CN114721046B

CN114721046B - Crack detection method, device, and computer storage medium

Info

Publication number: CN114721046B
Application number: CN202110006411.1A
Authority: CN
Inventors: 石学文; 吴建发; 张洞君; 文山师; 刘文平; 苟其勇; 王畅; 吴涛; 罗浩然
Original assignee: Petrochina Co Ltd
Current assignee: Petrochina Co Ltd
Priority date: 2021-01-05
Filing date: 2021-01-05
Publication date: 2025-02-07
Anticipated expiration: 2041-01-05
Also published as: CN114721046A

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

Method and device for detecting seam hole and computer storage medium

Technical Field

The embodiment of the application relates to the technical field of petroleum exploration, in particular to a fracture-cave detection method, a fracture-cave detection device and a computer storage medium.

Background

In sedimentary rock formations, although only about 20% of carbonate formations are present, more than 50% of the hydrocarbon resources have been found to be stored in carbonate formations. It can be inferred that in a rock formation there are many hydrocarbon resources, and that the place where the hydrocarbon resources are stored in the rock formation is referred to as a rock reservoir, which is usually in the form of a fracture that plays an important role in the storage and migration of hydrocarbon. Therefore, the true state of the oil and gas reservoir of the rock stratum can be clearly known by accurately predicting the fracture and hole in the rock stratum, and then the oil and gas reservoir of the rock stratum can be explored to determine oil and gas resources.

The curvature attribute can reflect the degree of deformation of the rock stratum when the rock stratum is wrinkled or bent due to pressure, so that the curvature attribute can be used for describing the strike of the rock stratum seam holes and the distribution condition of the seam holes. The more severe the deformation of the rock strata due to pressure, the greater the degree of fracture of the fracture holes in the rock strata and the corresponding curvature properties. In the related art, to determine curvature attributes, first, raw data, also called raw seismic data, is acquired through field exploration. Specifically, the seismic waves are excited manually at the earth's surface, and when propagating, they are reflected or refracted by formations having different rock types, and the reflected or refracted seismic waves can be received by detectors in the earth's surface or in the oil and gas well. The wave detector receives the seismic waves and displays the seismic waves in a data form, namely the original data. Further, the original data are subjected to static correction, denoising, offset, superposition and the like, and then a complex analysis algorithm, a discrete dip angle scanning algorithm or a gradient structure tensor algorithm is adopted to determine the dip angle and the azimuth angle of the rock stratum. And determining the curvature attribute of the rock stratum by using the determined inclination angle and azimuth angle of the rock stratum.

In the technology, the inclination angle and the azimuth angle of the stratum are determined by utilizing the data after the superposition processing of the rock stratum, so that the curvature attribute of the rock stratum is obtained. Because the data after the superposition processing is data without orientation division, that is, the data after the superposition processing cannot embody the difference of all orientations around the sampling point. Such as differences in amplitude, frequency, etc., of the individual azimuth signals. Therefore, the determined curvature attribute value of the rock stratum is inaccurate, and solutions of a plurality of curvature attribute values possibly exist, so that the detection result of the fracture hole is affected, and the fracture hole cannot be accurately predicted.

Disclosure of Invention

The embodiment of the application provides a fracture-cavity detection method which can accurately detect the fracture-cavity of a stratum. The technical scheme is as follows:

in a first aspect, a method for detecting a hole is provided, the method comprising:

Acquiring N seismic data volumes corresponding to target sampling points in a stratum to be detected, wherein the N seismic data volumes and N azimuth intervals correspond respectively, the seismic data volume corresponding to each azimuth interval in the N azimuth intervals comprises a plurality of pieces of original data, the plurality of pieces of original data and a plurality of receiving points correspond respectively, 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 points;

Determining a multi-dimensional gradient body corresponding to any one of the N seismic data bodies based on each piece of original data in a plurality of pieces of original data included in the any one of the N seismic data bodies, wherein the multi-dimensional gradient body indicates the change rule of the original data in different dimensions in the any one of the seismic data bodies;

Determining azimuth angles and inclination angles corresponding to the target sampling points based on the multidimensional gradient bodies corresponding to any one of the N seismic data bodies;

and determining the curvature attribute corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point.

Optionally, the determining, based on the multidimensional gradient body corresponding to each of the N seismic data volumes, an azimuth angle and an inclination angle corresponding to the target sampling point includes:

weighting the multidimensional gradient corresponding to each seismic data volume in the N seismic data volumes to obtain the multidimensional gradient corresponding to the target sampling point;

determining a gradient tensor of a multidimensional gradient body corresponding to the target sampling point;

determining a feature vector of a gradient tensor of the multidimensional gradient body corresponding to the target sampling point;

And determining the azimuth angle and the inclination angle corresponding to the target sampling point based on the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point.

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

determining a curved surface of the target sampling point on an azimuth interval corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point;

and determining the curvature attribute corresponding to the target sampling point based on the curved surface corresponding to the target sampling point.

Optionally, the curvature attribute includes average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, trending curvature, contour curvature, curvature.

In a second aspect, there is provided a hole detection apparatus, the apparatus comprising:

the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring N seismic data volumes corresponding to a target sampling point in a stratum to be detected, the N seismic data volumes correspond to N azimuth intervals respectively, the seismic data volumes corresponding to each azimuth interval in the N azimuth intervals comprise a plurality of pieces of original data, the plurality of pieces of original data correspond to a plurality of receiving points respectively, 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;

The determining module is used for determining a multi-dimensional gradient body corresponding to any one of the N seismic data bodies based on each piece of original data in a plurality of pieces of original data included in the seismic data bodies, wherein the multi-dimensional gradient body indicates the change rule of the original data in different dimensions in the any one of the seismic data bodies;

The determining module is used for determining an azimuth angle and an inclination angle corresponding to the target sampling point based on the multidimensional gradient body corresponding to each of the N seismic data bodies;

The determining module is used for determining the curvature attribute corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point.

