Crack prediction method and device

Abstract

The present application discloses a fracture prediction method and device, which belongs to the field of geophysical exploration technology. The fracture prediction method provided by the present application first performs inversion through seismic data, wavelet data and initial impedance to obtain impedance inversion results in different azimuths and angles, then extracts the first Fourier coefficient and the second Fourier coefficient from the impedance inversion results respectively, and finally performs inversion based on the first Fourier coefficient and the second Fourier coefficient to obtain the normal weakness and tangential weakness of the fracture. The method decouples the first Fourier coefficient and the second Fourier coefficient, which can eliminate the coupling between the parameters, reduce the number of parameters to be solved, and improve the stability of the inversion process, thereby improving the accuracy of the normal weakness and tangential weakness obtained by inversion, and thus improving the accuracy of fracture prediction.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration technology, and in particular to a fracture prediction method and device. Background Art

[0002] Anisotropic parameters can reflect information about formation fractures and play an extremely important role in fracture reservoir prediction. Among them, anisotropic parameters include the normal weakness and tangential weakness of fractures.

[0003] Related technologies primarily use prestack seismic inversion using PP waves (compressional waves emitted from the earthquake source and reflected once by the surface) to determine the normal and tangential weaknesses of fractures, and then predict fracture development characteristics based on these normal and tangential weaknesses. However, due to the large number of parameters involved in the inversion process, the inversion process is unstable, resulting in poor accuracy in the normal and tangential weaknesses obtained from the inversion, and consequently, poor fracture prediction accuracy. Summary of the Invention

[0004] The embodiments of the present application provide a crack prediction method and apparatus that can improve the accuracy of normal weakness and tangential weakness, thereby improving the accuracy of crack prediction. The technical solution is as follows:

[0005] In one aspect, a crack prediction method is provided, the method comprising:

[0006] Acquire initial impedance and wavelet data corresponding to M incident angles of the seismic wave; wherein M is a positive integer, and one incident angle corresponds to one initial impedance and one wavelet data;

[0007] For each incident angle, obtaining first seismic data corresponding to a plurality of azimuth angles at the incident angle;

[0008] superimposing first seismic data corresponding to a plurality of azimuths at the incident angle to obtain second seismic data corresponding to the incident angle;

[0009] Solving based on second seismic data corresponding to the incident angle, wavelet data corresponding to the incident angle, and initial impedance corresponding to the incident angle to obtain anisotropic impedance corresponding to the incident angle;

[0010] Extracting a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on first relationship data; wherein the first relationship data is used to represent a functional relationship between the anisotropic impedance, a cosine function of the azimuth angle, and the first Fourier coefficient;

[0011] Extracting a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on second relationship data; wherein the second relationship data is used to represent a functional relationship between the anisotropic impedance, the sine function of the azimuth angle, and the second Fourier coefficient;

[0012] Determining N first incident angles from the M incident angles; wherein N is a positive integer and N≤M;

[0013] Solving based on a first matrix, N first Fourier coefficients, and N second Fourier coefficients to obtain normal weakness and tangential weakness of the crack; wherein the first matrix is ​​used to represent a functional relationship between the first Fourier coefficient, the second Fourier coefficient, the normal weakness, and the tangential weakness; and one first incident angle corresponds to one first Fourier coefficient and one second Fourier coefficient;

[0014] Crack prediction is performed based on the normal weakness and the tangential weakness.

[0015] In another aspect, a crack prediction device is provided, comprising:

[0016] A first acquisition module is configured to acquire initial impedance and wavelet data corresponding to M incident angles of seismic waves; wherein M is a positive integer, and each incident angle corresponds to one initial impedance and one wavelet data;

[0017] A second acquisition module is configured to acquire, for each incident angle, first seismic data corresponding to a plurality of azimuth angles at the incident angle;

[0018] a superposition module, configured to superimpose first seismic data corresponding to a plurality of azimuths at the incident angle to obtain second seismic data corresponding to the incident angle;

[0019] A first processing module is configured to solve based on the second seismic data corresponding to the incident angle, the wavelet data corresponding to the incident angle, and the initial impedance corresponding to the incident angle to obtain the anisotropic impedance corresponding to the incident angle;

[0020] a first extraction module, configured to extract a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on first relationship data; wherein the first relationship data is used to represent a functional relationship between the anisotropic impedance, a cosine function of the azimuth angle, and the first Fourier coefficient;

[0021] a second extraction module, configured to extract a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on second relationship data; wherein the second relationship data is used to represent a functional relationship between the anisotropic impedance, a sine function of the azimuth angle, and the second Fourier coefficient;

[0022] A first determining module, configured to determine N first incident angles from the M incident angles; wherein N is a positive integer and N≤M;

[0023] a second processing module, configured to solve based on the first matrix, N first Fourier coefficients, and N second Fourier coefficients to obtain normal weakness and tangential weakness of the crack; wherein the first matrix is ​​used to represent a functional relationship between the first Fourier coefficients, the second Fourier coefficients, the normal weakness, and the tangential weakness; and each first incident angle corresponds to one first Fourier coefficient and one second Fourier coefficient;

[0024] A prediction module is used to predict cracks based on the normal weakness and the tangential weakness.

[0025] On the other hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement any of the above-mentioned crack prediction methods.

[0026] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement any of the above-mentioned crack prediction methods.

[0027] On the other hand, a computer program product is provided, wherein at least one program code is stored in the computer program product, and the at least one program code is loaded and executed by a processor to implement any of the above-mentioned crack prediction methods.

[0028] An embodiment of the present application provides a crack prediction method that first inverts to obtain azimuth and angle-specific impedance inversion results, then extracts the first and second Fourier coefficients from the impedance inversion results, and finally performs inversion based on the first and second Fourier coefficients to obtain the normal and tangential weaknesses of the crack. This method decouples the first and second Fourier coefficients, eliminating the coupling between the parameters, reducing the number of parameters to be solved, and improving the stability of the inversion process, thereby improving the accuracy of the normal and tangential weaknesses obtained through inversion, and thus improving the accuracy of crack prediction.

[0029] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a schematic diagram of an implementation environment of a crack prediction method provided in an embodiment of the present application;

[0031] Figure 2 This is a flow chart of a crack prediction method provided in an embodiment of the present application;

[0032] Figure 3 This is a schematic diagram of performing fracture weakness and velocity inversion provided in an embodiment of the present application;

[0033] Figure 4 1 is a schematic diagram of a synthetic seismic record and inversion result with a signal-to-noise ratio of 2 provided in an embodiment of the present application;

[0034] Figure 5 is a schematic diagram of the relationship between the condition number of a first matrix and the incident angle provided in an embodiment of the present application;

[0035] Figure 6 1 is a schematic diagram of a comparison between an impedance inversion result and a well logging curve provided in an embodiment of the present application;

[0036] Figure 7 is a schematic diagram of impedance inversion results corresponding to different incident angles provided in an embodiment of the present application;

[0037] Figure 8 1 is a schematic diagram of the inversion results of the longitudinal wave velocity, normal weakness and tangential weakness provided in an embodiment of the present application;

[0038] Figure 9 This is a schematic structural diagram of a crack prediction device provided in an embodiment of the present application;

[0039] Figure 10 This is a structural block diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the technical solutions and advantages of the present application clearer, the implementation methods of the present application are described in further detail below.

[0041] The terms "first," "second," "third," and "fourth," etc. in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0042] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, storage, display, etc.), and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the seismic data and wavelet data involved in this application were obtained with full authorization.

[0043] Figure 1 This is a schematic diagram of the implementation environment of a crack prediction method provided by this application, see Figure 1 , see Figure 1 The implementation environment includes: an electronic device, which can be provided as a terminal 101, or can be provided as a terminal 101 and a server 102, without specific limitation.

