Crack detection method, device, and computer storage medium

Abstract

The embodiment of the present application discloses a fracture detection method, which belongs to the field of oil exploration technology. The method includes: determining the azimuth and inclination corresponding to the target sampling point according to the multidimensional gradient body corresponding to each seismic data body in N seismic data bodies, and then determining the curvature attribute corresponding to the target sampling point. The embodiment of the present application determines the azimuth and inclination corresponding to the target sampling point by the multidimensional gradient body corresponding to each seismic data body in N seismic data bodies, and then determines the curvature attribute corresponding to the target sampling point. The curvature attribute of the target sampling point is used to reflect the curvature degree of the formation to be detected, and the fracture is detected. Since the N seismic data bodies correspond to the N azimuth intervals respectively, different azimuth intervals can reflect the differences of each azimuth, so the various azimuths around the target sampling point can reflect the differences of each azimuth, so that the fracture in the formation where the target sampling point is located can be accurately detected.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of oil exploration technology, and in particular to a fracture detection method, device, and computer storage medium. Background Art

[0002] In sedimentary rock formations, although carbonate formations only account for about 20%, the oil and gas resources stored in carbonate formations account for more than 50% of the discovered oil and gas resources. It can be inferred that there are many oil and gas resources in rock formations. The place where oil and gas resources are stored in rock formations is called rock reservoir. Rock reservoirs usually exist in the form of fractures and holes, which play an important role in the storage and migration of oil and gas. Therefore, by accurately predicting the fractures and holes in rock formations, we can clearly know the true state of the oil and gas reservoirs in the rock formations, and then we can explore the oil and gas reservoirs in the rock formations to determine the oil and gas resources.

[0003] The curvature attribute can reflect the degree of deformation of the rock formation when it is wrinkled or bent due to pressure, so the curvature attribute can be used to describe the direction and distribution of the fractures and holes in the rock formation. The more severe the deformation of the rock formation due to pressure, the greater the degree of rupture of the fractures and holes in the rock formation, and the greater the corresponding curvature attribute. In the related art, to determine the curvature attribute, it is first necessary to obtain the original data through field exploration and collection, and the original data is also called the original seismic data. Specifically, on the surface, seismic waves are artificially excited. When the seismic waves propagate, they will reflect or refract the seismic waves when they encounter strata with different types of rocks. The reflected or refracted seismic waves can be received by detectors on the surface or in oil and gas wells. The detector receives the seismic waves and displays them in the form of data, which is the original data. Further, these original data are processed by static correction, denoising, offset, superposition, etc., and then the complex channel analysis algorithm, or the discrete dip scanning algorithm, or the gradient structure tensor algorithm is used to determine the dip and azimuth of the rock formation. The determined inclination and azimuth of the rock formation are used to determine the curvature attribute of the rock formation.

[0004] In the above technology, the data after the rock strata are stacked and processed are used to determine the inclination and azimuth of the strata, and then the curvature attribute of the rock strata is obtained. Since the data after the stacking processing is not divided based on the azimuth, that is, the data after the stacking processing cannot reflect the differences in various azimuths around the sampling point. For example, there are differences in the amplitude and frequency of the signals in each azimuth. Therefore, the determined curvature attribute value of the rock strata is inaccurate, and there may be multiple solutions for the curvature attribute value, which in turn affects the detection results of the fractures and holes, resulting in the inability to accurately predict the fractures and holes. Summary of the invention

[0005] The embodiment of the present application provides a fracture hole detection method, which can accurately detect fracture holes in the formation. The technical solution is as follows:

[0006] In a first aspect, a crack hole detection method is provided, the method comprising:

[0007] Acquire N seismic data volumes corresponding to target sampling points in the to-be-detected stratum, wherein the N seismic data volumes correspond to N azimuth intervals respectively, and the seismic data volume corresponding to each azimuth interval in the N azimuth intervals includes a plurality of original data, and the plurality of original data correspond to a plurality of receiving points respectively, and the plurality of receiving points are arranged in corresponding azimuth intervals, and the N azimuth intervals can cover all azimuths around the target sampling point;

[0008] Based on each of the multiple pieces of original data included in any seismic data volume among the N seismic data volumes, determining a multidimensional gradient volume corresponding to the any seismic data volume, the multidimensional gradient volume indicating a change rule of the original data in the any seismic data volume in different dimensions;

[0009] Determine the azimuth and dip corresponding to the target sampling point based on a multidimensional gradient volume corresponding to each of the N seismic data volumes;

[0010] Based on the azimuth and inclination corresponding to the target sampling point, a curvature attribute corresponding to the target sampling point is determined.

[0011] Optionally, the determining the azimuth and dip corresponding to the target sampling point based on the multidimensional gradient volume corresponding to each of the N seismic data volumes comprises:

[0012] Weighting the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes to obtain a multidimensional gradient volume corresponding to the target sampling point;

[0013] Determine the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point;

[0014] Determine a characteristic vector of a gradient tensor of a multidimensional gradient volume corresponding to the target sampling point;

[0015] Based on the eigenvector of the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point, the azimuth and inclination corresponding to the target sampling point are determined.

[0016] Optionally, determining the curvature attribute corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point includes:

[0017] Determine, based on the azimuth and inclination corresponding to the target sampling point, a curved surface of the target sampling point in the azimuth interval corresponding to the target sampling point;

[0018] Based on the curved surface corresponding to the target sampling point, a curvature attribute corresponding to the target sampling point is determined.

[0019] Optionally, the curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and tortuosity.

[0020] In a second aspect, a crack hole detection device is provided, the device comprising:

[0021] An acquisition module, used for acquiring N seismic data volumes corresponding to target sampling points in the to-be-detected stratum, wherein the N seismic data volumes correspond to N azimuth intervals respectively, and the seismic data volume corresponding to each azimuth interval in the N azimuth intervals includes a plurality of original data, and the plurality of original data correspond to a plurality of receiving points respectively, and the plurality of receiving points are arranged in corresponding azimuth intervals, and the N azimuth intervals can cover all azimuths around the target sampling point;

[0022] A determination module, configured to determine, based on each of a plurality of pieces of original data included in any of the N seismic data volumes, a multidimensional gradient volume corresponding to the any seismic data volume, wherein the multidimensional gradient volume indicates a variation rule of the original data in the any seismic data volume in different dimensions;

[0023] The determination module is used to determine the azimuth and inclination corresponding to the target sampling point based on the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes;

[0024] The determination module is used to determine the curvature attribute corresponding to the target sampling point based on the azimuth and inclination angle corresponding to the target sampling point.