Optionally, the determining module further includes:

The determining unit is used for carrying out weighting processing on the multidimensional gradient body corresponding to each seismic data body in the N seismic data bodies to obtain the multidimensional gradient body corresponding to the target sampling point;

the determining unit is used for determining the gradient tensor of the multidimensional gradient body corresponding to the target sampling point;

The determining unit is used for determining the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point;

the determining unit is used for determining the azimuth angle and the inclination angle corresponding to the target sampling point based on the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point.

Optionally, the determining module further includes:

The determining unit is used for determining a curved surface of the target sampling point on the azimuth interval corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point;

The determining unit is used for determining 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.

In a third aspect, a computer readable storage medium is provided, where instructions are stored, the instructions when executed by a processor implement a method for oilfield joint detection as described in the first aspect.

In a fourth aspect, there is provided a computer apparatus, the apparatus comprising:

A processor;

A memory for storing processor-executable instructions;

Wherein the processor is configured to perform a method of oilfield joint detection as described in the first aspect above.

In a fifth aspect, there is provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of hole detection as described in the first aspect above.

The technical scheme provided by the embodiment of the application has the beneficial effects that at least:

And determining azimuth angles and inclination angles corresponding to the target sampling points through multidimensional gradients corresponding to each seismic data volume in the N seismic data volumes, and further determining curvature attributes corresponding to the target sampling points. And further reflecting the bending degree of the stratum to be detected by utilizing the curvature attribute of the target sampling point. Because the N seismic data volumes respectively correspond to the N azimuth intervals, each seismic data volume in the N seismic data volumes corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of all the azimuths. Therefore, the bending degree of the stratum to be detected is determined based on all the positions around the target sampling point, and the differences of all the positions around the target sampling point can be reflected, so that the fracture and tunnel in the stratum where the target sampling point is located can be accurately detected.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a method for detecting a hole in a seam according to an embodiment of the present application.

Fig. 2 is a flowchart of a method for detecting a hole in a seam according to an embodiment of the present application.

Fig. 3 is a schematic structural diagram of a hole detection device according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of a hole detection device according to an embodiment of the present application.

Fig. 5 is a block diagram of a terminal according to an embodiment of the present application.

Fig. 6 is a schematic diagram of a server structure according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings.

For convenience of description, application scenarios of the embodiments of the present application are described herein.

Hydrocarbon reservoir types of rock formations include pore type, karst cave type, fracture type, karst cave-fracture type combinations, and the like, and these types of structures are collectively referred to herein as fracture holes. The size of the seam hole is an important index for judging the oil and gas productivity of the oil and gas reservoir of the rock stratum, and the seam hole of the rock stratum is accurately detected, so that the oil and gas yield of the oil and gas reservoir of the rock stratum can be judged.

The method provided by the embodiment of the application is applied to a scene of detecting the fracture and hole of the rock stratum.

The method provided by the embodiment of the application is further explained below. It should be noted that, in the embodiment of the present application, the steps in fig. 1 may be performed by using devices such as a terminal, a controller, and a server, which are not limited herein by the execution body of the embodiment of the present application. In fig. 1, a terminal is described as an execution subject.

Fig. 1 is a flowchart of a method for detecting a hole according to an embodiment of the present application, where the method for detecting a hole may include the following steps.

Step 101, a terminal acquires N seismic data volumes corresponding to target sampling points in a stratum to be detected.

In order to facilitate the determination of the curvature attribute of the stratum 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 bending degree of the stratum to be detected. And the terminal determines a target sampling point according to the plurality of sampling points in the stratum to be detected. And then the terminal acquires N seismic data volumes corresponding to the target sampling points in the stratum to be detected. Wherein the formation to be detected is a rock formation. The target sampling point is one of a plurality of sampling points in the stratum to be detected. In actual operation, N is usually in the range of 5 to 8 as more as3 or more.

The implementation mode of determining the target sampling point from the plurality of sampling points is that the stratum to be detected and the plurality of sampling points of the stratum to be detected are displayed on a display interface of the terminal. The user selects one of the plurality of sampling points on the terminal display interface based on a preset operation, at this time, the terminal detects a selection instruction of the user based on the one of the plurality of sampling points, and then the terminal determines a target sampling point in response to the selection instruction.

It should be noted that, the preset operation may be a click operation, a sliding operation, or a voice operation, and the embodiment of the present application does not limit the preset operation, which is not described herein.

The N seismic data volumes are divided in a mode that all the orientations around the target sampling point are divided into N azimuth intervals, all the azimuth indications around the target sampling point map the target sampling point on the ground surface, the mapping point is used as a circle center, the ground surface is used as a plane, and the angles around the circle center are 0-360 degrees. That is, 0 to 360 ° around the mapping point corresponding to the target sampling point is divided into N azimuth intervals. The N seismic data volumes and the N azimuth intervals correspond respectively. Each azimuth interval corresponds to one seismic data volume, and N azimuth intervals have N seismic data volumes.

The N azimuth intervals may be N azimuth intervals equally divided into 0 to 360 ° around the mapping point corresponding to the target sampling point, and may be N azimuth intervals arbitrarily divided into 0 to 360 ° around the mapping point corresponding to the target sampling point.

The implementation manner of acquiring the seismic data volume corresponding to each azimuth interval is that a plurality of receiving points are selected in each azimuth interval, namely, the plurality of receiving points are arranged in the corresponding azimuth interval. Wherein the plurality of receiving points have different offset distances therebetween. In order to enable the plurality of receiving points to receive the wave signals of the target sampling points, a plurality of excitation points are arranged at 0-360 degrees around the corresponding mapping points of the target sampling points, and the plurality of excitation points are in one-to-one correspondence with the plurality of receiving points. And the connecting line of each receiving point and the corresponding excitation point passes through the mapping point corresponding to the target sampling point. A detector is positioned at each receiving point for receiving the wave signal reflected by the target employing point. When any excitation point is excited, the wave signal reflected by the target sampling point is received by the detector at the corresponding receiving point. The detector displays the received wave signal in the form of data, which is the original data received by the receiving point. The plurality of original data and the plurality of receiving points correspond respectively, and the plurality of receiving points receive the plurality of original data. The plurality of pieces of original data in each azimuth interval are called seismic data volumes corresponding to each azimuth interval.