[0044] If the electronic device is provided as a terminal 101 and a target application is installed on the terminal 101, the user can log in to the target application and then determine the normal weakness and tangential weakness of the crack using the method provided in this application, and then predict the crack based on the normal weakness and tangential weakness.

[0045] If the electronic device is provided as a terminal 101 and a server 102, the terminal 101 and server 102 can be connected via a wireless or wired network. Accordingly, the terminal 101 has a target application installed, and the server 102 is the server 102 corresponding to the target application. When a user uses the target application to perform crack prediction, the server 102 provides background services. In this embodiment of the application, the electronic device is provided as a terminal 101 as an example.

[0046] The terminal 101 is at least one of a mobile phone, a tablet computer, a PC (Personal Computer), an intelligent voice interaction device, and an in-vehicle terminal. The server 102 is at least one of a single server, a server cluster consisting of multiple servers, a cloud server, a cloud computing platform, and a virtualization center.

[0047] Figure 2 This is a flow chart of a crack prediction method provided by an embodiment of the present application, which is executed by an electronic device. Figure 2 , the method comprising:

[0048] Step 201: The electronic device obtains initial impedance and wavelet data corresponding to M incident angles of the seismic wave.

[0049] Wherein, M is a positive integer, and one incident angle corresponds to one initial impedance and one wavelet data.

[0050] For each incident angle, the electronic device obtains the initial impedance corresponding to the incident angle in the following process: the electronic device obtains the logging data corresponding to the incident angle, generates an isotropic impedance model with layer constraints based on the logging data, determines the initial value of the impedance model, and obtains the initial impedance.

[0051] For the wavelet data, the electronic device may obtain the wavelet data corresponding to the incident angle extracted along the well bypass.

[0052] The number of M can be set and changed as needed, for example, M is 3 or 4. When M is 3, the incident angles can be 10°, 20°, and 30°, respectively.

[0053] Step 202: For each incident angle, the electronic device acquires first seismic data corresponding to multiple azimuth angles at the incident angle.

[0054] The azimuth is the angle between the line between the source and the detector and the true north direction. The number of multiple azimuths can be set and changed as needed, for example, the azimuths are 0°, 45°, 90° and 135°. When the M incident angles are 10°, 20° and 30°, and the azimuths are 0°, 45°, 90° and 135°, the electronic device obtains the first seismic data corresponding to the azimuths of 0°, 45°, 90° and 135° when the incident angle is 10°; the first seismic data corresponding to the azimuths of 0°, 45°, 90° and 135° when the incident angle is 20°; and the first seismic data corresponding to the azimuths of 0°, 45°, 90° and 135° when the incident angle is 30°.

[0055] It is important to note that the initial impedances for different azimuths at the same incident angle are the same. For example, when the incident angle is 10°, the initial impedances for azimuths of 0°, 45°, 90°, and 135° are the same.

[0056] Step 203: The electronic device superimposes the first seismic data corresponding to multiple azimuth angles at the incident angle to obtain second seismic data corresponding to the incident angle.

[0057] The electronic device superimposes the first seismic data corresponding to multiple azimuth angles at the incident angle to obtain the second seismic data corresponding to the incident angle.

[0058] Step 204: The electronic device performs a solution based on the second seismic data corresponding to the incident angle, the wavelet data corresponding to the incident angle, and the initial impedance corresponding to the incident angle to obtain the anisotropic impedance corresponding to the incident angle.

[0059] This step can be achieved by following the steps (1) to (3), including:

[0060] (1) The electronic device constructs the first objective function.

[0061] The first objective function is used to solve the anisotropic impedance corresponding to the incident angle through an iterative update solution method.

[0062] In the embodiment of the present application, the general form of the objective function is:

[0063]

[0064] Where L is the difference operator and argmin represents the minimum value.

[0065] For the first objective function, its expression can be:

[0066]

[0067] Among them, d1 represents the second earthquake data, G1 represents Wavelet data, m1 represents anisotropic impedance, and L1 is the first difference operator.

[0068] (2) The electronic device solves based on the first objective function and the initial impedance until a norm of a difference between the first product value and the second seismic data is within a first preset range.

[0069] Step (2) can be achieved by the following steps (2-1) to (2-6), including:

[0070] (2-1) The electronic device determines an initial gradient of the first objective function based on the initial impedance; and determines an initial search direction for iterative update of the first objective function based on the initial gradient.

[0071] The electronic device can determine the gradient of the first objective function by the following formula 1:

[0072] Formula 1:

[0073] Wherein, the superscript T represents the transpose of the matrix, k represents the number of iterations of the first objective function, and g k represents the gradient of the first objective function.

[0074] The initial gradient of the first objective function is the gradient corresponding to k = 1, that is, g1. The electronic device substitutes the initial impedance, the first differential operator, the wavelet data, and the second seismic data into Formula 1 to obtain the initial gradient.

[0075] The search direction of the iterative update of the first objective function can be expressed as p k When k=1, the initial search direction is p1, and p1=-g1, that is, the initial search direction is the inverse of the initial gradient. Therefore, the electronic device can determine the initial search direction according to the initial gradient.

[0076] (2-2) The electronic device determines an initial step size for iterative updating of the first objective function.

[0077] The step size can be λ k It means that when k=1, the corresponding step size is the initial step size, that is, λ1.

[0078] Under the constraints of the initial gradient and the initial impedance, the electronic device can determine the initial step length using a one-dimensional linear search algorithm. Of course, the electronic device can also determine the initial step length using other algorithms, which are not specifically limited.

[0079] (2-3) The electronic device updates the impedance based on the third relationship data, the initial impedance, the initial search direction, and the initial step size to obtain a first impedance.

[0080] The third relationship data is used to represent the functional relationship between the impedance outputted by the current round of iterative update and the impedance outputted by the previous round of iterative update.

[0081] In the embodiment of the present application, the third relationship data can be expressed as: m k+1 =m k +λ k p k ; Among them, m k+1 Represents the impedance of the current round of iterative update output, m k Indicates the impedance of the output of the previous iteration update.

[0082] The electronic device substitutes the initial impedance, the initial search direction and the initial step length into the third relationship data to obtain the first impedance.

[0083] (2-4) The electronic device determines a first gradient of a first objective function based on the first impedance.

[0084] The electronic device substitutes the first impedance into Formula 1 to obtain a first gradient.

[0085] (2-5) The electronic device updates the search direction and step size based on the initial gradient, the first gradient, the initial impedance, and the first impedance to obtain a first search direction and a first step size.

[0086] Step (2-5) can be achieved by the following steps (2-5-1) to (2-5-5), including:

[0087] (2-5-1) The electronic device constructs a first Hessian matrix based on the first objective function.

[0088] The electronic device can determine the second-order partial derivative of the first objective function and form a first Hessian matrix with the second-order partial derivative of the first objective function. The first Hessian matrix can be represented by H k Correspondingly, the third relational data can also be expressed as: mk+1 =m k +λ k p k =m k -λ k H k g k .

[0089] (2-5-2) The electronic device obtains a first vector for representing a first Hessian matrix based on the initial gradient, the first gradient and the fourth relationship data.

[0090] The fourth relational data is used to represent the functional relationship between the first vector, the initial gradient and the first gradient. The fourth relational data can be expressed as: k-1 =g k -g k-1 ; Among them, y k-1 Represents the first vector.

[0091] The electronic device substitutes the initial gradient and the first gradient into the fourth relationship data to obtain a first vector. When k=2, the first vector y1=g2-g1, where g2 represents the first gradient and g1 represents the initial gradient.

[0092] (2-5-3) The electronic device obtains a second vector for representing the first Hessian matrix based on the initial impedance, the first impedance and the fifth relationship data.