[0025] Optionally, the determining module further includes:

[0026] A determination unit, configured to perform weighted processing on a multidimensional gradient volume corresponding to each of the N seismic data volumes to obtain a multidimensional gradient volume corresponding to the target sampling point;

[0027] The determining unit is used to determine the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point;

[0028] The determining unit is used to determine the characteristic vector of the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point;

[0029] The determination unit is used to determine the azimuth and inclination corresponding to the target sampling point based on the eigenvector of the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point.

[0030] Optionally, the determining module further includes:

[0031] A determination unit, configured to determine a curved surface of the target sampling point in the azimuth interval corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point;

[0032] The determining unit is used to determine the curvature attribute corresponding to the target sampling point based on the curved surface of the target sampling point in the azimuth interval corresponding to the target sampling point.

[0033] Optionally, the curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and tortuosity.

[0034] In a third aspect, a computer-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, an oilfield joint station detection method described in the first aspect is implemented.

[0035] According to a fourth aspect, a computer device is provided, the device comprising:

[0036] processor;

[0037] a memory for storing processor-executable instructions;

[0038] Wherein, the processor is configured to execute an oilfield joint station detection method described in the first aspect above.

[0039] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, enables the computer to execute the seam hole detection method described in the first aspect.

[0040] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0041] The azimuth and inclination corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each seismic data body in the N seismic data bodies, and then the curvature attribute corresponding to the target sampling point is determined. The curvature attribute of the target sampling point is used to reflect the curvature of the formation to be detected. Since the N seismic data bodies correspond to the N azimuth intervals respectively, each seismic data body in the N seismic data bodies corresponds to a different azimuth interval, and different azimuth intervals can reflect the differences in each azimuth. Therefore, the curvature of the formation to be detected is determined based on the various azimuths around the target sampling point, and the various azimuths around the target sampling point can reflect the differences in each azimuth, so that the fractures and holes in the formation where the target sampling point is located can be accurately detected. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 This is a flow chart of a crack hole detection method provided in an embodiment of the present application.

[0044] Figure 2 This is a flow chart of a crack hole detection method provided in an embodiment of the present application.

[0045] Figure 3 It is a structural schematic diagram of a crack detection device provided in an embodiment of the present application.

[0046] Figure 4 It is a structural schematic diagram of a crack detection device provided in an embodiment of the present application.

[0047] Figure 5 It is a structural block diagram of a terminal provided in an embodiment of the present application.

[0048] Figure 6 It is a schematic diagram of a server structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0050] For the convenience of subsequent explanation, the application scenarios of the embodiments of the present application are first introduced and explained here.

[0051] The types of oil and gas reservoirs in rock formations include pore type, cave type, fracture type, cave-fracture type combination, etc. The structures that constitute these types are collectively referred to as fractures and holes. The size of fractures and holes is an important indicator for judging the oil and gas production capacity of oil and gas reservoirs in rock formations. Accurate detection of fractures and holes in rock formations can then determine the oil and gas production in oil and gas reservoirs in rock formations.

[0052] The method provided in the embodiment of the present application is applied to the scenario of fracture and hole detection in rock formations.

[0053] The method provided in the embodiment of the present application is further described below. It should be noted that the embodiment of the present application can be performed using devices such as terminals, controllers, and servers. Figure 1 The various steps in the embodiment of the present application are not limited here to the execution subject. Figure 1 The description is given with the terminal as the execution subject.

[0054] Figure 1 : is a flow chart of a crack hole detection method provided in an embodiment of the present application. The crack hole detection method may include the following steps.

[0055] Step 101: The terminal obtains N seismic data volumes corresponding to target sampling points in the stratum to be detected.

[0056] In order to facilitate the determination of the curvature attribute of the formation to be detected, the terminal needs to select a target sampling point and calculate the curvature attribute of the target sampling point to reflect the curvature degree of the formation to be detected. The terminal determines the target sampling point based on multiple sampling points in the formation to be detected. Then the terminal obtains N seismic data bodies corresponding to the target sampling point in the formation to be detected. Among them, the formation to be detected is a rock formation. The target sampling point is one of the multiple sampling points in the formation to be detected. N is greater than or equal to 3. In actual operation, the more N, the better. N is usually in the range of 5 to 8.

[0057] The above-mentioned implementation method of determining the target sampling point from the multiple sampling points is as follows: the stratum to be detected and the multiple sampling points of the stratum to be detected are displayed on the display interface of the terminal. The user selects one of the multiple sampling points on the display interface of the terminal based on a preset operation, and at this time, the terminal detects the user's selection instruction based on one of the multiple sampling points, and then the terminal determines the target sampling point in response to the selection instruction.

[0058] It should be noted that the above-mentioned preset operations can be operations such as clicking, sliding or voice. The embodiments of the present application do not limit the preset operations and will not be described one by one here.

[0059] The above-mentioned N seismic data volumes are divided in the following way: all azimuths around the target sampling point are divided into N azimuth intervals. All azimuths around the target sampling point indicate that the target sampling point is mapped on the surface, with the mapping point as the center of the circle, the surface as the plane, and 0 to 360° around the center of the circle. That is, 0 to 360° around the mapping point corresponding to the target sampling point is divided into N azimuth intervals. N seismic data volumes correspond to N azimuth intervals respectively. Each azimuth interval corresponds to a seismic data volume, and N azimuth intervals have N seismic data volumes.

[0060] The N azimuth intervals may be obtained by equally dividing 0 to 360 degrees around the mapping point corresponding to the target sampling point into the N azimuth intervals, or by arbitrarily dividing 0 to 360 degrees around the mapping point corresponding to the target sampling point into the N azimuth intervals.