The manner of exciting any excitation point is not limited in the embodiment of the present application, and is not illustrated here one by one.

In addition, after the terminal obtains N seismic data volumes corresponding to the target sampling points in the stratum to be detected, any one of the N seismic data volumes performs the following steps 102, 103 and 104 to obtain curvature attributes corresponding to the N azimuth intervals respectively. For ease of explanation, the first seismic data volume will be described as an example.

Step 102, the terminal determines a multidimensional gradient body corresponding to a first seismic data body based on each piece of original data in a plurality of pieces of original data included in the first seismic data body in the N seismic data bodies.

In one possible implementation manner, since each piece of original data includes three-dimensional data volumes along three directions of line (line), track (xline) and time (time), the terminal determines the three-dimensional data volume of the first seismic data volume based on the three-dimensional data volume of each piece of original data included in the first seismic data volume, and determines the multi-dimensional gradient volume corresponding to the first seismic data volume based on the three-dimensional data volume of the first seismic data volume.

The terminal determines the implementation mode of the three-dimensional data body of the first seismic data body based on the three-dimensional data body of each piece of original data in the plurality of pieces of original data included in the first seismic data body.

Specifically, each piece of original data in the plurality of pieces of original data is subjected to weighting processing along the data in the line direction, each piece of original data in the plurality of pieces of original data is subjected to weighting processing along the data in the channel direction, each piece of original data in the plurality of pieces of original data is subjected to weighting processing along the data in the time direction, and the three-dimensional data volume of the first seismic data volume is obtained through weighting processing along the three directions of the line, the channel and the time.

For example, three data volumes along a line, track, and time of any one of the plurality of original data are denoted by (p, q, r). The first seismic data volume comprises 3 pieces of original data, the three-dimensional data volume corresponding to the first piece of original data is (p 1, q1, r 1), the three-dimensional data volume corresponding to the second piece of original data is (p 2, q2, r 2), and the three-dimensional data volume corresponding to the third piece of original data is (p 3, q3, r 3). Each piece of original data is weighted according to the weight of each piece of original data. If the weight of the first piece of original data is Q1, the weight of the second piece of original data is Q2, and the weight of the second piece of original data is Q3, the result of the weighting process is (q1p1+q2p2+q3r 3,Q1q1+Q2q2+Q3 r3,Q1q1+Q2q2+Q3 r 3). The result of the weighting process is a three-dimensional data volume of the first seismic data volume. The result of the weighting process is denoted herein as (p, q, r).

The implementation mode of determining the multi-dimensional gradient body corresponding to the first seismic data body based on the three-dimensional data body of the first seismic data body is that the multi-dimensional gradient body in three dimensions of a line, a track and time of the three-dimensional data body of the first seismic data body is determined, and the multi-dimensional gradient body is also called as a three-dimensional gradient body. The multi-dimensional gradient volume in three dimensions along the line, trace, and time of the first seismic data volume is denoted herein as (x, y, z).

As shown in equation 1 below, equation 1 is a multi-dimensional gradient volume of the first seismic data volume, denoted by g 1.Representing a three-dimensional gradient of (x, y, z).Indicating that deriving x results in a gradient trace g1 _x along the line,The derivation of y is shown, resulting in a gradient track g1 _y along the track direction.The z is derived to obtain a gradient trace g1 _z along the time direction. Final g1 _x、g1_y、g1_z composition is a multi-dimensional gradient of (c).

Equation 1:

Since there are N seismic data volumes, the multidimensional gradient gn of the nth seismic data volume is shown in the following equation 2.

Equation 2:

The above g1 _x、g1_y、g1_z can be determined by convolution. As shown in formula 3, G' (t) is a derivative of a one-dimensional zero-mean discrete gaussian kernel function, t is a discrete variable, a value range of t is [ -R _i,+R_i],R_i ²＝4²σ_i,R_i is a kernel radius, σ _i is a predetermined scale factor, i e { x, y, z }.

Equation 3:

If i is x, σ _x is a predetermined scale factor corresponding to the line direction, and R _x is a kernel radius corresponding to the line direction, that is, in equation 3, the data in the line direction is convolved with the convolution kernel, so as to obtain a gradient trace g1 _x of the first seismic data volume in the line direction. If i is y, σ _y is a predetermined scale factor corresponding to the trace direction, and R _y is a kernel radius corresponding to the trace direction, that is, in equation 3, the data along the trace direction is convolved with the convolution kernel, so as to obtain a gradient trace g1 _y of the first seismic data volume along the trace direction. If i is z, σ _z is a predetermined scale factor corresponding to the time direction, and R _z is a kernel radius corresponding to the time direction, that is, in equation 3, the data along the time direction is convolved with the convolution kernel, so as to obtain a gradient trace g1 _z of the first seismic data volume along the line direction. Where a larger σ indicates a wider band of the gaussian filter and a better degree of smoothing. The smaller σ, the worse the smoothing effect, but the better the detail, the window length of a typical gaussian filter is 5-9 discrete points.

It should be noted that, the method for determining the multidimensional gradient corresponding to the other seismic data volumes in the N seismic data volumes is the same as that of the first seismic data volume, and no description is given here on how to determine the multidimensional gradient corresponding to each seismic data volume in the N seismic data volumes.

And 103, determining azimuth angles and inclination angles corresponding to the target sampling points by the terminal based on the multidimensional gradient body corresponding to each of the N seismic data volumes.

The terminal is based on the multidimensional gradient bodies corresponding to each of N seismic data bodies, and the implementation mode of determining the azimuth angle and the inclination angle corresponding to the target sampling point is that the multidimensional gradient bodies corresponding to each of the N seismic data bodies are weighted to obtain the multidimensional gradient bodies corresponding to the target sampling point, then the gradient tensor of the multidimensional gradient bodies corresponding to the target sampling point is determined, further the characteristic vector of the gradient tensor of the multidimensional gradient bodies corresponding to the target sampling point is determined, and the azimuth angle and the inclination angle corresponding to the target sampling point are determined based on the characteristic vector of the gradient tensor of the multidimensional gradient bodies corresponding to the target sampling point.

