute point and the sequence number of the scale factor respectively; Among them, λ 1 is the first eigenvalue, λ 2 is the second eigenvalue; t is the vector scaling factor, e 1x and e 1y are the components of the first eigenvector in the corresponding coordinate directions; i and i are the i-th attribute point x i The corresponding coordinate values ​​at .

7. The method according to any one of claims 1 to 6, It is characterized in that The enhanced attribute slices are obtained based on the principle of maximizing enhancement, including: For each attribute point, the maximum value among the enhanced attribute values ​​corresponding to each scale is determined as the enhanced attribute value of the attribute point to obtain the enhanced attribute slice.

8. A post-stack seismic fracture attribute enhancement device based on multi-scale Hessian matrix, It is characterized in that include: The multi-scale attribute slice establishment module is used to perform regularization processing on the attribute slices of post-stack seismic fractures, and obtain attribute slices of multiple different scales of the attribute slices after regularization processing by convolution with Gaussian function; A multi-scale attribute value determination module is used to: construct a Hessian matrix for each attribute point in the attribute slice of each scale, determine a first eigenvalue, a second eigenvalue and a first eigenvector corresponding to the first eigenvalue of the Hessian matrix, wherein the first eigenvalue is greater than the second eigenvalue, and determine whether the attribute point meets the enhancement condition of the filter function according to the first eigenvalue and the first eigenvector; If yes, determining the enhanced attribute value by the filtering function according to the first eigenvalue and the second eigenvalue, the filtering function being pre-established based on a Frangi filtering function and a Steger filtering function; If not, the enhanced attribute value is determined to be 0; The enhanced attribute slice establishment module is used to obtain the enhanced attribute slice based on the principle of maximizing enhancement.

9. A computer storage medium, It is characterized in that The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix according to any one of claims 1 to 7 is implemented.

10. A server, It is characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the post-stack seismic fracture attribute enhancement method based on a multi-scale Hessian matrix as claimed in any one of claims 1 to 7 is implemented.

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