[0061] The above-mentioned implementation method of obtaining a seismic data volume corresponding to each azimuth interval is: select multiple receiving points in each azimuth interval, that is, multiple receiving points are arranged in the corresponding azimuth interval. Among them, there are different shot distances between multiple receiving points. In order to enable multiple receiving points to receive the wave signal of the target sampling point, multiple excitation points are arranged at 0 to 360° around the mapping point corresponding to the target sampling point, and the multiple excitation points correspond to multiple receiving points one by one. The connecting line between each receiving point and the corresponding excitation point passes through the mapping point corresponding to the target sampling point. A detector is placed at each receiving point to receive the wave signal reflected by the target sampling point. When any excitation point is excited, the detector at the corresponding receiving point will receive the wave signal reflected by the target sampling point. The detector displays the received wave signal in the form of data, which is the original data received by the receiving point. Multiple original data correspond to multiple receiving points respectively, and multiple receiving points will receive multiple original data. The multiple original data in each azimuth interval are called the seismic data volume corresponding to each azimuth interval.

[0062] It should be noted that the above-mentioned methods of exciting any excitation point include well gun excitation, vehicle impacting the ground excitation, air gun excitation, etc. The method of exciting any excitation point is not limited in the embodiments of the present application, and examples are not given one by one here.

[0063] In addition, after the terminal obtains N seismic data volumes corresponding to the target sampling points in the to-be-detected stratum, any of the N seismic data volumes executes the following steps 102, 103, and 104 to obtain curvature attributes corresponding to the N azimuth intervals. For ease of explanation, the first seismic data volume will be taken as an example for explanation.

[0064] Step 102: The terminal determines a multidimensional gradient volume corresponding to the first seismic data volume based on each of the multiple original data included in the first seismic data volume among the N seismic data volumes.

[0065] In one possible implementation, since each of the multiple original data includes three-dimensional data volumes in three directions: inline, xline, and time, the terminal determines the three-dimensional data volume of the first seismic data volume based on the three-dimensional data volume of each of the multiple original data included in the first seismic data volume, and determines the multidimensional gradient volume corresponding to the first seismic data volume based on the three-dimensional data volume of the first seismic data volume.

[0066] The above-mentioned terminal determines the three-dimensional data volume of the first seismic data volume based on the three-dimensional data volume of each of the multiple original data included in the first seismic data volume by weighting the three-dimensional data volume of each of the multiple original data to obtain the three-dimensional data volume of the first seismic data volume.

[0067] Specifically, weighted processing is performed on the data along the line direction of each of the multiple original data, weighted processing is performed on the data along the track direction of each of the multiple original data, and weighted processing is performed on the data along the time direction of each of the multiple original data. Weighted processing is performed along the three directions of line, track and time to obtain a three-dimensional data volume of the first seismic data volume.

[0068] For example, the three data bodies of line, track, and time of any of the multiple original data are represented by (p, q, r). The first seismic data body includes 3 original data, the three-dimensional data body corresponding to the first original data is (p1, q1, r 1), the three-dimensional data body corresponding to the second original data is (p2, q2, r 2), and the three-dimensional data body corresponding to the third original data is (p3, q3, r 3). According to the weight of each original data, each original data is weighted. If the weight of the first original data is Q1, the weight of the second original data is Q2, and the weight of the third original data is Q3, then the result of the weighted processing is (Q1p1+Q2p2+Q3r 3, Q1q1+Q2q2+Q3 r 3, Q1q1+Q2q2+Q3 r 3). Then the result of the weighted processing is the three-dimensional data body of the first seismic data body. Here, the result of the weighted processing is represented by (p, q, r).

[0069] The above-mentioned three-dimensional data volume based on the first seismic data volume determines the multidimensional gradient volume corresponding to the first seismic data volume in the following way: determine the multidimensional gradient volume along the three dimensions of line, track and time of the three-dimensional data volume of the first seismic data volume, and the multidimensional gradient volume is also called a three-dimensional gradient volume. Here, the multidimensional gradient volume along the three dimensions of line, track and time of the first seismic data volume is represented by (x, y, z).

[0070] As shown in the following formula 1, formula 1 is a multi-dimensional gradient body of the first seismic data body, represented by g1. Represents a three-dimensional gradient volume of (x, y, z). Indicates the derivative of x, and obtains the gradient path g1 along the line direction x , Indicates the derivative of y, and obtains the gradient g1 along the path direction y . Indicates the derivative of z, and obtains the gradient path g1 along the time direction z . Final g1 x 、g1 y 、g1 z A multidimensional gradient body.

[0071] Formula 1:

[0072]

[0073] Since there are N seismic data volumes, the multidimensional gradient volume gn of the Nth seismic data volume is expressed as the following formula 2.

[0074] Formula 2:

[0075]

[0076] The above g1 x 、g1 y 、g1 z It can be determined by convolution. As shown in Formula 3, in Formula 3, G′(t) is the derivative of the one-dimensional zero-mean discrete Gaussian kernel function, t is a discrete variable, and the value range of t is [-R i , +R i ], R i 2 =4 2 σ i , R i is the nuclear radius, σ i is the predetermined scale factor, i∈{x,y,z}.

[0077] Formula 3:

[0078]

[0079] If i is x, σ x is the predetermined scale factor corresponding to the line direction, R x is the kernel radius corresponding to the line direction, that is, in formula 3, the data along the line direction is convolved with the convolution kernel to obtain the gradient trace g1 along the line direction of the first seismic data volume x . If i is y, σ y is the predetermined scale factor corresponding to the track direction, R y is the kernel radius corresponding to the trace direction, that is, in formula 3, the data along the trace direction is convolved with the convolution kernel to obtain the gradient trace g1 along the line direction of the first seismic data volume y . If i is z, σ z is the predetermined scale factor corresponding to the time direction, R z is the kernel radius corresponding to the time direction, that is, in formula 3, the data along the time direction is convolved with the convolution kernel to obtain the gradient path g1 along the line direction of the first seismic data volume z The larger the value of σ, the wider the frequency band of the Gaussian filter and the better the smoothing effect. The smaller the value of σ, the worse the smoothing effect, but the better the details. Generally, the window length of the Gaussian filter is 5-9 discrete points.

[0080] It should be noted that the method for determining the multidimensional gradient volume corresponding to other seismic data volumes among the N seismic data volumes is the same as that for the first seismic data volume, and how to determine the multidimensional gradient volume corresponding to each seismic data volume among the N seismic data volumes will not be explained here.