The implementation method for weighting the multidimensional gradient corresponding to each seismic data body of the N seismic data bodies to obtain the multidimensional gradient corresponding to the target sampling point comprises the steps of weighting and adding the multidimensional gradients corresponding to each seismic data body of the N seismic data bodies to obtain a gradient channel along the direction (x), a gradient channel along the direction (y) and a gradient channel along the direction (z). These three gradient tracks are referred to as multi-dimensional gradients corresponding to the target sampling points.

As shown in equation 4, g represents a multidimensional gradient corresponding to the target sampling point.Representing the multidimensional gradient body of the target sampling point along three directions of line, track and time. w1 is defined as the number of the components, the term, wn represents the weight of the first of the N volumes of seismic data. g1 _x,……,gn_x denotes a multi-dimensional gradient volume corresponding to all seismic data volumes along the line direction, g1 _y,……,gn_y denotes a multi-dimensional gradient volume corresponding to all seismic data volumes along the trace direction, and g1 _z,……,gn_z denotes a multi-dimensional gradient volume corresponding to all seismic data volumes along the time direction. g _x denotes the final gradient trace after the multi-dimensional gradient volume weights for all seismic data volumes along the trace direction, g _y denotes the final gradient trace after the multi-dimensional gradient volume weights for all seismic data volumes along the trace direction, and g _z denotes the final gradient trace after the multi-dimensional gradient volume weights for all seismic data volumes along the time direction. g1 _x、g1_y、g1_z is a multidimensional gradient corresponding to the target sampling point. May also be referred to as a multi-dimensional gradient volume corresponding to the N seismic data volumes.

Equation 4:

It should be noted that the weights of the corresponding initial multidimensional gradiometers may be given by human experience. Weights can also be given on the basis of post-stack hole detection QC (quantitycontrol, quality control). The mode of giving the weight on the basis of QC is that if the seam hole at a certain receiving point is relatively developed, namely the characteristics of the seam hole are clear, the weight of the initial multidimensional gradient body corresponding to the original data of the receiving point is large, so that finer seam hole data is obtained.

The implementation manner of the terminal to determine the gradient tensor of the multidimensional gradient body corresponding to the target sampling point is as shown in a formula 5, wherein in the formula 5, T represents the gradient tensor, the gradient tensor is represented in a matrix form, and the row and column positions of each element in the matrix are represented by T _ij. As T ₁₃ denotes the first row and the third column. And the terminal determines a transposed matrix g ^T of the g corresponding matrix according to the multidimensional gradient g corresponding to the target sampling point determined in the step 103. And multiplying the g corresponding matrix by the device matrix to obtain T.

Equation 5:

The implementation method of the feature vector for determining the gradient tensor of the multi-dimensional gradient body corresponding to the target sampling point is that feature decomposition is carried out on the gradient tensor of the multi-dimensional gradient body corresponding to the target sampling point to obtain the feature vector, as shown in a formula 6, in the formula 6, lambda represents a diagonal matrix, values on the diagonal are feature values, lambda ₁,λ₂,λ₃ are respectively arranged from large to small. v represents a feature vector matrix, and column vectors represent feature vectors corresponding to the above feature values, which are v ₁,v₂,v₃ respectively.

Equation 6:

Tv=Λv

The terminal determines the azimuth angle and the inclination angle corresponding to the target sampling point based on the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point. Specifically, assuming that the feature vector corresponding to the maximum feature value λ ₁,λ₁ is v ₁ (x, y, z), v ₁ (x, y, z) is decomposed in the x-direction, the y-direction, and the z-direction, respectively, three elements v _1x(x,y,z)、v_1y(x,y,z)、v_1z (x, y, z) can be obtained, and the inclination angle v ₁ (1) and the azimuth angle v ₁ (2) can be obtained using the three elements. Specifically, the method can be represented by the following formula:

it should be noted that other implementations of determining the azimuth angle and the inclination angle are also possible, and embodiments of the present application are not limited herein.

In addition, in order to amplify the difference between the inclination angle and the azimuth angle. After the azimuth angle and the inclination angle are determined, the inclination angle and the azimuth angle are respectively subjected to enhancement treatment. Tilt angle after the enhancement treatment is p=sign (v ₁(1))*v₁(1)*v₁ (1). Azimuth angle after the enhancement treatment is q=sign (v ₁(2))*v₁(2)*v₁ (2).

And 104, determining the curvature attribute corresponding to the target sampling point by the terminal based on the azimuth angle and the inclination angle corresponding to the target sampling point.

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

The terminal determines the curved surface corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point, wherein the realization mode of the curved surface corresponding to the target sampling point is that the normal quadric surface is expressed as f (x, y) =ax ²+by² +cxy+dx+ey+f, and a, b, c, d, e can be determined by the inclination angle and the azimuth angle after enhancement. In particular, the method comprises the steps of,D=p, e=q, f=f (0, 0), where p represents the tilt angle after enhancement and q represents the azimuth angle after enhancement.

The curvature attribute corresponding to the target sampling point is determined based on the curved surface corresponding to the target sampling point by performing first-order derivation on the quadric surface f (x, y) and then combining various curvature calculation formulas to extract the curvature attribute of the target sampling point under different wave numbers and different scales.

The implementation mode of performing first-order derivation on the quadric f (x, y) is that the quadric f (x, y) is subjected to first-order derivation according to a determination method of a fractional derivative. The method for determining the derivative of the wave number is shown in the formula 7, F represents the Fourier transform, k _x is the wave number,T (k _x) is a raised cosine function. And alpha is a fractional coefficient, for different alpha values, determining a filtering output result according to a formula 7, and performing inverse Fourier transformation on the result to obtain a fractional derivative. Then a, b, c, d, e may also be used in this way,

Equation 7:

it should be noted that α is generally defined by the user, and the value range of α is generally 0.25-1, the larger the value, the more details, the smaller the value, the better the smoothness, the better the details are blurred, and the user needs to balance to give the value of α.

Further, curvature attributes include average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, trending curvature, contour curvature, curvature. The calculation modes of different curvature attributes are different, and different curvature calculation formulas need to be characterized by a plurality of parameters a, b, c, d, e in a curved surface equation, specifically as follows:

1. average curvature: the curvature is controlled by the maximum curvature and looks similar to the maximum curvature.