[0093] The fifth relationship data is used to represent the functional relationship between the second vector, the initial impedance and the first impedance. The first vector and the second vector are used to represent the first Hessian matrix.

[0094] The fifth relation data can be expressed as: s k-1 =m k -m k-1 ; Among them, s k-1 Represents the second vector.

[0095] The electronic device substitutes the initial impedance and the first impedance into the fifth relationship data to obtain a second vector. When k=2, the second vector s1=m2-m1, where m2 represents the first impedance and m1 represents the initial impedance.

[0096] (2-5-4) The electronic device updates the search direction based on the first vector, the second vector and the first gradient to obtain a first search direction.

[0097] make

[0098] When [i=k-1,k-2,……kz], iterate q k =q k-1 -α iy i ;

[0099] When [i=kz,k-z+1,......k-1], iterate r k =r k-1 +s i (α i -β i ), Where z represents the number of updates of the first Hessian matrix.

[0100] p k =-r k .

[0101] When k=2, q2=q1-α1y1. The electronic device substitutes s1, y1, and g2 into the formula to obtain α1.

[0102] r2=r1+s1(α1-β1), The electronic device obtains r2 according to the formula; and since p2=-r2, the electronic device obtains p2.

[0103] (2-5-5) The electronic device updates the step size based on the first impedance and the first gradient to obtain the first step size.

[0104] The electronic device updates the step length through a one-dimensional linear search algorithm based on the first impedance and the first gradient to obtain the first step length.

[0105] (2-6) The electronic device uses the first step length as the initial step length, the first search direction as the initial search direction, and the first impedance as the initial impedance, and executes the step of updating the impedance until the norm of the difference between the first product value and the second seismic data is within a first preset range.

[0106] The electronic device uses the first step length as the initial step length, the first search direction as the initial search direction, and the first impedance as the initial impedance, executes the above steps (2-1) to (2-5), updates the first vector and the second vector, and then updates the search direction and step length, and finally realizes the iterative update of the impedance until the norm of the difference between the first product value and the second seismic data is within a first preset range.

[0107] The first product value is the product of the current impedance, the first constant and the wavelet data, and the current impedance is the impedance output during the current round of iterative update process.

[0108] The first preset range can be set and changed as needed, and is not specifically limited thereto.

[0109] (3) The electronic device determines the current impedance as the anisotropic impedance corresponding to the incident angle.

[0110] The electronic device determines the anisotropic impedance corresponding to the incident angle based on the impedance when the condition is met, wherein the anisotropic impedance corresponding to the incident angle includes anisotropic impedances corresponding to multiple azimuth angles under the incident angle.

[0111] In an embodiment of the present application, an iterative algorithm is used to calculate the inverse of the pseudo-Hessian matrix to continuously approximate the inverse of the true Hessian matrix. The objective function is introduced into the second-order derivative to improve the accuracy of the inversion. The principle is to solve the objective function according to Taylor's first-order expansion. While the traditional BFGS (quasi-Newton) algorithm has a large amount of data storage, the L-BFGS (Limited-momery BFGS) algorithm simplifies the dimension of data storage, saves memory space, and makes the algorithm suitable for solving practical problems.

[0112] Step 205: The electronic device extracts a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the first relationship data.

[0113] The first relationship data is used to represent a functional relationship among the anisotropic impedance, the cosine function of the azimuth angle, and the first Fourier coefficient.

[0114] In the embodiment of the present application, the first relationship data can be expressed as:

[0115]

[0116] Among them, θ represents the incident angle, φ represents the azimuth angle, and u n (θ) represents the Fourier coefficient, n is the order of the Fourier coefficient, and the value of n can be 0, 2, or 4. The first Fourier coefficient is the Fourier coefficient corresponding to n=2, that is, u2(θ). X represents the number of azimuth angles, It represents the logarithmic value of the anisotropic impedance when the incident angle is θ and the azimuth angle is φ.

[0117] For each incident angle, when n=2, the electronic device substitutes the anisotropic impedances corresponding to the multiple azimuth angles under the incident angle and the multiple azimuth angles into the first relationship data to obtain the first Fourier coefficient u2(θ).

[0118] It should be noted that the electronic device needs to determine the first relationship data before step 205. The following describes the process of determining the first relationship data.

[0119] In the embodiment of the present application, the electronic device may determine the first relationship data through the following steps (1) to (4), including:

[0120] (1) The electronic device obtains the sixth relationship data and the seventh relationship data.

[0121] The sixth relationship data is used to represent the functional relationship between the reflection coefficient of the longitudinal wave, the incident angle and the azimuth angle, and the seventh relationship data is used to represent the functional relationship between the reflection coefficient of the longitudinal wave and the anisotropic impedance.

[0122] In the embodiment of the present application, the sixth relationship data can be expressed as:

[0123]

[0124] Among them, R pp (θ, φ) represents the longitudinal wave reflection coefficient of the inhomogeneous anisotropic HTI (Horizontal Transverse Isotropy) medium, ΔV p Represents the difference between the upper and lower layers of the longitudinal wave velocity, ΔV s Represents the difference between the shear wave velocities of the upper and lower layers, ΔV ρ It represents the difference between the upper and lower strata densities. represents the average value of the longitudinal wave velocity of the upper and lower layers, represents the average value of the shear wave velocity of the upper and lower layers, It represents the average value of the density of the upper and lower strata.

[0125] Δ N represents the normal weakness, Δ T represents the tangential weakness, represents the difference between the normal weaknesses of the upper and lower layers, represents the difference between the tangential weaknesses of the upper and lower layers, is the azimuth angle φ and the azimuth angle φ of the crack symmetry axis sym The difference.

[0126] A(θ), B(θ) and C(θ) are all expressions that vary with θ, where A(θ) = sec 2 θ, B(θ)=-8g sin 2 θ, C(θ)=1-4g sin 2 θ,

[0127] and Both are dependent on θ and The changing relationship,

[0128] In the embodiment of the present application, the seventh relationship data can be expressed as:

[0129]

[0130] Here, ΔEI(θ, φ) represents the difference in anisotropic impedance between the upper and lower layers, and EI(θ, φ) represents the average value of the anisotropic impedance of the upper and lower layers.

[0131] (2) The electronic device converts the sixth relationship data and the seventh relationship data based on the reflection coefficient of the longitudinal wave to obtain the eighth relationship data.

[0132] Since both the sixth and seventh relationship data include the reflection coefficient of the longitudinal wave, the sixth and seventh relationship data can be converted based on the reflection coefficient of the longitudinal wave to obtain the eighth relationship data. The eighth relationship data is used to represent the functional relationship between the anisotropic impedance, the incident angle, and the azimuth angle.

[0133] In the embodiment of the present application, the eighth relationship data can be expressed as:

[0134] L EI (θ, φ) = A(θ)L p +B(θ)L s +C(θ)L ρ +D(θ,φ)Δ N +E(θ,φ)Δ T ;

[0135] Among them, L EI (θ, φ) represents the ln logarithm of the anisotropic impedance when the incident angle is θ and the azimuth angle is φ.

[0136] (3) The electronic device performs Fourier series expansion processing on the eighth relational data to obtain ninth relational data.

[0137] The electronic device performs Fourier series expansion processing on the eighth relationship data based on the orthogonal law of trigonometric functions to obtain ninth relationship data.

[0138] In the embodiment of the present application, the ninth relationship data can be expressed as:

[0139] L EI (θ,φ)=u0(θ)+u2(θ)cos(2φ)+v2(θ)sin(2φ)+u4(θ)cos(4φ)+v4(θ)sin(4φ);

[0140] Among them, u0(θ) is the third Fourier coefficient, that is, the zero-order Fourier coefficient, u2(θ) and v2(θ) are the first Fourier coefficient and the second Fourier coefficient, that is, the second-order Fourier coefficient, u4(θ) and v4(θ) are the fourth-order Fourier coefficients.