[0081] Step 103: The terminal determines the azimuth and inclination corresponding to the target sampling point based on the multidimensional gradient volume corresponding to each of the N seismic data volumes.

[0082] The above-mentioned terminal determines the azimuth and inclination corresponding to the target sampling point based on the multidimensional gradient body corresponding to each of the N seismic data bodies in the following way: weighted processing is performed on the multidimensional gradient body corresponding to each of the N seismic data bodies to obtain the multidimensional gradient body corresponding to the target sampling point, and then the gradient tensor of the multidimensional gradient body corresponding to the target sampling point is determined, and then the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point is determined; based on the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point, the azimuth and inclination corresponding to the target sampling point are determined.

[0083] The above-mentioned multidimensional gradient volume corresponding to each seismic data volume of N seismic data volumes is weighted and processed to obtain the multidimensional gradient volume corresponding to the target sampling point by weighted addition of the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes to obtain a gradient track along the line (x) direction, a gradient track along the track (y) direction, and a gradient track along the time (z) direction. These three gradient tracks are called the multidimensional gradient volume corresponding to the target sampling point.

[0084] As shown in Formula 4, in Formula 4, g represents the multidimensional gradient body corresponding to the target sampling point. Represents the multidimensional gradient volume in the three directions of line, track and time along the target sampling point. w1, ..., wn represents the weight of the first seismic data volume among N seismic data volumes. g1 x ,……,gn x Represents the multidimensional gradient volume corresponding to all seismic data volumes along the line direction, g1 y ,……,gn y Represents the multidimensional gradient volume corresponding to all seismic data volumes along the track direction, g1 z ,……,gn z Represents the multidimensional gradient volume corresponding to all seismic data volumes along the time direction. x It represents the final gradient trace after weighting the multi-dimensional gradient volume corresponding to all seismic data volumes along the line, g y It represents the final gradient trace after weighting the multi-dimensional gradient volume corresponding to all seismic data volumes along the trace direction, g z Represents the final gradient trace after weighting the multidimensional gradient volume corresponding to all seismic data volumes along the time direction. g1x 、g1 y 、g1 z is the multidimensional gradient volume corresponding to the target sampling point. It can also be called a multidimensional gradient volume corresponding to N seismic data volumes.

[0085] Formula 4:

[0086]

[0087] It should be noted that the weight of the initial multidimensional gradient body corresponding to the above can be given by human experience. The weight can also be given on the basis of post-stack fracture hole detection QC (quantity control). The way to give the weight on the basis of QC is: if the fracture hole at a certain receiving point is relatively developed, that is, the fracture hole feature is very clear, then the weight of the initial multidimensional gradient body corresponding to the original data of the receiving point is large, thereby obtaining more detailed fracture hole data.

[0088] The above terminal determines the gradient tensor of the multidimensional gradient body corresponding to the target sampling point in the following manner: As shown in Formula 5, in Formula 5, T represents the gradient tensor, the gradient tensor is represented in the form of a matrix, and the row and column position of each element in the matrix is ​​represented by T ij Indicates. 13 The terminal determines the transposed matrix g of the matrix corresponding to g according to the multidimensional gradient volume g corresponding to the target sampling point determined in step 103. T The corresponding matrix of g is multiplied by the device matrix to obtain T.

[0089] Formula 5:

[0090]

[0091] The above-mentioned method for determining the eigenvector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point is as follows: the gradient tensor of the multidimensional gradient body corresponding to the target sampling point is subjected to eigendecomposition to obtain the eigenvector, as shown in Formula 6, where Λ represents a diagonal matrix, and the values ​​on the diagonal are eigenvalues, which are λ1, λ2, and λ3, respectively, and they are arranged from large to small. v represents an eigenvector matrix, and the column vector represents the eigenvector corresponding to the above eigenvalues, which are v1, v2, and v3, respectively.

[0092] Formula 6:

[0093] Tv=Λv

[0094] The terminal determines the azimuth and inclination corresponding to the target sampling point based on the eigenvector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point by using the eigenvector corresponding to the maximum eigenvalue to determine the azimuth and inclination. Specifically, assuming that the maximum eigenvalue is λ1, the eigenvector corresponding to λ1 is v1(x, y, z), and v1(x, y, z) is decomposed in the x direction, y direction, and z direction respectively, to obtain three elements v 1x (x, y, z), v 1y (x, y, z), v 1z (x, y, z), using these three elements, we can get the inclination angle v1(1) and azimuth angle v1(2). Specifically, it can be shown as follows:

[0095]

[0096]

[0097] It should be noted that there may be other ways to determine the azimuth and inclination, which are not limited in the embodiments of the present application.

[0098] In addition, in order to magnify the difference between the inclination and azimuth, after determining the azimuth and inclination, the inclination and azimuth need to be enhanced respectively. The inclination after enhancement is p = sign (v1 (1)) * v1 (1) * v1 (1). The azimuth after enhancement is q = sign (v1 (2)) * v1 (2) * v1 (2).

[0099] Step 104: The terminal determines the curvature attribute corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point.

[0100] The terminal determines the curved surface corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point, and further determines the curvature attribute corresponding to the target sampling point based on the curved surface corresponding to the target sampling point.

[0101] The above terminal determines the surface corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point in the following manner: The quadratic surface is usually represented by f(x, y) = ax 2 +by 2 +cxy+dx+ey+f, a, b, c, d, e can be determined by the enhanced inclination and azimuth. Specifically, d=p, e=q, f=f(0,0), where p represents the enhanced inclination angle, and q represents the enhanced azimuth angle.

[0102] The above-mentioned method of determining the curvature attribute corresponding to the target sampling point based on the surface corresponding to the target sampling point is as follows: taking the first-order derivative of the quadratic surface f(x, y), and then combining various curvature calculation formulas, the curvature attributes of the target sampling point at different wave numbers and different scales can be extracted.

[0103] The first-order derivative of the quadratic surface f(x, y) is implemented as follows: according to the method for determining the partial wave number derivative, the first-order derivative of the quadratic surface f(x, y) is performed. The method for determining the partial wave number derivative is shown in Formula 7, where F represents Fourier transform, k x is the wave number, T(k x ) is the raised cosine function. α is the wave number coefficient. For different α values, the filter output result is determined according to formula 7, and the result is subjected to inverse Fourier transform to obtain the wave number derivative. Then a, b, c, d, and e can also be obtained by this method.