2. Gaussian curvature: Gaussian curvature alone cannot be used and average curvature information is also needed to assist.

3. Great curvature: the stratum to be detected shows adjacency of a positive curvature value and a negative curvature value, the curvature value determines the fault breaking direction of the stratum to be detected, the positive curvature value represents fault rising, and the negative curvature value represents fault falling.

4. Very small curvature: When the minimum curvature is very small or zero, the stratum to be detected is a plane surface, and when the minimum curvature is very large, the stratum to be detected is distorted in a non-equidistant way, namely dislocation and fracture of the stratum to be detected can occur, so that a fracture zone can be judged.

5. Maximum positive curvature: the curvature can be measured by detecting formation discontinuity information and some small linear structures.

6. Minimum negative curvature: the curved surface characteristics can be determined.

7. Morphology index: The morphological parameters are obtained by combining the extremely small curvature and the extremely large curvature, and the local morphology of the layer surface which is irrelevant to the scale can be described.

8. Trend curvature: The curvature contains size information and azimuth information of faults in the stratum to be detected.

9. Curvature of trend: the morphology of the substrate to be inspected is described.

10. Contour curvature: Particularly large values can occur in anticlines, ridges, etc.

11. Bending: representing the magnitude of the morphology-independent curvature of the formation to be examined.

When the underground fracture hole is developed, the wave signals received by the receiving points in different azimuth intervals are changed to different degrees, and the travel time, amplitude, frequency, phase and the like are also greatly different, so that the calculated curvature properties are also different.

The curvature attribute corresponding to the target sampling point is determined, so that the bending degree of the stratum to be detected, where the target sampling point is located, can be reflected. Because the target sampling point has N azimuth intervals, the curvature attribute of the target sampling point can show different azimuth, so the bending degree of the stratum to be detected can reflect the difference of the azimuth, and the fracture-cavity in the stratum where the target sampling point is located can be accurately detected.

Fig. 2 is a diagram showing the effect of performing fracture, karst hole and karst cave identification on a three-dimensional work area of a Sichuan basin by using the method in the embodiment of the application, and fig. 2 is a schematic diagram of a karst cave provided in the real-time example of the application, wherein in addition to fine characterization of fracture, black anomalies of circles and ellipses are interpreted as karst cave, and the correctness of the black anomalies is verified by real drilling.

In summary, in the real-time example of the present application, the azimuth angle and the inclination angle corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each of the N seismic data volumes, so as to determine the curvature attribute corresponding to the target sampling point. And further reflecting the bending degree of the stratum to be detected by utilizing the curvature attribute of the target sampling point. Because the N seismic data volumes respectively correspond to the N azimuth intervals, each seismic data volume in the N seismic data volumes corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of all the azimuths. Therefore, the bending degree of the stratum to be detected is determined based on all the positions around the target sampling point, and the differences of all the positions around the target sampling point can be reflected, so that the fracture and tunnel in the stratum where the target sampling point is located can be accurately detected.

The method provided by the embodiment of the application is further explained below by taking fig. 3 as an example. It should be noted that, the embodiment shown in fig. 3 is only a part of the optional technical solutions in the embodiment shown in fig. 1, and does not limit the method for detecting a hole in the embodiment of the present application.

1. And reading three-dimensional seismic data of each azimuth, wherein each azimuth is N azimuth intervals. That is, the terminal acquires N seismic data volumes corresponding to the target sampling points in the stratum to be detected, where each seismic data volume includes a seismic three-dimensional data volume corresponding to three directions of line, trace and time of each piece of original data in the plurality of pieces of original data.

2. Gradients along, and in time of each track of the azimuth data volume are determined. Each azimuth corresponds to N volumes of seismic data, and therefore, a multidimensional gradient corresponding to each of the N volumes of seismic data is determined.

3. And weighting and adding the multidimensional gradients corresponding to each seismic data volume in the N seismic data volumes to obtain a gradient channel along the direction (x), a gradient channel along the direction (y) and a gradient channel along the direction (z). These three gradient tracks are referred to as multi-dimensional gradients corresponding to the target sampling points. The multi-dimensional gradient body corresponding to the target sampling point is called the final gradient of each direction interval corresponding to the target sampling point.

4. And sequentially extracting each receiving point in the target sampling points to determine the gradient tensor of the multidimensional gradient body corresponding to the target sampling points. The gradient tensor is the constructed covariance matrix.

5. And carrying out eigenvalue decomposition on the gradient tensor, and determining eigenvectors of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point.

6. And the terminal determines an azimuth angle and an inclination angle based on the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point, and performs enhancement processing on the inclination angle and the azimuth angle respectively.

7. And determining the curvature attribute corresponding to the target sampling point.

8. And judging whether the curvature attribute corresponding to the target sampling point is determined to be finished. If not, continuously determining the gradient tensor of the multidimensional gradient body corresponding to the target sampling point. If yes, judging whether the curvature attribute of the target sampling point is determined. If the curvature attribute corresponding to the target sampling point is not determined, the method indicates that gradients of all seismic data volumes along the line, the channel and the time direction in the N seismic data volumes are not determined, and continuously determines gradients of each seismic data volume along the line, the channel and the time direction in each azimuth data volume. And if the curvature attribute corresponding to the target sampling point is determined, outputting the curvature attribute corresponding to the target sampling point.

Fig. 4 is a schematic structural diagram of a hole detection device according to an embodiment of the present application, where the hole detection device may be implemented by software, hardware, or a combination of both. The hole detection device 400 may include a determination module 101 and a processing module 102.

The acquisition module is used for acquiring N seismic data volumes corresponding to a target sampling point in a stratum to be detected, the N seismic data volumes and N azimuth intervals are respectively corresponding, the seismic data volumes corresponding to each azimuth interval in the N azimuth intervals comprise a plurality of pieces of original data, the plurality of pieces of original data and a plurality of receiving points are respectively corresponding, 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;

the determining module is used for determining a multi-dimensional gradient body corresponding to a first seismic data body based on each piece of original data in a plurality of pieces of original data included in the first seismic data body in the N seismic data bodies, wherein the multi-dimensional gradient body indicates the change rule of the original data in different dimensions in the first seismic data body;

the determining module is used for determining azimuth angles and inclination angles corresponding to the target sampling points based on the multidimensional gradient body corresponding to each of the N seismic data bodies;

And the determining module is used for determining the curvature attribute corresponding to the target sampling point based on the azimuth angle and the inclination angle corresponding to the target sampling point.