[0141] And, u0(θ)=A(θ)L p +B(θ)Ls +C(θ)L ρ +F(θ)Δ N +G(θ)Δ T ;

[0142] u2(θ)=H(θ)cos(2φ sym )Δ N +I(θ)cos(2φ sym )Δ T ;

[0143] v2(θ)=H(θ)sin(2φ sym )Δ N +I(θ)sin(2φ sym )Δ T ;

[0144] in,

[0145]

[0146] H(θ)=g(2g-1)sin 2 θ+g(g-1)sin 2 θtan 2 θ,

[0147] I(θ)=g sin 2 θ.

[0148] (4) The electronic device performs discrete Fourier transform on the ninth relational data based on the cosine function of the azimuth angle to obtain the first relational data.

[0149] Step 206: The electronic device extracts a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the second relationship data.

[0150] The second relationship data is used to represent a functional relationship among the anisotropic impedance, the sine function of the azimuth angle, and the second Fourier coefficient.

[0151] In the embodiment of the present application, the second relationship data can be expressed as:

[0152]

[0153] The second Fourier coefficient is the Fourier coefficient corresponding to n=2, that is, v2(θ).

[0154] For each incident angle, when n=2, the electronic device substitutes the anisotropic impedances corresponding to the multiple azimuth angles under the incident angle and the multiple azimuth angles into the second relationship data to obtain the second Fourier coefficient v2(θ).

[0155] Before step 206, the electronic device needs to determine the second relationship data. The process of the electronic device determining the second relationship data may be: the electronic device performs discrete Fourier transform on the ninth relationship data based on the sine function of the azimuth angle to obtain the second relationship data.

[0156] Step 207: The electronic device determines N first incident angles from the M incident angles.

[0157] N is a positive integer, and N≤M, that is, N can be equal to M or less than M.

[0158] When N is equal to M, the electronic device determines all of the M incident angles as the first incident angle. When N is less than M, the electronic device selects N incident angles from the M incident angles as the first incident angle.

[0159] The electronic device may sort the M incident angles from largest to smallest or from smallest to largest, and select the N largest incident angles as the first incident angles based on the sorting results. Alternatively, the electronic device may randomly select N incident angles from the M incident angles as the first incident angles, which is not specifically limited.

[0160] For example, if M is 3 and the three incident angles are 10°, 20° and 30° respectively, the electronic device may select 20° and 30° as the first incident angle.

[0161] Step 208: The electronic device performs a solution based on the first matrix, the N first Fourier coefficients, and the N second Fourier coefficients to obtain the normal weakness and the tangential weakness of the crack.

[0162] This step can be achieved by following the steps (1) to (3), including:

[0163] (1) The electronic device constructs a second objective function based on the first matrix.

[0164] The first matrix is ​​used to represent the functional relationship between the first Fourier coefficient, the second Fourier coefficient, the normal weakness and the tangential weakness, and the second objective function is used to solve the normal weakness and the tangential weakness through an iterative update solution.

[0165] In the case of N first incident angles and J time sampling points, the electronic device converts u2(θ) and v2(θ) into a matrix form to obtain a first matrix, which can be expressed as:

[0166]

[0167] in,

[0168]

[0169] H(θN )=diag[H 1 (θ N ) ...... H J (θ N )], the symbol diag represents a diagonal matrix,

[0170] I(θ N )=diag[I 1 (θ N ) ...... I J (θ N )],

[0171]

[0172] The electronic device expresses the first matrix in vector form, and obtains Formula 2: d2=G2m2.

[0173] Each item in formula 2 corresponds one-to-one to each item in the first matrix, that is,

[0174] The electronic device constructs a second objective function based on formula 2. The expression of the second objective function is:

[0175]

[0176] Wherein, L2 represents the second difference operator.

[0177] (2) The electronic device solves based on the second objective function, the N first Fourier coefficients, and the N second Fourier coefficients until the second objective function satisfies the first convergence condition.

[0178] A first incident angle corresponds to a first Fourier coefficient and a second Fourier coefficient.

[0179] The electronic device performs total variation regularized inversion based on the second objective function, the N first Fourier coefficients, and the N second Fourier coefficients. The process of solving m2 based on the second objective function, the N first Fourier coefficients, and the N second Fourier coefficients is similar to the process of solving m1 in step 204. The electronic device also first determines the initial gradient, initial search direction, initial step size, initial normal weakness, and initial tangential weakness of the second objective function. Then, based on Formula 3, the initial gradient, initial normal weakness, initial tangential weakness, initial search direction, and initial step size, the normal weakness and tangential weakness are updated until the second objective function satisfies the first convergence condition.

[0180] Among them, the initial normal weakness and the initial tangential weakness can be 0. Formula 3 is used to express the output of the current round of iterative update: Update output from the previous iteration The functional relationship between them. The expression of formula 3 is the same as the third relationship data, both are m k+1 =m k +λ k p k , but m in formula 3 k+1 Indicates the output of the current round of iterative update m k Indicates the output of the last round of iteration update

[0181] In addition, the first convergence condition can be set and modified as needed and is not specifically limited thereto. For example, the first convergence condition is that the difference between the first function value and the second function value is within a second preset range, the first function value is the function value of the second objective function obtained in the current iteration, and the second function value is the function value of the second objective function obtained in the previous iteration.

[0182] (3) The electronic device determines the normal weakness and the tangential weakness based on the second objective function when the first convergence condition is satisfied.

[0183] The electronic device determines m2 in the second objective function when the first convergence condition is satisfied, and obtains normal weakness and tangential weakness.

[0184] Step 209: The electronic device performs crack prediction based on the normal weakness and the tangential weakness.

[0185] This step can be achieved by following the steps (1) to (4), including:

[0186] (1) The electronic device extracts a third Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the first relationship data.

[0187] For each incident angle, when n=0, the electronic device substitutes the anisotropic impedances corresponding to the multiple azimuth angles under the incident angle and the multiple azimuth angles into the first relationship data to obtain the third Fourier coefficient u0(θ).

[0188] (2) The electronic device determines Q second incident angles from the M incident angles.

[0189] Q is a positive integer, and Q≤M, that is, Q can be equal to M or less than M.

[0190] When Q is equal to M, the electronic device determines all M incident angles as the second incident angle. When Q is less than M, the electronic device selects Q incident angles from the M incident angles as the second incident angle.

[0191] The electronic device may select Q smallest incident angles as the second incident angles, or the electronic device may randomly select Q incident angles as the second incident angles, which is not specifically limited.

[0192] For example, if M is 3 and the three incident angles are 10°, 20° and 30° respectively, the electronic device may select 10° as the second incident angle.

[0193] (3) The electronic device solves based on the second matrix and Q third Fourier coefficients to obtain the longitudinal wave velocity and the shear wave velocity.

[0194] A second incident angle corresponds to a third Fourier coefficient.

[0195] The electronic device constructs a third objective function based on the second matrix; and performs total variation regularization inversion based on the third objective function and Q third Fourier coefficients until the third objective function satisfies a second convergence condition.

[0196] In the case of Q second incident angles and J time sampling points, the electronic device converts u0(θ) into a matrix form to obtain a second matrix, which can be expressed as:

[0197]

[0198] in,

[0199] A(θ Q )=diag[A 1 (θ Q ) ...... A J (θ Q )],

[0200] B(θ Q )=diag[B 1 (θ Q ) ...... B J (θ Q )],

[0201] C(θ Q )=diag[C 1 (θ Q ) ...... C J (θ Q )],

[0202]

[0203] represents the ln logarithm of the longitudinal wave velocity, Indicates the ln logarithm of the shear wave velocity, L ρ Indicates the ln logarithm of formation density.