[0104] Formula 7:

[0105]

[0106] It should be noted that α is usually defined by the user, and the value range of α is usually 0.25-1. The larger the value, the more details; the smaller the value, the better the smoothness, and the details are blurred. The user needs to balance the α value himself.

[0107] In addition, curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and curvature. Different curvature attributes are calculated in different ways, and different curvature calculation formulas need to be characterized by the parameters a, b, c, d, and e in the surface equation, as follows:

[0108] 1. Average curvature: This curvature is controlled by the maximum curvature and looks similar to it.

[0109] 2. Gaussian curvature: Gaussian curvature cannot be used alone, and mean curvature information is also needed to assist.

[0110] 3. Maximum curvature: The stratum to be detected is represented by the adjacency of positive curvature values ​​and negative curvature values. The curvature value determines the fault direction of the stratum to be detected. The positive curvature value represents the rise of the fault, and the negative curvature value represents the fall of the fault.

[0111] 4. Minimum curvature: When the minimum curvature is very small or zero, the stratum to be detected is a plane surface; when the minimum curvature is very large, it means that the stratum to be detected has undergone non-equidistant distortion, that is, the surface to be detected may be dislocated and fractured, thereby determining the fracture zone.

[0112] 5. Maximum positive curvature: This curvature method can detect fault information and some small linear structures in the formation.

[0113] 6. Minimum negative curvature: Can determine the surface characteristics.

[0114] 7. Morphological index: Combining the minimum curvature and the maximum curvature to obtain the morphological parameters can describe the local morphology of the level that is independent of scale.

[0115] 8. Inclination curvature: The curvature contains the size and orientation information of the fault layer to be detected.

[0116] 9. Strike curvature: Describes the morphology of the underlying layer to be detected.

[0117] 10. Contour curvature: Particularly large values ​​will appear in places such as anticlines and ridges.

[0118] 11. Curvature: It indicates the curvature of the stratum to be detected, which is independent of its morphology.

[0119] It should be noted that when underground fractures and caves develop, the wave signals received by each receiving point in different azimuth intervals will change to varying degrees, and there will be great differences in travel time, amplitude, frequency and phase, and the calculated curvature properties will also be different.

[0120] By determining the curvature attribute corresponding to the target sampling point, the curvature degree of the stratum to be detected where the target sampling point is located can be reflected. Since the target sampling point has N azimuth intervals, the curvature attribute of the target sampling point can reflect different azimuths. Therefore, the curvature degree of the stratum to be detected can reflect the difference in azimuths, thereby accurately detecting the fractures and holes in the stratum where the target sampling point is located.

[0121] Figure 2 This is an effect diagram of the identification of fractures, karst pores and caves in a three-dimensional work area in the Sichuan Basin using the method in the embodiment of the present application. Figure 2 As shown, Figure 2 This is a fracture-cave schematic diagram provided in the real-time example of this application. In addition to the fine depiction of the fracture, the circular and elliptical black anomalies are interpreted as caves, and their correctness has also been verified by actual drilling.

[0122] In summary, in the real-time example of the present application, the azimuth and inclination corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each seismic data body in the N seismic data bodies, and then the curvature attribute corresponding to the target sampling point is determined. The curvature attribute of the target sampling point is used to reflect the curvature degree of the stratum to be detected. Since the N seismic data bodies correspond to the N azimuth intervals respectively, each seismic data body in the N seismic data bodies corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of each azimuth. Therefore, the curvature degree of the stratum to be detected is determined based on the various azimuths around the target sampling point, and the various azimuths around the target sampling point can reflect the differences of each azimuth, so that the fractures and holes in the stratum where the target sampling point is located can be accurately detected.

[0123] Below Figure 3 The method provided in the embodiment of the present application is further explained by taking as an example. It should be noted that: Figure 3 The embodiment shown is only the aforementioned Figure 1 Some optional technical solutions in the illustrated embodiments do not constitute a limitation on the crack hole detection method provided in the embodiments of the present application.

[0124] 1. Read the three-dimensional seismic data of each azimuth angle, where each azimuth angle is N azimuth intervals. That is, the terminal obtains N seismic data volumes corresponding to the target sampling points in the stratum to be detected, and each seismic data volume includes the corresponding three-dimensional seismic data volume in the three directions of line, track, and time of each of the multiple original data.

[0125] 2. Determine the gradient of each line, track, and time direction of each orientation data volume. Each orientation corresponds to N seismic data volumes, so that is, determine the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes.

[0126] 3. Perform weighted addition on the multidimensional gradient bodies corresponding to each of the N seismic data bodies to obtain a gradient track along the line (x) direction, a gradient track along the track (y) direction, and a gradient track along the time (z) direction. These three gradient tracks are called the multidimensional gradient body corresponding to the target sampling point. Among them, the multidimensional gradient body corresponding to the target sampling point is called the final gradient of each direction interval corresponding to the target sampling point.

[0127] 4. Extract each receiving point in the target sampling point in turn to determine the gradient tensor of the multidimensional gradient body corresponding to the target sampling point. The gradient tensor is the constructed covariance matrix.

[0128] 5. Perform eigenvalue decomposition on the gradient tensor to determine the eigenvector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point.

[0129] 6. The terminal determines the azimuth and inclination based on the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point, and enhances the inclination and azimuth respectively.

[0130] 7. Determine the curvature attribute corresponding to the target sampling point.

[0131] 8. Determine whether the curvature attribute corresponding to the target sampling point has been determined. If not, continue to determine the gradient tensor of the multidimensional gradient body corresponding to the target sampling point. If yes, determine whether the curvature attribute of the target sampling point has been determined. If the curvature attribute corresponding to the target sampling point has not been determined, it means that the gradients of all seismic data bodies along the line, track, and time directions in the N seismic data bodies have not been determined, and continue to determine the gradients of each seismic data body along the line, track, and time directions in each orientation data body. If the curvature attribute corresponding to the target sampling point has been determined, output the curvature attribute corresponding to the target sampling point.

