Seismic data processing method, apparatus and device, and computer readable storage medium

Abstract

The invention discloses a seismic data processing method, device and equipment and a computer readable storage medium, and belongs to the technical field of seismic exploration. The method comprises: according to seismic data of a to-be-explored area, obtaining area data of the to-be-explored area, the area data comprising position information of each point included in the to-be-explored area and an initial coherence attribute value of each point; preprocessing the initial coherence attribute value of each point to obtain a target coherence attribute value of each point; determining a Gaussian-Laplacian filtering operator of each point according to the position information of each point; and according to the Gaussian-Laplacian filtering operator of each point, smoothing sharpening processing is carried out on the target coherence attribute value of each point to obtain a fault enhancement value of each point, and the fault enhancement value of each point is used for indicating fault information of the to-be-explored area. The accuracy of the fault enhancement value of each point determined by the method is higher.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of seismic exploration technology, and in particular to a method, device, equipment and computer-readable storage medium for processing seismic data. Background Art

[0002] Seismic exploration generally includes three stages: seismic data acquisition, seismic data processing and seismic data interpretation. In the seismic data acquisition stage, due to the influence of many factors, the signal-to-noise ratio of the collected seismic data is low and the imaging quality is poor, which makes the fault information obtained by processing the collected seismic data in the seismic data processing stage relatively vague, affecting the detection and identification of faults, and further affecting the accuracy of seismic data interpretation.

[0003] Therefore, there is an urgent need for a seismic data processing method to process the seismic data so as to make the obtained fault information clearer, thereby improving the detection and identification of faults and improving the accuracy of seismic data interpretation. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and computer-readable storage medium for processing seismic data, which can be used to solve the problems in related technologies. The technical solution is as follows:

[0005] On the one hand, an embodiment of the present application provides a method for processing seismic data, the method comprising:

[0006] According to the seismic data of the area to be explored, regional data of the area to be explored are obtained, wherein the regional data includes the position information of each point included in the area to be explored and the initial coherent attribute value of each point, and the initial coherent attribute value of any point is used to indicate the correlation between the any point and its adjacent points;

[0007] Preprocessing the initial coherent attribute values ​​of the respective points to obtain target coherent attribute values ​​of the respective points;

[0008] Determine the Gaussian-Laplacian filter operator of each point according to the position information of each point, and the Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherent attribute value of any point;

[0009] According to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the fault enhancement value of each point, and the fault enhancement value of each point is used to indicate the fault information of the area to be explored.

[0010] In a possible implementation, the regional data further includes a longitudinal survey line direction inclination angle of each point and a transverse survey line direction inclination angle of each point, the longitudinal survey line direction inclination angle of any point is used to indicate a longitudinal angle between the any point and an observation route for collecting the seismic data, and the transverse survey line direction inclination angle of any point is used to indicate a transverse angle between the any point and the observation route;

[0011] The step of performing smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point includes:

[0012] According to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the intermediate enhancement value of each point;

[0013] The fault enhancement value of each point is determined according to the intermediate enhancement value of each point, the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point.

[0014] In a possible implementation, performing smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the intermediate enhancement value of each point includes:

[0015] For a first point among the points, determining a first area according to position information of the first point, wherein the first point is any point among the points;

[0016] According to the Gaussian-Laplacian filter operators respectively corresponding to the plurality of second points included in the first area, smoothing and sharpening processing is performed on the target coherent attribute values ​​respectively corresponding to the plurality of second points to obtain smoothing and sharpening results of the respective second points;

[0017] Determining an intermediate enhancement value of the first point according to the smoothing and sharpening results of each of the second points;

[0018] Determining the fault enhancement value of each point according to the intermediate enhancement value of each point, the longitudinal survey line direction inclination angle of each point and the transverse survey line direction inclination angle of each point comprises:

[0019] Determine a unit normal vector of a reflection layer at the first point according to an inclination angle of a longitudinal survey line direction of the first point and an inclination angle of a transverse survey line direction of the first point;

[0020] Determine a fault unit normal vector of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points;

[0021] The fault enhancement value of the first point is determined according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point.

[0022] In a possible implementation manner, determining the fault enhancement value of the first point according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point, and the intermediate enhancement value of the first point includes:

[0023] determining an angle between the reflection layer and the fault at the first point according to a unit normal vector of the reflection layer at the first point and a unit normal vector of the fault at the first point;

[0024] determining, according to an angle between a reflection layer and a fault at the first point, an edge filtering operator of the first point, wherein the edge filtering operator of the first point is used to perform edge filtering processing on an intermediate enhancement value of the first point;

[0025] According to the fringing filter operator of the first point, fringing filter processing is performed on the intermediate enhancement value of the first point to obtain the fault enhancement value of the first point.