[0204] The electronic device expresses the second matrix in vector form, and obtains Formula 4: d3=G3m3.

[0205] Each item in formula 4 corresponds one-to-one to each item in the first matrix, that is,

[0206] The electronic device constructs the third objective function based on formula 4. The expression of the third objective function is:

[0207]

[0208] Wherein, L3 represents the third difference operator.

[0209] The process of the electronic device solving m3 based on the third objective function and Q third Fourier coefficients is the same as the process of solving m1 in step 204, which also first determines the initial gradient, initial search direction, initial step size, initial longitudinal wave velocity, initial shear wave velocity and initial density of the third objective function, and then updates the initial longitudinal wave velocity and initial shear wave velocity based on Formula 5, the initial gradient, initial longitudinal wave velocity, initial shear wave velocity, initial density, initial search direction and initial step size until the third objective function meets the second convergence condition.

[0210] Among them, the initial shear wave velocity, initial longitudinal wave velocity and initial density can be 0, and Formula 5 is used to express the output of the current round of iterative update: Update output from the previous iteration The functional relationship between them. The expression of formula 5 is the same as the third relationship data, both are m k+1 =m k +λ k p k , but m in formula 5 k+1 Indicates the output of the current round of iterative update m k Indicates the output of the last round of iteration update

[0211] In addition, the second convergence condition can be set and modified as needed and is not specifically limited. For example, the second convergence condition is that the difference between the third function value and the fourth function value is within a third preset range, the third function value is the function value of the third objective function obtained in the current iteration, and the fourth function value is the function value of the third objective function obtained in the previous iteration.

[0212] The electronic device determines m3 in the third objective function when the second convergence condition is satisfied, and obtains the longitudinal wave velocity, the shear wave velocity and the formation density.

[0213] The key points of this application are: first, extracting Fourier coefficients of different orders and different value ranges, and inverting anisotropic parameters from them, which reduces the complexity of inversion and the number of input data; second, obtaining a relatively stable elastic parameter body with obvious interface through total variation regularization inversion; third, improving the convergence speed and accuracy of inversion through the L-BFGS algorithm.

[0214] (4) Electronic equipment predicts cracks based on longitudinal wave velocity, shear wave velocity, normal weakness, and tangential weakness.

[0215] The electronic device determines the ratio of the square of the shear wave velocity to the longitudinal wave velocity to obtain the velocity ratio; and determines the crack indication factor based on the tenth relationship data, the velocity ratio, the tangential weakness, and the normal weakness.

[0216] The crack indicator factor is used to indicate the development of cracks, and the tenth relationship data is used to represent the functional relationship between the crack indicator factor, normal weakness, tangential weakness, and velocity ratio.

[0217] In the embodiment of the present application, the tenth relationship data can be expressed as:

[0218]

[0219] in, represents the crack indicator factor, γ b represents the speed ratio, which can be expressed as

[0220] A high fracture indicator factor indicates saturation with oil and gas, while a low value indicates saturation with water. Therefore, electronic equipment can predict the development of fractures based on the size of the fracture indicator factor.

[0221] In an embodiment of the present application, the electronic device can also determine the crack density based on Formula 6, the velocity ratio, and the tangential weakness. The crack density is used to describe the crack development characteristics per unit area. Formula 6 is used to express the functional relationship between the crack density, the velocity ratio, and the tangential weakness. Formula 6 can be expressed as:

[0222]

[0223] Where w represents the crack density.

[0224] Since the value of the anisotropy parameter is small, this application first extracts the Fourier coefficients through a two-step method, which can eliminate the coupling between the parameters, and then groups the parameters to reduce the number of parameters to be solved. The complexity of the inversion is reduced by Fourier coefficient decomposition. At the same time, sparse regularization constraints are introduced to establish the objective function of the inverse problem, and the L-BFGS algorithm is used to solve the inversion, resulting in an inversion effect with fast convergence speed, small storage space occupation and good effect, thereby improving the accuracy and efficiency of the inversion and achieving a fine characterization of the fracture reservoir.

[0225] In order to more clearly illustrate the inversion process of fracture weakness and velocity, Figure 3 The above process is explained below. Figure 3 As shown, for example, the electronic device obtains pre-stack gathers with incident angles, namely, incident angle 1 gather, incident angle 2 gather, and incident angle 3 gather, with azimuth 1 / 2 of incident angle 1 stacked, azimuth 1 / 2 of incident angle 2 stacked, and azimuth 1 / 2 of incident angle 3 stacked. Total variation regularized inversion is then performed based on the fractional angle wavelet data of incident angle 1, fractional angle wavelet data of incident angle 2, fractional angle wavelet data of incident angle 3, fractional angle wavelet data of incident angle 4, and the stacked seismic data to obtain the impedance volume of incident angle 1 at azimuth 1, the impedance volume of incident angle 1 at azimuth 2, the impedance volume of incident angle 2 at azimuth 1, the impedance volume of incident angle 2 at azimuth 2, the impedance volume of incident angle 3 at azimuth 1, the impedance volume of incident angle 3 at azimuth 2, the impedance volume of incident angle 4 at azimuth 1, and the impedance volume of incident angle 4 at azimuth 2. Then, the first Fourier coefficient, the second Fourier coefficient and the third Fourier coefficient are extracted from the impedance body at each incident angle and each azimuth angle. The crack weakness is inverted based on the first Fourier coefficient and the second Fourier coefficient, i.e., the second-order Fourier coefficient, to obtain the normal weakness and tangential weakness; the velocity is inverted based on the third Fourier coefficient, i.e., the zero-order coefficient, to obtain the shear wave velocity and longitudinal wave velocity.

[0226] The data was tested using the method provided in this application. The test results can be found in Figure 4 . Figure 4 (a) is a synthetic seismic record with a signal-to-noise ratio of 2. Figure 4 (b) is the impedance result corresponding to an incident angle of 30°. Figure 4 (c) is the inversion result of the P-wave velocity based on the zero-order Fourier coefficient. Figure 4 (d) and (e) are the normal weakness inversion results and tangential weakness inversion results based on the second-order Fourier coefficient, respectively. The black line represents the true value and the gray line represents the inversion result. Figure 4 It can be seen from the figure that under the condition of low signal-to-noise ratio, the inversion method provided by the present application can also obtain relatively stable inversion results and accurately characterize the anisotropic parameters.

[0227] See also Figure 5 ,from Figure 5 It can be seen from the figure that by analyzing the condition number of the first matrix inversion, it can be determined that the method is stable.

[0228] The method provided by this application is applied to a certain exploration area in Sichuan Basin, my country. The azimuth and angle-divided seismic data and the angle-divided wavelet data in the exploration area are superimposed as input data to obtain the azimuth and angle-divided impedance body, such as Figure 6 As shown. Figure 6 It can be seen from the figure that the inversion result is consistent with the logging curve.

[0229] See also Figure 7 , Figure 7 (a) is the impedance inversion result corresponding to an incident angle of 10°. Figure 7 (b) is the impedance inversion result corresponding to an incident angle of 20°. Figure 7 (c) is the impedance inversion result corresponding to an incident angle of 30°. Figure 7 It can be seen that the impedance inversion results at each incident angle are consistent with the logging data.

[0230] See also Figure 8 , Figure 8 (a) is the inversion result of the longitudinal wave velocity, Figure 8 (b) is the inversion result of normal weakness, Figure 8 (c) is the inversion result of tangential weakness. Figure 8 It can be seen from the figure that the inversion results of P-wave velocity are consistent with the logging data, and the inversion results of normal weakness and tangential weakness can effectively distinguish the fracture development areas.