[0132] In summary, in the real-time example of the present application, the azimuth and inclination corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each seismic data body in the N seismic data bodies, and then the curvature attribute corresponding to the target sampling point is determined. The curvature attribute of the target sampling point is used to reflect the curvature degree of the stratum to be detected. Since the N seismic data bodies correspond to the N azimuth intervals respectively, each seismic data body in the N seismic data bodies corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of each azimuth. Therefore, the curvature degree of the stratum to be detected is determined based on the various azimuths around the target sampling point, and the various azimuths around the target sampling point can reflect the differences of each azimuth, so that the fractures and holes in the stratum where the target sampling point is located can be accurately detected.

[0133] Figure 4 1 is a schematic diagram of a crack detection device provided in an embodiment of the present application. The crack detection device can be implemented by software, hardware or a combination of both. The crack detection device 400 can include: a determination module 101 and a processing module 102.

[0134] An acquisition module is used to acquire N seismic data volumes corresponding to a target sampling point in a to-be-detected stratum, wherein the N seismic data volumes correspond to N azimuth intervals respectively, and the seismic data volume corresponding to each azimuth interval in the N azimuth intervals includes a plurality of original data, and the plurality of original data correspond to a plurality of receiving points respectively, and the plurality of receiving points are arranged in the corresponding azimuth intervals, and the N azimuth intervals can cover all azimuths around the target sampling point;

[0135] A determination module, configured to determine, based on each of a plurality of pieces of original data included in a first seismic data volume among the N seismic data volumes, a multidimensional gradient volume corresponding to the first seismic data volume, wherein the multidimensional gradient volume indicates a change rule of the original data in the first seismic data volume in different dimensions;

[0136] A determination module, for determining an azimuth and an inclination corresponding to a target sampling point based on a multidimensional gradient volume corresponding to each of the N seismic data volumes;

[0137] The determination module is used to determine the curvature attribute corresponding to the target sampling point based on the azimuth and inclination angle corresponding to the target sampling point.

[0138] Optionally, the determination module further includes:

[0139] A determination unit, used for weighting the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes to obtain a multidimensional gradient volume corresponding to the target sampling point;

[0140] A determination unit, used to determine the gradient tensor of the multidimensional gradient body corresponding to the target sampling point;

[0141] A determination unit, used to determine a characteristic vector of a gradient tensor of a multidimensional gradient body corresponding to a target sampling point;

[0142] The determination unit is used to determine the azimuth and inclination corresponding to the target sampling point based on the characteristic vector of the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point.

[0143] Optionally, the determination module further includes:

[0144] A determination unit, configured to determine a curved surface of the target sampling point on the azimuth interval corresponding to the target sampling point based on the azimuth and inclination corresponding to the target sampling point;

[0145] The determining unit is used to determine the curvature attribute corresponding to the target sampling point based on the curved surface of the target sampling point in the azimuth interval corresponding to the target sampling point.

[0146] Optionally, the curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and tortuosity.

[0147] In summary, in the embodiment of the present application, the azimuth and inclination corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each seismic data body in the N seismic data bodies, and then the curvature attribute corresponding to the target sampling point is determined. The curvature attribute of the target sampling point is used to reflect the curvature degree of the formation to be detected. Since the N seismic data bodies correspond to the N azimuth intervals respectively, each seismic data body in the N seismic data bodies corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of each azimuth. Therefore, the curvature degree of the formation to be detected is determined based on the various azimuths around the target sampling point, and the various azimuths around the target sampling point can reflect the differences of each azimuth, so that the fractures and holes in the formation where the target sampling point is located can be accurately detected.

[0148] It should be noted that: the crack hole detection device provided in the above embodiment only uses the division of the above functional modules as an example when performing crack hole detection. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the terminal is divided into different functional modules to complete all or part of the functions described above. In addition, the crack hole detection device provided in the above embodiment and the crack hole detection method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0149] Figure 5 The structure block diagram of a terminal 500 provided by an exemplary embodiment of the present application is shown. The terminal 500 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4), a laptop computer or a desktop computer. The terminal 500 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0150] Typically, the terminal 500 includes a processor 501 and a memory 502 .

[0151] The processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 501 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 501 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 501 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0152] The memory 502 may include one or more computer-readable storage media, which may be non-transitory. The memory 502 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 502 is used to store at least one instruction, which is used to be executed by the processor 501 to implement the seam hole detection method provided in the method embodiment of the present application.

[0153] In some embodiments, the terminal 500 may also optionally include: a peripheral device interface 503 and at least one peripheral device. The processor 501, the memory 502 and the peripheral device interface 503 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 503 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 504, a display screen 505, a camera assembly 506, an audio circuit 507, a positioning assembly 508 and a power supply 509.

[0154] The peripheral device interface 503 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 501 and the memory 502. In some embodiments, the processor 501, the memory 502, and the peripheral device interface 503 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 501, the memory 502, and the peripheral device interface 503 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0155] The radio frequency circuit 504 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 504 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 504 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 504 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 504 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: a metropolitan area network, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 504 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0156] The display screen 505 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 505 is a touch display screen, the display screen 505 also has the ability to collect touch signals on the surface or above the surface of the display screen 505. The touch signal can be input to the processor 501 as a control signal for processing. At this time, the display screen 505 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 505 can be one, and the front panel of the terminal 500 is set; in other embodiments, the display screen 505 can be at least two, which are respectively set on different surfaces of the terminal 500 or are folded; in other embodiments, the display screen 505 can be a flexible display screen, which is set on the curved surface or folded surface of the terminal 500. Even, the display screen 505 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 505 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0157] The camera assembly 506 is used to capture images or videos. Optionally, the camera assembly 506 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal, and the rear camera is arranged on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, a wide-angle camera, and a telephoto camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 506 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0158] The audio circuit 507 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 501 for processing, or input them into the radio frequency circuit 504 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 500. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 501 or the radio frequency circuit 504 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 507 may also include a headphone jack.

[0159] The positioning component 508 is used to locate the current geographical location of the terminal 500 to implement navigation or LBS (Location Based Service). The positioning component 508 can be a positioning component based on the US GPS (Global Positioning System), China's Beidou system, Russia's Grenas system or the European Union's Galileo system.