Optionally, the determining module further includes:

the determining unit is used for determining the characteristic vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point;

And the determining unit is used for determining the azimuth angle and the inclination angle 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.

Optionally, the determining module further includes:

the determining unit is used for determining the curvature attribute corresponding to the target sampling point based on the curved surface of the target sampling point on the azimuth interval corresponding to the target sampling point.

Optionally, the curvature attribute comprises average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, trending curvature, contour curvature, curvature.

In summary, in the embodiment of the present application, the azimuth angle and the inclination angle corresponding to the target sampling point are determined by the multidimensional gradient body corresponding to each of the N seismic data volumes, so as to determine the curvature attribute corresponding to the target sampling point. And further reflecting the bending degree of the stratum to be detected by utilizing the curvature attribute of the target sampling point. Because the N seismic data volumes respectively correspond to the N azimuth intervals, each seismic data volume in the N seismic data volumes corresponds to a different azimuth interval, and the different azimuth intervals can reflect the differences of all the azimuths. Therefore, the bending degree of the stratum to be detected is determined based on all the positions around the target sampling point, and the differences of all the positions around the target sampling point can be reflected, so that the fracture and tunnel in the stratum where the target sampling point is located can be accurately detected.

It should be noted that, in the slot hole detection device provided in the above embodiment, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the terminal is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the embodiment of the slit hole detection device and the embodiment of the slit hole detection method provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment, and are not repeated herein.

Fig. 5 shows a block diagram of a terminal 500 according to an exemplary embodiment of the present application. The terminal 500 may be a smart phone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III, MPEG audio layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, MPEG audio layer 4) player, notebook computer, or desktop computer. The terminal 500 may also be referred to by other names of user devices, portable terminals, laptop terminals, desktop terminals, etc.

In general, terminal 500 includes a processor 501 and a memory 502.

Processor 501 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 501 may be implemented in at least one hardware form of DSP (DIGITAL SIGNAL Processing), FPGA (Field-Programmable gate array), PLA (Programmable Logic Array ). The processor 501 may also include a main processor, which is a processor for processing data in a wake-up state, also referred to as a CPU (Central Processing Unit ), and a coprocessor, which is a low-power processor for processing data in a standby state. In some embodiments, the processor 501 may be integrated with a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 501 may also include an AI (ARTIFICIAL INTELLIGENCE ) processor for processing computing operations related to machine learning.

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

In some embodiments, terminal 500 may optionally further comprise a peripheral interface 503 and at least one peripheral. The processor 501, memory 502, and peripheral interface 503 may be connected by buses or signal lines. The individual peripheral devices may be connected to the peripheral device interface 503 by buses, signal lines or circuit boards. Specifically, the peripheral devices include at least one of radio frequency circuitry 504, a display 505, a camera assembly 506, audio circuitry 507, a positioning assembly 508, and a power supply 509.

Peripheral interface 503 may be used to connect at least one Input/Output (I/O) related peripheral to processor 501 and memory 502. In some embodiments, processor 501, memory 502, and peripheral interface 503 are integrated on the same chip or circuit board, and in some other embodiments, either or both of processor 501, memory 502, and peripheral interface 503 may be implemented on separate chips or circuit boards, which is not limited in this embodiment.

The Radio Frequency circuit 504 is configured to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuitry 504 communicates with a communication network and other communication devices via electromagnetic signals. The radio frequency circuit 504 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuitry 504 includes an antenna system, an RF transceiver, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuitry 504 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to, metropolitan area networks, generation-by-generation mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (WIRELESS FIDELITY ) networks. In some embodiments, the radio frequency circuit 504 may further include NFC (NEAR FIELD Communication) related circuits, which is not limited by the present application.

The display screen 505 is used to display a UI (user interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display 505 is a touch display, the display 505 also has the ability to collect touch signals at or above the surface of the display 505. The touch signal may be input as a control signal to the processor 501 for processing. At this time, the display 505 may also be used to provide virtual buttons and/or virtual keyboards, also referred to as soft buttons and/or soft keyboards. In some embodiments, the display 505 may be one, with the front panel of the terminal 500 disposed, in other embodiments, at least two, with the display 505 disposed on different surfaces or in a folded configuration of the terminal 500, respectively, and in other embodiments, the display 505 may be a flexible display disposed on a curved surface or a folded surface of the terminal 500. Even more, the display 505 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The display 505 may be made of LCD (Liquid CRYSTAL DISPLAY), OLED (Organic Light-Emitting Diode), or other materials.

The camera assembly 506 is used to capture images or video. Optionally, the camera assembly 506 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal and the rear camera is disposed on the rear surface of the terminal. In some embodiments, the at least two rear cameras are any one of a main camera, a depth camera, a wide-angle camera and a tele camera, so as to realize that the main camera and the depth camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize a panoramic shooting function and a VR (VirtualReality ) shooting function or other fusion shooting functions. In some embodiments, camera assembly 506 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The dual-color temperature flash lamp refers to a combination of a warm light flash lamp and a cold light flash lamp, and can be used for light compensation under different color temperatures.

The audio circuitry 507 may include a microphone and a speaker. The microphone is used for collecting sound waves of users and environments, converting the sound waves into electric signals, and inputting the electric signals to the processor 501 for processing, or inputting the electric signals to the radio frequency circuit 504 for voice communication. For the purpose of stereo acquisition or noise reduction, a plurality of microphones may be respectively disposed at different portions of the terminal 500. The microphone may also be an array microphone or an omni-directional pickup microphone. The speaker is used to convert electrical signals from the processor 501 or the radio frequency circuit 504 into sound waves. The speaker may be a conventional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only the electric signal can be converted into a sound wave audible to humans, but also the electric signal can be converted into a sound wave inaudible to humans for ranging and other purposes. In some embodiments, audio circuitry 507 may also include a headphone jack.