[0231] An embodiment of the present application provides a crack prediction method that first inverts to obtain azimuth and angle-specific impedance inversion results, then extracts the first and second Fourier coefficients from the impedance inversion results, and finally performs inversion based on the first and second Fourier coefficients to obtain the normal and tangential weaknesses of the crack. This method decouples the first and second Fourier coefficients, eliminating the coupling between the parameters, reducing the number of parameters to be solved, and improving the stability of the inversion process, thereby improving the accuracy of the normal and tangential weaknesses obtained through inversion, and thus improving the accuracy of crack prediction.

[0232] Figure 9 This is a schematic diagram of the structure of a crack prediction device provided in an embodiment of the present application, see Figure 9 , the device comprises:

[0233] The first acquisition module 901 is used to obtain initial impedance and wavelet data corresponding to M incident angles of seismic waves; wherein M is a positive integer, and each incident angle corresponds to one initial impedance and one wavelet data;

[0234] A second acquisition module 902 is configured to acquire, for each incident angle, first seismic data corresponding to a plurality of azimuth angles under the incident angle;

[0235] A superposition module 903 is configured to superimpose first seismic data corresponding to multiple azimuth angles under an incident angle to obtain second seismic data corresponding to the incident angle;

[0236] A first processing module 904 is configured to solve based on the second seismic data corresponding to the incident angle, the wavelet data corresponding to the incident angle, and the initial impedance corresponding to the incident angle to obtain anisotropic impedance corresponding to the incident angle;

[0237] A first extraction module 905 is configured to extract a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the first relationship data, wherein the first relationship data is configured to represent a functional relationship between the anisotropic impedance, a cosine function of the azimuth angle, and the first Fourier coefficient;

[0238] A second extraction module 906 is configured to extract a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the second relationship data, wherein the second relationship data is configured to represent a functional relationship between the anisotropic impedance, a sine function of the azimuth angle, and the second Fourier coefficient;

[0239] A first determining module 907 is configured to determine N first incident angles from the M incident angles, wherein N is a positive integer and N≤M;

[0240] A second processing module 908 is configured to perform a solution based on the first matrix, N first Fourier coefficients, and N second Fourier coefficients to obtain normal weakness and tangential weakness of the crack; wherein the first matrix is ​​configured to represent a functional relationship between the first Fourier coefficients, the second Fourier coefficients, the normal weakness, and the tangential weakness; and each first incident angle corresponds to one first Fourier coefficient and one second Fourier coefficient.

[0241] The prediction module 909 is used to predict cracks based on normal weakness and tangential weakness.

[0242] In one possible implementation, the first processing module 904 is used to construct a first objective function, which is used to solve the anisotropic impedance corresponding to the incident angle through an iterative update solution; the solution is performed based on the first objective function and the initial impedance until the norm of the difference between the first product value and the second seismic data is within a first preset range; wherein the first product value is the product of the current impedance, the first constant and the wavelet data, and the current impedance is the impedance output during the current round of iterative update; and the current impedance is determined as the anisotropic impedance corresponding to the incident angle.

[0243] In another possible implementation, the first processing module 904 is used to determine an initial gradient of the first objective function based on the initial impedance; determine an initial search direction for iterative update of the first objective function based on the initial gradient; determine an initial step size for iterative update of the first objective function; update the impedance based on third relationship data, the initial impedance, the initial search direction, and the initial step size to obtain a first impedance; wherein the third relationship data is used to represent a functional relationship between the impedance output by the current round of iterative update and the impedance output by the previous round of iterative update; determine a first gradient of the first objective function based on the first impedance; update the search direction and the step size based on the initial gradient, the first gradient, the initial impedance, and the first impedance to obtain a first search direction and a first step size; and use the first step size as the initial step size, the first search direction as the initial search direction, and the first impedance as the initial impedance to perform the step of updating the impedance until the norm of the difference between the first product value and the second seismic data is within a first preset range.

[0244] In another possible implementation, the first processing module 904 is used to construct a first Hessian matrix based on the first objective function; obtain a first vector for characterizing the first Hessian matrix based on the initial gradient, the first gradient and the fourth relationship data; wherein the fourth relationship data is used to represent the functional relationship between the first vector, the initial gradient and the first gradient; obtain a second vector for characterizing the first Hessian matrix based on the initial impedance, the first impedance and the fifth relationship data; wherein the fifth relationship data is used to represent the functional relationship between the second vector, the initial impedance and the first impedance; update the search direction based on the first vector, the second vector and the first gradient to obtain a first search direction; and update the step size based on the first impedance and the first gradient to obtain a first step size.

[0245] In another possible implementation, the apparatus further includes:

[0246] a third acquisition module, configured to acquire sixth relationship data and seventh relationship data; wherein the sixth relationship data is used to represent a functional relationship between a longitudinal wave reflection coefficient, an incident angle, and an azimuth angle, and the seventh relationship data is used to represent a functional relationship between a longitudinal wave reflection coefficient and anisotropic impedance;

[0247] a conversion module, configured to convert the sixth relationship data and the seventh relationship data based on a reflection coefficient of a longitudinal wave to obtain eighth relationship data; wherein the eighth relationship data is used to represent a functional relationship between anisotropic impedance, an incident angle, and an azimuth angle;

[0248] a third processing module, configured to perform Fourier series expansion processing on the eighth relationship data to obtain ninth relationship data; wherein the ninth relationship data is used to represent a functional relationship between anisotropic impedance, incident angle, sine function of azimuth angle, and cosine function of azimuth angle;

[0249] The first transformation module is configured to perform a discrete Fourier transform on the ninth relational data based on a cosine function of the azimuth angle to obtain first relational data.

[0250] In another possible implementation, the apparatus further includes:

[0251] The second transformation module is used to perform discrete Fourier transform on the ninth relational data based on the sine function of the azimuth angle to obtain second relational data.

[0252] In another possible implementation, the second processing module 908 is used to construct a second objective function based on the first matrix; wherein the second objective function is used to solve the normal weakness and the tangential weakness through an iterative update solution method; the solution is performed based on the second objective function, N first Fourier coefficients, and N second Fourier coefficients until the second objective function satisfies the first convergence condition; based on the second objective function when the first convergence condition is satisfied, the normal weakness and the tangential weakness are determined.

[0253] In another possible implementation, the prediction module 909 is used to extract the third Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the first relationship data; determine Q second incident angles among the M incident angles; wherein Q is a positive integer and Q≤M; solve based on the second matrix and the Q third Fourier coefficients to obtain the longitudinal wave velocity and the shear wave velocity; wherein the second matrix is ​​used to represent the functional relationship between the third Fourier coefficient, the longitudinal wave velocity and the shear wave velocity, and one second incident angle corresponds to one third Fourier coefficient; and perform crack prediction based on the longitudinal wave velocity, the shear wave velocity, the normal weakness and the tangential weakness.

[0254] In another possible implementation, the prediction module 909 is used to determine the square ratio of the shear wave velocity to the longitudinal wave velocity to obtain the velocity ratio; and determine the crack indication factor based on the tenth relationship data, the velocity ratio, the tangential weakness, and the normal weakness; wherein the crack indication factor is used to indicate the development of the crack, and the tenth relationship data is used to represent the functional relationship between the crack indication factor, the normal weakness, the tangential weakness, and the velocity ratio.

[0255] An embodiment of the present application provides a crack prediction device that first inverts to obtain azimuth and angle-specific impedance inversion results, then extracts first and second Fourier coefficients from the impedance inversion results, and finally performs inversion based on the first and second Fourier coefficients to obtain the normal and tangential weaknesses of the crack. This device decouples the first and second Fourier coefficients, eliminating the coupling between the parameters, reducing the number of parameters to be solved, and improving the stability of the inversion process, thereby increasing the accuracy of the normal and tangential weaknesses obtained through inversion and, consequently, the accuracy of crack prediction.