[0160] The power supply 509 is used to power various components in the terminal 500. The power supply 509 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 509 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0161] In some embodiments, the terminal 500 further includes one or more sensors 510 , including but not limited to: an acceleration sensor 511 , a gyroscope sensor 512 , a pressure sensor 513 , a fingerprint sensor 514 , an optical sensor 515 , and a proximity sensor 516 .

[0162] The acceleration sensor 511 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal 500. For example, the acceleration sensor 511 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 501 can control the display screen 505 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 511. The acceleration sensor 511 can also be used to collect game or user motion data.

[0163] The gyro sensor 512 can detect the body direction and rotation angle of the terminal 500, and the gyro sensor 512 can cooperate with the acceleration sensor 511 to collect the user's 3D actions on the terminal 500. The processor 501 can implement the following functions based on the data collected by the gyro sensor 512: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0164] The pressure sensor 513 can be set on the side frame of the terminal 500 and / or the lower layer of the display screen 505. When the pressure sensor 513 is set on the side frame of the terminal 500, it can detect the user's holding signal of the terminal 500, and the processor 501 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 513. When the pressure sensor 513 is set on the lower layer of the display screen 505, the processor 501 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 505. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0165] The fingerprint sensor 514 is used to collect the user's fingerprint, and the processor 501 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 514, or the fingerprint sensor 514 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as a trusted identity, the processor 501 authorizes the user to perform relevant sensitive operations, which include unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings. The fingerprint sensor 514 can be set on the front, back, or side of the terminal 500. When a physical button or a manufacturer logo is set on the terminal 500, the fingerprint sensor 514 can be integrated with the physical button or the manufacturer logo.

[0166] The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 can control the display brightness of the display screen 505 according to the ambient light intensity collected by the optical sensor 515. Specifically, when the ambient light intensity is high, the display brightness of the display screen 505 is increased; when the ambient light intensity is low, the display brightness of the display screen 505 is reduced. In another embodiment, the processor 501 can also dynamically adjust the shooting parameters of the camera component 506 according to the ambient light intensity collected by the optical sensor 515.

[0167] The proximity sensor 516, also called a distance sensor, is usually disposed on the front panel of the terminal 500. The proximity sensor 516 is used to collect the distance between the user and the front of the terminal 500. In one embodiment, when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually decreasing, the processor 501 controls the display screen 505 to switch from the screen-on state to the screen-off state; when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually increasing, the processor 501 controls the display screen 505 to switch from the screen-off state to the screen-on state.

[0168] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the terminal 500, and the terminal 500 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0169] The embodiment of the present application also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the terminal, the terminal can execute the seam hole detection method provided in the above embodiment.

[0170] The embodiment of the present application also provides a computer program product including instructions, which, when executed on a terminal, enables the terminal to execute the hole detection method provided in the above embodiment.

[0171] Figure 6 Schematic diagram of a server structure provided in an embodiment of the present application. The server may be a server in a backend server cluster. Specifically:

[0172] The server 600 includes a central processing unit (CPU) 601, a system memory 604 including a random access memory (RAM) 602 and a read-only memory (ROM) 603, and a system bus 605 connecting the system memory 604 and the central processing unit 601. The server 600 also includes a basic input / output system (I / O system) 606 that helps transfer information between various components in the computer, and a large-capacity storage device 607 for storing an operating system 613, application programs 614, and other program modules 615.

[0173] The basic input / output system 606 includes a display 608 for displaying information and an input device 609 such as a mouse and a keyboard for user inputting information. The display 608 and the input device 609 are connected to the central processing unit 601 through an input / output controller 610 connected to the system bus 605. The basic input / output system 606 may also include an input / output controller 610 for receiving and processing inputs from a plurality of other devices such as a keyboard, a mouse, or an electronic stylus. Similarly, the input / output controller 610 also provides output to a display screen, a printer, or other types of output devices.

[0174] The mass storage device 607 is connected to the central processing unit 601 through a mass storage controller (not shown) connected to the system bus 605. The mass storage device 607 and its associated computer-readable media provide non-volatile storage for the server 600. That is, the mass storage device 607 may include a computer-readable medium (not shown) such as a hard disk or a CD-ROM drive.

[0175] Without loss of generality, computer readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, cassettes, magnetic tapes, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media are not limited to the above. The above-mentioned system memory 604 and mass storage device 607 can be collectively referred to as memory.

[0176] According to various embodiments of the present application, the server 600 can also be connected to a remote computer on the network through a network such as the Internet. That is, the server 600 can be connected to the network 612 through the network interface unit 611 connected to the system bus 605, or the network interface unit 611 can be used to connect to other types of networks or remote computer systems (not shown).

[0177] The memory also includes one or more programs, which are stored in the memory and configured to be executed by the CPU. The one or more programs include instructions for performing the seam hole detection method provided in the embodiment of the present application.

[0178] The embodiment of the present application also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the server, the server can execute the seam hole detection method provided in the above embodiment.

[0179] The embodiment of the present application also provides a computer program product including instructions, which, when executed on a server, enables the server to execute the crack hole detection method provided in the above embodiment.