The location component 508 is used to locate the current geographic location of the terminal 500 to enable navigation or LBS (Location Based Service, location-based services). The positioning component 508 may be a positioning component based on the United states GPS (Global Positioning System ), the Beidou system of China, the Granati system of Russia, or the Galileo system of the European Union.

A power supply 509 is used to power the various components in the terminal 500. The power supply 509 may be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 509 comprises a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, the terminal 500 further includes one or more sensors 510. The one or more sensors 510 include, but are 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.

The acceleration sensor 511 can detect the magnitudes of accelerations on three coordinate axes of the coordinate system established with the terminal 500. For example, the acceleration sensor 511 may be used to detect components of gravitational acceleration on three coordinate axes. The processor 501 may control the display 505 to display a user interface in a landscape view or a portrait view according to a gravitational acceleration signal acquired by the acceleration sensor 511. The acceleration sensor 511 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 512 may detect a body direction and a rotation angle of the terminal 500, and the gyro sensor 512 may collect a 3D motion of the user to the terminal 500 in cooperation with the acceleration sensor 511. The processor 501 can realize functions such as motion sensing (e.g., changing a UI according to a tilting operation of a user), image stabilization at photographing, game control, and inertial navigation, based on data collected by the gyro sensor 512.

The pressure sensor 513 may be disposed at a side frame of the terminal 500 and/or at a lower layer of the display 505. When the pressure sensor 513 is disposed at a side frame of the terminal 500, a grip signal of the user to the terminal 500 may be detected, and the processor 501 performs left-right hand recognition or quick operation according to the grip signal collected by the pressure sensor 513. When the pressure sensor 513 is disposed at the lower layer of the display screen 505, the processor 501 controls the operability control on the UI interface according to the pressure operation of the user 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.

The fingerprint sensor 514 is used for collecting the fingerprint of the user, and the processor 501 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 514 or the fingerprint sensor 514 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the user is authorized by the processor 501 to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying for and changing settings, etc. The fingerprint sensor 514 may be provided on the front, back or side of the terminal 500. When a physical key or a vendor Logo is provided on the terminal 500, the fingerprint sensor 514 may be integrated with the physical key or the vendor Logo.

The optical sensor 515 is used to collect the ambient light intensity. In one embodiment, the processor 501 may control the display brightness of the display screen 505 based on the intensity of ambient light collected by the optical sensor 515. Specifically, the display brightness of the display screen 505 is turned up when the ambient light intensity is high, and the display brightness of the display screen 505 is turned down when the ambient light intensity is low. In another embodiment, the processor 501 may also dynamically adjust the shooting parameters of the camera assembly 506 based on the ambient light intensity collected by the optical sensor 515.

A proximity sensor 516, also referred to as a distance sensor, is typically provided on the front panel of the terminal 500. The proximity sensor 516 serves to collect a distance between the user and the front surface of the terminal 500. In one embodiment, the processor 501 controls the display 505 to switch from the on-screen state to the off-screen state when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually decreasing, and the processor 501 controls the display 505 to switch from the off-screen state to the on-screen state when the proximity sensor 516 detects that the distance between the user and the front of the terminal 500 is gradually increasing.

Those skilled in the art will appreciate that the structure shown in fig. 5 is not limiting and that more or fewer components than shown may be included or certain components may be combined or a different arrangement of components may be employed.

The embodiment of the application also provides a non-transitory computer readable storage medium, which enables the terminal to execute the fracture-hole detection method provided in the above embodiment when the instructions in the storage medium are executed by the processor of the terminal.

The embodiment of the application also provides a computer program product containing instructions, which when run on a terminal, causes the terminal to execute the method for detecting the seam holes provided by the embodiment.

Fig. 6 is a schematic diagram of a server structure according to an embodiment of the present application. The server may be a server in a backend server cluster. Specifically, the present application relates to a method for manufacturing a semiconductor device.

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 for facilitating the transfer of information between various devices within the computer, and a mass storage device 607 for storing an operating system 613, application programs 614, and other program modules 615.

The basic input/output system 606 includes a display 608 for displaying information and an input device 609, such as a mouse, keyboard, etc., for a user to input information. Wherein both the display 608 and the input device 609 are coupled to the central processing unit 601 via an input output controller 610 coupled to the system bus 605. The basic input/output system 606 may also include an input/output controller 610 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input output controller 610 also provides output to a display screen, a printer, or other type of output device.

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 CD-ROM drive.

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

The server 600 may also operate by a remote computer connected to the network through a network such as the internet, according to various embodiments of the present application. I.e., server 600 may be connected to network 612 through a network interface unit 611 coupled to system bus 605, or other types of networks or remote computer systems (not shown) may be coupled to using network interface unit 611.

The memory also includes one or more programs, one or more programs stored in the memory and configured to be executed by the CPU. The one or more programs include instructions for performing the method for detecting a hole provided by the embodiment of the present application.

The embodiment of the application also provides a non-transitory computer readable storage medium, which enables the server to execute the fracture-hole detection method provided by the embodiment when the instructions in the storage medium are executed by the processor of the server.

The embodiment of the application also provides a computer program product containing instructions, which when run on a server, cause the server to execute the method for detecting the seam holes provided by the embodiment.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The foregoing description of the preferred embodiments of the present application is not intended to limit the embodiments of the present application, but is intended to cover any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the embodiments of the present application.