[0256] It should be noted that the crack prediction device provided in the above embodiment is only illustrated by the division of the above functional modules when predicting cracks. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. In addition, the crack prediction device provided in the above embodiment and the crack prediction method embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0257] refer to Figure 10 , Figure 10 The following is a block diagram of a terminal 1000 according to an exemplary embodiment of the present application. Terminal 1000 may be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. Terminal 1000 may also be referred to as user equipment, portable terminal, laptop terminal, desktop terminal, or other similar names.

[0258] Typically, the terminal 1000 includes a processor 1001 and a memory 1002 .

[0259] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1001 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 1001 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 1001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0260] Memory 1002 may include one or more computer-readable storage media, which may be non-transitory. Memory 1002 may also include high-speed random access memory and non-volatile memory, such as one or more magnetic disk storage devices or flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in memory 1002 is used to store at least one program code, which is executed by processor 1001 to implement the operations performed by the terminal in the crack prediction method provided in the method embodiments of this application.

[0261] In some embodiments, terminal 1000 may optionally include a peripheral device interface 1003 and at least one peripheral device. Processor 1001, memory 1002, and peripheral device interface 1003 may be connected via a bus or signal lines. Each peripheral device may be connected to peripheral device interface 1003 via a bus, signal lines, or circuit boards. Specifically, the peripheral device may include at least one of a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007, and a power supply 1008.

[0262] The peripheral device interface 1003 can be used to connect at least one I / O (Input / Output)-related peripheral device to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1001, the memory 1002, and the peripheral device interface 1003 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0263] The RF circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1004 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the RF circuit 1004 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 RF circuit 1004 can communicate with other terminals via at least one wireless communication protocol. Such wireless communication protocols include, but are not limited to, the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1004 may also include circuitry related to Near Field Communication (NFC), which is not limited in this application.

[0264] The display screen 1005 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 1005 is a touch screen display, the display screen 1005 also has the ability to collect touch signals on the surface or above the surface of the display screen 1005. The touch signal can be input as a control signal to the processor 1001 for processing. At this time, the display screen 1005 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, there can be one display screen 1005, which is set on the front panel of the terminal 1000; in other embodiments, there can be at least two display screens 1005, which are respectively set on different surfaces of the terminal 1000 or in a folding design; in other embodiments, the display screen 1005 can be a flexible display screen, which is set on the curved surface or folding surface of the terminal 1000. Even more, the display screen 1005 can be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0265] The camera assembly 1006 is used to capture images or videos. Optionally, the camera assembly 1006 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 1006 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.

[0266] The audio circuit 1007 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 that are input into the processor 1001 for processing, or input into the RF circuit 1004 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, there may be multiple microphones, each located in different parts of the terminal 1000. The microphone may also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert electrical signals from the processor 1001 or the RF circuit 1004 into sound waves. The speaker may be a traditional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert electrical signals into sound waves audible to humans, but also convert electrical signals into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0267] Power supply 1008 is used to power various components in terminal 1000. Power supply 1008 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1008 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is charged via a wired line, while a wireless rechargeable battery is charged via a wireless coil. The rechargeable battery can also support fast charging technology.

[0268] In some embodiments, the terminal 1000 further includes one or more sensors 1009 . The one or more sensors 1009 include, but are not limited to, an acceleration sensor 1010 , a gyroscope sensor 1011 , a pressure sensor 1012 , an optical sensor 1013 , and a proximity sensor 1014 .

[0269] The accelerometer 1010 can detect the magnitude of acceleration along the three coordinate axes of the coordinate system established by the terminal 1000. For example, the accelerometer 1010 can be used to detect the components of gravity acceleration along the three coordinate axes. The processor 1001 can control the display screen 1005 to display the user interface in a landscape or portrait view based on the gravity acceleration signal collected by the accelerometer 1010. The accelerometer 1010 can also be used to collect game or user motion data.

[0270] The gyroscope sensor 1011 can detect the body orientation and rotation angle of the terminal 1000. The gyroscope sensor 1011 can cooperate with the acceleration sensor 1010 to collect the user's 3D movements on the terminal 1000. Based on the data collected by the gyroscope sensor 1011, the processor 1001 can implement the following functions: motion sensing (such as changing the UI based on the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0271] The pressure sensor 1012 can be set on the side frame of the terminal 1000 and / or the lower layer of the display screen 1005. When the pressure sensor 1012 is set on the side frame of the terminal 1000, it can detect the user's grip signal of the terminal 1000, and the processor 1001 performs left and right hand recognition or shortcut operations based on the grip signal collected by the pressure sensor 1012. When the pressure sensor 1012 is set on the lower layer of the display screen 1005, the processor 1001 controls the operable controls on the UI interface based on the user's pressure operation on the display screen 1005. Operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0272] Optical sensor 1013 is used to detect ambient light intensity. In one embodiment, processor 1001 can control the display brightness of display screen 1005 based on the ambient light intensity detected by optical sensor 1013. Specifically, when the ambient light intensity is high, the display brightness of display screen 1005 is increased; when the ambient light intensity is low, the display brightness of display screen 1005 is decreased. In another embodiment, processor 1001 can also dynamically adjust the shooting parameters of camera assembly 1006 based on the ambient light intensity detected by optical sensor 1013.

[0273] Proximity sensor 1014, also known as a distance sensor, is typically located on the front panel of terminal 1000. Proximity sensor 1014 is used to detect the distance between the user and the front of terminal 1000. In one embodiment, when proximity sensor 1014 detects that the distance between the user and the front of terminal 1000 is gradually decreasing, processor 1001 controls display screen 1005 to switch from the screen-on state to the screen-off state. When proximity sensor 1014 detects that the distance between the user and the front of terminal 1000 is gradually increasing, processor 1001 controls display screen 1005 to switch from the screen-off state to the screen-on state.

[0274] Those skilled in the art will understand that Figure 10 The structure shown in the figure does not constitute a limitation on the terminal 1000, and the terminal 1000 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0275] In an exemplary embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores at least one program code. The at least one program code is loaded and executed by a processor to implement the crack prediction method in the above embodiment.

[0276] In an exemplary embodiment, a computer program product is further provided. The computer program product stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the crack prediction method in the above embodiment.

[0277] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by a program to instruct the relevant hardware, 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.

[0278] The above description is only for the purpose of facilitating those skilled in the art to understand the technical solution of this application and is not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included in the scope of protection of this application.

Claims

1. A crack prediction method, characterized in that: The method comprises: Acquire initial impedance and wavelet data corresponding to M incident angles of the seismic wave; wherein M is a positive integer, and one incident angle corresponds to one initial impedance and one wavelet data; For each incident angle, obtaining first seismic data corresponding to a plurality of azimuth angles at the incident angle; superimposing first seismic data corresponding to a plurality of azimuths at the incident angle to obtain second seismic data corresponding to the incident angle; Solving based on second seismic data corresponding to the incident angle, wavelet data corresponding to the incident angle, and initial impedance corresponding to the incident angle to obtain anisotropic impedance corresponding to the incident angle; Extracting a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on first relationship data; wherein the first relationship data is used to represent a functional relationship between the anisotropic impedance, a cosine function of the azimuth angle, and the first Fourier coefficient; Extracting a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on second relationship data; wherein the second relationship data is used to represent a functional relationship between the anisotropic impedance, the sine function of the azimuth angle, and the second Fourier coefficient; Determining N first incident angles from the M incident angles; wherein N is a positive integer and N≤M; Solving based on a first matrix, N first Fourier coefficients, and N second Fourier coefficients to obtain normal weakness and tangential weakness of the crack; wherein the first matrix is ​​used to represent a functional relationship between the first Fourier coefficient, the second Fourier coefficient, the normal weakness, and the tangential weakness; and one first incident angle corresponds to one first Fourier coefficient and one second Fourier coefficient; Crack prediction is performed based on the normal weakness and the tangential weakness.