[0180] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0181] The above description is only a preferred embodiment of the embodiments of the present application and is not intended to limit the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A crack hole detection method, characterized in that: The method comprises: Acquire N seismic data volumes corresponding to target sampling points in a to-be-detected stratum, wherein the N seismic data volumes correspond to N azimuth intervals respectively, and the seismic data volume corresponding to each azimuth interval in the N azimuth intervals includes a plurality of original data, and the plurality of original data correspond to a plurality of receiving points respectively, and the plurality of receiving points are arranged in corresponding azimuth intervals, and the plurality of receiving points have different offsets, and the N azimuth intervals can cover all azimuths around the target sampling point; Based on the three-dimensional data volume of each of the multiple original data included in any one of the N seismic data volumes, the three-dimensional data volume of the any seismic data volume is determined, and based on the three-dimensional data volume of the any one of the seismic data volumes, the multidimensional gradient volume corresponding to the any one of the seismic data volumes is determined, and the multidimensional gradient volume indicates the change law of the original data in the any one of the seismic data volumes in different dimensions; wherein, based on the three-dimensional data volume of each of the multiple original data included in any one of the N seismic data volumes, the three-dimensional data volume of the any one of the seismic data volumes is determined, including: weighting the data of each of the multiple original data along the line direction, weighting the data of each of the multiple original data along the track direction, and weighting the data of each of the multiple original data along the time direction, to obtain the three-dimensional data volume of any one of the seismic data volumes; Weighting the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes to obtain a multidimensional gradient volume corresponding to the target sampling point; Determine the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point; Determine a characteristic vector of a gradient tensor of a multidimensional gradient volume corresponding to the target sampling point; Determine the azimuth and inclination corresponding to the target sampling point based on the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point; The inclination angle and azimuth angle are enhanced respectively. The inclination angle after enhancement is p=sign(v1(1))*v1(1)*v1(1), and the azimuth angle after enhancement is q=sign(v1(2))*v1(2)*v1(2), where v1(1) is the inclination angle and v1(2) is the azimuth angle. Based on the enhanced azimuth and inclination corresponding to the target sampling point, determine the surface corresponding to the target sampling point; based on the surface corresponding to the target sampling point, determine the curvature attribute corresponding to the target sampling point, wherein the determining the curvature attribute corresponding to the target sampling point based on the surface corresponding to the target sampling point includes: taking a first-order derivative of the quadratic surface f(x, y), and then combining the curvature calculation formula to obtain the curvature attribute of the target sampling point at different wave numbers and different scales, and the quadratic surface is expressed as f(x, y)=ax 2 +by 2 +cxy+dx+ey+f, d=p,e=q,f=f(0,0), a, b, c, d, e can be determined by the enhanced inclination and azimuth, p represents the enhanced inclination, q represents the enhanced azimuth; The first-order derivative of the quadratic surface f(x, y) is realized by: performing the first-order derivative of the quadratic surface f(x, y) according to the determination method of the partial wave number derivative, and the determination method of the partial wave number derivative is shown in Formula 7, where F represents Fourier transform, k x is the wave number, T(k x ) is the raised cosine function, α is the wave number coefficient, for different α values, the filtering output result is determined according to formula 7, and the result is subjected to inverse Fourier transform to obtain the wave number derivative; Formula 7: Among them, α is defined by the user, and the value range of α is 0.25-1.

2. The method according to claim 1, characterized in that The curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and tortuosity.

3. A crack hole detection device, characterized in that: The device comprises: An acquisition module, used for acquiring N seismic data volumes corresponding to target sampling points in a to-be-detected stratum, wherein the N seismic data volumes correspond to N azimuth intervals respectively, and the seismic data volume corresponding to each azimuth interval in the N azimuth intervals includes a plurality of original data, and the plurality of original data correspond to a plurality of receiving points respectively, and the plurality of receiving points are arranged in corresponding azimuth intervals, and the plurality of receiving points have different shot offsets, and the N azimuth intervals can cover all azimuths around the target sampling point; A determination module, configured to determine a three-dimensional data volume of any seismic data volume among the N seismic data volumes based on a three-dimensional data volume of each of a plurality of original data included in the plurality of original data volumes, and to determine a multidimensional gradient volume corresponding to the any seismic data volume based on the three-dimensional data volume of the any seismic data, wherein the multidimensional gradient volume indicates a change rule of the original data in the any seismic data volume in different dimensions; The determination module is used to perform weighted processing on the data along the line direction of each of the multiple original data, perform weighted processing on the data along the track direction of each of the multiple original data, and perform weighted processing on the data along the time direction of each of the multiple original data to obtain a three-dimensional data volume of any seismic data volume; The determination module is used to determine the azimuth and inclination corresponding to the target sampling point based on the multidimensional gradient volume corresponding to each seismic data volume in the N seismic data volumes; The determination module is used to determine the curvature attribute corresponding to the target sampling point based on the azimuth and inclination angle corresponding to the target sampling point; The determining module further includes: A determination unit, configured to perform weighted processing on a multidimensional gradient volume corresponding to each of the N seismic data volumes to obtain a multidimensional gradient volume corresponding to the target sampling point; The determining unit is used to determine the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point; The determining unit is used to determine the characteristic vector of the gradient tensor of the multidimensional gradient volume corresponding to the target sampling point; The determination unit is used to determine the azimuth and inclination corresponding to the target sampling point based on the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point; enhance the inclination and azimuth respectively, the inclination after enhancement is p=sign(v1(1))*v1(1)*v1(1), and the azimuth after enhancement is q=sign(v1(2))*v1(2)*v1(2), wherein v1(1) is the inclination and v1(2) is the azimuth; The determining module further includes: A determination unit, configured to determine a curved surface of the target sampling point in the azimuth interval corresponding to the target sampling point based on the azimuth and inclination angle corresponding to the target sampling point after enhancement processing; The determining unit is used to determine the curvature attribute corresponding to the target sampling point based on the surface corresponding to the target sampling point; wherein, the determining the curvature attribute corresponding to the target sampling point based on the surface corresponding to the target sampling point includes: taking a first-order derivative of the quadratic surface f(x, y), and then combining the curvature calculation formula to obtain the curvature attribute of the target sampling point at different wave numbers and different scales, and the quadratic surface is expressed as f(x, y)=ax 2 +by 2 +cxy+dx+ey+f, d=p,e=q,f=f(0,0), a, b, c, d, e can be determined by the enhanced inclination and azimuth, p represents the enhanced inclination, q represents the enhanced azimuth; The first-order derivative of the quadratic surface f(x, y) is realized by: performing the first-order derivative of the quadratic surface f(x, y) according to the determination method of the partial wave number derivative, and the determination method of the partial wave number derivative is shown in Formula 7, where F represents Fourier transform, k x is the wave number, T(k x ) is the raised cosine function, α is the wave number coefficient, for different α values, the filtering output result is determined according to formula 7, and the result is subjected to inverse Fourier transform to obtain the wave number derivative; Formula 7: Among them, α is defined by the user, and the value range of α is 0.25-1.

4. The device according to claim 3, characterized in that The curvature attributes include mean curvature, Gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, dip curvature, strike curvature, contour curvature, and tortuosity.

5. A computer device, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; The processor is configured to execute the steps of the method described in any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the steps of the method described in any one of claims 1 to 2 are implemented.

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