Claims

1. A method for detecting a hole, the method comprising:

Acquiring N seismic data volumes corresponding to target sampling points in a stratum to be detected, wherein the N seismic data volumes and N azimuth intervals correspond respectively, the seismic data volume corresponding to each azimuth interval in the N azimuth intervals comprises a plurality of pieces of original data, the plurality of pieces of original data and a plurality of receiving points correspond respectively, the plurality of receiving points are arranged in the corresponding azimuth intervals, different offset distances are arranged among the plurality of receiving points, and the N azimuth intervals can cover all azimuths around the target sampling points;

Determining a three-dimensional data volume of any one of the N seismic data volumes based on a three-dimensional data volume of each of a plurality of original data included in the any one of the N seismic data volumes, determining a multi-dimensional gradient volume corresponding to the any one of the seismic data volumes based on the three-dimensional data volume of the any one of the seismic data volumes, the multi-dimensional gradient volume indicating a change rule of the original data in different dimensions in the any one of the seismic data volumes, wherein the determining the three-dimensional data volume of the any one of the N seismic data volumes based on the three-dimensional data volume of each of the plurality of original data included in the any one of the N seismic data volumes comprises weighting the data of each of the plurality of original data along the direction of the trace, and weighting the data of each of the plurality of original data along the time direction to obtain the three-dimensional data volume of the any one of the seismic data volume;

Determining azimuth angles and inclination angles corresponding to the target sampling points based on feature vectors of gradient tensors of the multidimensional gradient bodies corresponding to the target sampling points;

respectively performing enhancement treatment on the inclination angle and the azimuth angle, wherein the inclination angle after the enhancement treatment is p=sign (v ₁(1))*v₁(1)*v₁ (1), the azimuth angle after the enhancement treatment is q=sign (v ₁(2))*v₁(2)*v₁ (2), and v ₁ (1) is the inclination angle and v ₁ (2) is the azimuth angle;

Determining curvature attribute corresponding to the target sampling point based on the curved surface corresponding to the target sampling point, wherein the curvature attribute corresponding to the target sampling point is determined based on the enhanced azimuth angle and the inclination angle corresponding to the target sampling point, and comprises the steps of first-order derivation of a quadric surface f (x, y) and combination of a curvature calculation formula to obtain the curvature attribute of the target sampling point under different wave numbers and different scales, wherein the quadric surface is expressed as f (x, y) =ax ²+by² +cx+dx+ey+f, D=p, e=q, f=f (0, 0), a, b, c, d, e can be determined from the enhanced tilt angle and azimuth angle, p represents the enhanced tilt angle, q represents the enhanced azimuth angle;

The first order derivation of the quadric F (x, y) is realized according to a determination method of a fractional derivative, wherein the determination method of the fractional derivative is shown in a formula 7, in the formula 7, F represents Fourier transform, k _x is wave number, T (k _x) is a raised cosine function, alpha is a fractional coefficient, for different alpha values, a filtering output result is determined according to a formula 7, and inverse Fourier transformation is carried out on the result to obtain a fractional derivative;

Equation 7:

wherein, alpha is defined by the user, and the value range of alpha is 0.25-1.

2. The method of claim 1, wherein the curvature attribute comprises average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, trending curvature, contour curvature, curvature.

3. A hole detection apparatus, the apparatus comprising:

The system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring N seismic data volumes corresponding to a target sampling point in a stratum to be detected, the N seismic data volumes correspond to N azimuth intervals respectively, the seismic data volumes corresponding to each azimuth interval in the N azimuth intervals comprise a plurality of pieces of original data, the plurality of pieces of original data correspond to a plurality of receiving points respectively, the plurality of receiving points are arranged in the corresponding azimuth intervals, different offset distances are arranged among the plurality of receiving points, and the N azimuth intervals can cover all azimuths around the target sampling point;

The determining module is used for determining a three-dimensional data volume of any one of the N seismic data volumes based on the three-dimensional data volume of each piece of original data in the plurality of pieces of original data included in the seismic data volume, and determining a multi-dimensional gradient volume corresponding to the any one of the seismic data volumes based on the three-dimensional data volume of the any one of the seismic data volumes, wherein the multi-dimensional gradient volume indicates the change rule of the original data in different dimensions in the any one of the seismic data volumes;

the determining module is used for carrying out weighting processing on the data of each piece of original data along the line direction, carrying out weighting processing on the data of each piece of original data along the channel direction, and carrying out weighting processing on the data of each piece of original data along the time direction to obtain a three-dimensional data body of any seismic data body;

The determining module is used for determining curvature attributes corresponding to the target sampling points based on azimuth angles and inclination angles corresponding to the target sampling points;

The determining module further includes:

The determination unit is used for determining an azimuth angle and an inclination angle corresponding to the target sampling point based on the feature vector of the gradient tensor of the multidimensional gradient body corresponding to the target sampling point, respectively carrying out enhancement processing on the inclination angle and the azimuth angle, wherein the inclination angle after the enhancement processing is p=sign (v ₁(1))*v₁(1)*v₁ (1), the azimuth angle after the enhancement processing is q=sign (v ₁(2))*v₁(2)*v₁ (2), v ₁ (1) is the inclination angle, and v ₁ (2) is the azimuth angle;

The determining module further includes:

the determining unit is used for determining a curved surface of the target sampling point on an azimuth interval corresponding to the target sampling point based on the enhanced azimuth angle and the inclination angle corresponding to the target sampling point;

The determining unit is used for determining the curvature attribute corresponding to the target sampling point based on the curved surface corresponding to the target sampling point, wherein the determining the curvature attribute corresponding to the target sampling point based on the curved surface corresponding to the target sampling point comprises the steps of performing first-order derivation on a quadric f (x, y), obtaining the curvature attribute of the target sampling point under different wavenumbers and different scales by combining a curvature calculation formula, wherein the quadric is expressed as f (x, y) =ax ²+by² +cx+dx+ey+f, D=p, e=q, f=f (0, 0), a, b, c, d, e can be determined from the enhanced tilt angle and azimuth angle, p represents the enhanced tilt angle, q represents the enhanced azimuth angle;

Equation 7:

wherein, alpha is defined by the user, and the value range of alpha is 0.25-1.

4. The apparatus of claim 3, wherein the curvature attribute comprises average curvature, gaussian curvature, maximum curvature, minimum curvature, maximum positive curvature, minimum negative curvature, trending curvature, contour curvature, curvature.

5. A computer apparatus, the apparatus comprising:

A processor;

A memory for storing processor-executable instructions;

wherein the processor is configured to perform the steps of the method of any of the preceding claims 1 to 2.

6. A computer readable storage medium having stored thereon instructions which, when executed by a processor, implement the steps of the method of any of the preceding claims 1 to 2.