2. The method according to claim 1, characterized in that The obtaining of the anisotropic impedance corresponding to the incident angle by solving the second seismic data corresponding to the incident angle, the wavelet data corresponding to the incident angle, and the initial impedance corresponding to the incident angle includes: Constructing a first objective function, wherein the first objective function is used to solve the anisotropic impedance corresponding to the incident angle through an iterative update solution method; Solving based on the first objective function and the initial impedance until a norm of a difference between a first product value and the second seismic data is within a first preset range; wherein the first product value is the product of a current impedance, a first constant, and the wavelet data, and the current impedance is the impedance output during a current round of iterative updating; The current impedance is determined as the anisotropic impedance corresponding to the incident angle.

3. The method according to claim 2, characterized in that The solving based on the first objective function and the initial impedance until a norm of a difference between the first product value and the second seismic data is within a first preset range includes: Determining an initial gradient of the first objective function based on the initial impedance; determining an initial search direction for iterative updating of the first objective function based on the initial gradient; Determining an initial step size for iterative updating of the first objective function; Based on the third relationship data, the initial impedance, the initial search direction, and the initial step size, the impedance is updated to obtain a first impedance; wherein the third relationship data is used to represent a functional relationship between the impedance outputted by the current round of iterative update and the impedance outputted by the previous round of iterative update; determining a first gradient of the first objective function based on the first impedance; Based on the initial gradient, the first gradient, the initial impedance, and the first impedance, updating a search direction and a step size to obtain a first search direction and a first step size; Taking the first step length as the initial step length, the first search direction as the initial search direction, and the first impedance as the initial impedance, the step of updating the impedance is performed until the norm of the difference between the first product value and the second seismic data is within the first preset range.

4. The method according to claim 3, characterized in that The updating of the search direction and the step size based on the initial gradient, the first gradient, the initial impedance, and the first impedance to obtain a first search direction and a first step size includes: Based on the first objective function, construct a first Hessian matrix; Based on the initial gradient, the first gradient, and fourth relationship data, obtaining a first vector for representing the first Hessian matrix; wherein the fourth relationship data is used to represent a functional relationship between the first vector, the initial gradient, and the first gradient; Based on the initial impedance, the first impedance, and fifth relationship data, obtaining a second vector for representing the first Hessian matrix; wherein the fifth relationship data is used to represent a functional relationship between the second vector, the initial impedance, and the first impedance; updating the search direction based on the first vector, the second vector, and the first gradient to obtain the first search direction; The step size is updated based on the first impedance and the first gradient to obtain the first step size.

5. The method according to claim 1, wherein The process of determining the first relationship data includes: Acquiring sixth relationship data and seventh relationship data; wherein the sixth relationship data is used to represent the functional relationship between the reflection coefficient of the longitudinal wave, the incident angle, and the azimuth angle, and the seventh relationship data is used to represent the functional relationship between the reflection coefficient of the longitudinal wave and the anisotropic impedance; Based on the reflection coefficient of the longitudinal wave, the sixth relationship data and the seventh relationship data are converted to obtain eighth relationship data; wherein the eighth relationship data is used to represent the functional relationship between anisotropic impedance, incident angle and azimuth angle; Performing Fourier series expansion processing on the eighth relationship data to obtain ninth relationship data; wherein the ninth relationship data is used to represent a functional relationship between anisotropic impedance, incident angle, sine function of azimuth angle, and cosine function of azimuth angle; Based on the cosine function of the azimuth angle, a discrete Fourier transform is performed on the ninth relationship data to obtain the first relationship data.

6. The method according to claim 5, characterized in that The process of determining the second relationship data includes: Based on the sine function of the azimuth angle, a discrete Fourier transform is performed on the ninth relational data to obtain the second relational data.

7. The method according to claim 1, characterized in that The solution based on the first matrix, N first Fourier coefficients, and N second Fourier coefficients is performed to obtain the normal weakness and tangential weakness of the crack, including: Based on the first matrix, constructing a second objective function; wherein the second objective function is used to solve the normal weakness and the tangential weakness by an iterative update solution method; Solving based on the second objective function, the N first Fourier coefficients, and the N second Fourier coefficients until the second objective function satisfies a first convergence condition; The normal weakness and the tangential weakness are determined based on a second objective function when the first convergence condition is satisfied.

8. The method according to claim 1, characterized in that The performing of crack prediction based on the normal weakness and the tangential weakness includes: extracting a third Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on the first relationship data; Determining Q second incident angles from the M incident angles; wherein Q is a positive integer and Q≤M; Solving based on the second matrix and Q third Fourier coefficients to obtain the longitudinal wave velocity and the shear wave velocity; wherein the second matrix is ​​used to represent the functional relationship between the third Fourier coefficients, the longitudinal wave velocity, and the shear wave velocity, and each second incident angle corresponds to one third Fourier coefficient; Crack prediction is performed based on the longitudinal wave velocity, the shear wave velocity, the normal weakness, and the tangential weakness.

9. The method according to claim 8, characterized in that The performing of crack prediction based on the longitudinal wave velocity, the shear wave velocity, the normal weakness, and the tangential weakness includes: determining a ratio of the square of the shear wave velocity to the longitudinal wave velocity to obtain a velocity ratio; Based on the tenth relationship data, the velocity ratio, the tangential weakness and the normal weakness, a crack indication factor is determined; wherein the crack indication factor is used to indicate the development of cracks, and the tenth relationship data is used to represent the functional relationship between the crack indication factor, normal weakness, tangential weakness and velocity ratio.

10. A crack prediction device, characterized in that: The device comprises: A first acquisition module is configured to acquire initial impedance and wavelet data corresponding to M incident angles of seismic waves; wherein M is a positive integer, and each incident angle corresponds to one initial impedance and one wavelet data; A second acquisition module is configured to acquire, for each incident angle, first seismic data corresponding to a plurality of azimuth angles at the incident angle; a superposition module, configured to superimpose first seismic data corresponding to a plurality of azimuths at the incident angle to obtain second seismic data corresponding to the incident angle; A first processing module is configured to solve based on the second seismic data corresponding to the incident angle, the wavelet data corresponding to the incident angle, and the initial impedance corresponding to the incident angle to obtain the anisotropic impedance corresponding to the incident angle; a first extraction module, configured to extract a first Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on first relationship data; wherein the first relationship data is used to represent a functional relationship between the anisotropic impedance, a cosine function of the azimuth angle, and the first Fourier coefficient; a second extraction module, configured to extract a second Fourier coefficient from the anisotropic impedance corresponding to the incident angle based on second relationship data; wherein the second relationship data is used to represent a functional relationship between the anisotropic impedance, a sine function of the azimuth angle, and the second Fourier coefficient; A first determining module, configured to determine N first incident angles from the M incident angles; wherein N is a positive integer and N≤M; a second processing module, configured to solve based on the first matrix, N first Fourier coefficients, and N second Fourier coefficients to obtain normal weakness and tangential weakness of the crack; wherein the first matrix is ​​used to represent a functional relationship between the first Fourier coefficients, the second Fourier coefficients, the normal weakness, and the tangential weakness; and each first incident angle corresponds to one first Fourier coefficient and one second Fourier coefficient; A prediction module is used to predict cracks based on the normal weakness and the tangential weakness.

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