[0026] In a possible implementation, determining the fault unit normal vector of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points includes:

[0027] Determine a second-order moment tensor of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points, wherein the second-order moment tensor of the first point is used to determine a fault unit normal vector of the first point;

[0028] determining a plurality of eigenvalues ​​corresponding to the second-order moment tensor of the first point;

[0029] The eigenvector corresponding to the target eigenvalue is used as the fault unit normal vector of the first point, and the target eigenvalue is the eigenvalue with the smallest value among multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

[0030] In a possible implementation manner, preprocessing the initial coherent attribute values ​​of the respective points to obtain target coherent attribute values ​​of the respective points includes:

[0031] Determine a first attribute value and a second attribute value according to the initial coherent attribute values ​​of the various points, wherein the first attribute value is the initial coherent attribute value with the largest value among the initial coherent attribute values ​​of the various points, and the second attribute value is the initial coherent attribute value with the smallest value among the initial coherent attribute values ​​of the various points;

[0032] The target coherent attribute value of each point is determined according to the first attribute value, the second attribute value and the initial coherent attribute value of each point.

[0033] On the other hand, an embodiment of the present application provides a seismic data processing device, the device comprising:

[0034] An acquisition module, used for acquiring regional data of the area to be explored according to seismic data of the area to be explored, wherein the regional data includes position information of each point included in the area to be explored and initial coherent attribute values ​​of each point, and the initial coherent attribute value of any point is used to indicate the correlation between the any point and its adjacent points;

[0035] A preprocessing module, used for preprocessing the initial coherent attribute value of each point to obtain the target coherent attribute value of each point;

[0036] A determination module, used to determine the Gaussian-Laplacian filter operator of each point according to the position information of each point, and the Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherent attribute value of any point;

[0037] A processing module is used to perform smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point, and the fault enhancement value of each point is used to indicate the fault information of the area to be explored.

[0038] In a possible implementation, the regional data further includes a longitudinal survey line direction inclination angle of each point and a transverse survey line direction inclination angle of each point, the longitudinal survey line direction inclination angle of any point is used to indicate a longitudinal angle between the any point and an observation route for collecting the seismic data, and the transverse survey line direction inclination angle of any point is used to indicate a transverse angle between the any point and the observation route;

[0039] The processing module is used to perform smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the intermediate enhancement value of each point; and determine the fault enhancement value of each point according to the intermediate enhancement value of each point, the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point.

[0040] In a possible implementation, the processing module is used to determine a first area for a first point among the points according to the position information of the first point, where the first point is any point among the points; perform smoothing and sharpening processing on the target coherence attribute values ​​corresponding to the multiple second points respectively according to the Gaussian-Laplacian filter operators respectively corresponding to the multiple second points included in the first area to obtain smoothing and sharpening results of each second point; determine the intermediate enhancement value of the first point according to the smoothing and sharpening results of each second point; determine the reflection layer unit normal vector of the first point according to the inclination angle of the longitudinal survey line direction of the first point and the inclination angle of the transverse survey line direction of the first point; determine the fault unit normal vector of the first point according to the position information respectively corresponding to the multiple second points and the target coherence attribute values ​​respectively corresponding to the multiple second points; determine the fault enhancement value of the first point according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point.

[0041] In a possible implementation, the processing module is used to determine the angle between the reflection layer and the fault at the first point based on the unit normal vector of the reflection layer at the first point and the unit normal vector of the fault at the first point; determine the edging filter operator of the first point based on the angle between the reflection layer and the fault at the first point, the edging filter operator of the first point is used to perform edging filter processing on the intermediate enhancement value of the first point; and perform edging filter processing on the intermediate enhancement value of the first point based on the edging filter operator of the first point to obtain the fault enhancement value of the first point.

[0042] In a possible implementation, the processing module is used to determine the second-order moment tensor of the first point based on the position information corresponding to the multiple second points and the target coherent attribute values ​​corresponding to the multiple second points, the second-order moment tensor of the first point is used to determine the fault unit normal vector of the first point; determine multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point; use the eigenvector corresponding to the target eigenvalue as the fault unit normal vector of the first point, the target eigenvalue is the eigenvalue with the smallest value among the multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

[0043] In a possible implementation, the preprocessing module is used to determine a first attribute value and a second attribute value based on the initial coherent attribute values ​​of the various points, wherein the first attribute value is the initial coherent attribute value with the largest value among the initial coherent attribute values ​​of the various points, and the second attribute value is the initial coherent attribute value with the smallest value among the initial coherent attribute values ​​of the various points; and determine the target coherent attribute value of the various points based on the first attribute value, the second attribute value and the initial coherent attribute values ​​of the various points.

[0044] On the other hand, an embodiment of the present application provides a computer device, which includes 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 so that the computer device implements any of the above-mentioned methods for processing seismic data.

[0045] 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 so that a computer implements any of the above-mentioned methods for processing seismic data.

[0046] On the other hand, a computer program or a computer program product is also provided, wherein at least one computer instruction is stored in the computer program or the computer program product, and the at least one computer instruction is loaded and executed by a processor so that the computer implements any of the above-mentioned methods for processing seismic data.

[0047] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0048] The technical solution provided in the embodiment of the present application determines the Gaussian-Laplacian filter operator of each point according to the position information of each point; according to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the fault enhancement value of each point. Since the Gaussian-Laplacian filter operator is a merging operator, it can not only smooth the noise data in the seismic data, but also sharpen the fault information in the seismic data. Smoothing and sharpening are performed simultaneously, so that the fault enhancement value of each point obtained is more accurate, and the fault information obtained according to the fault enhancement value of each point is clearer, thereby improving the detection and identification of faults in the seismic data and improving the accuracy of the interpretation of the seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 It is a schematic diagram of an implementation environment of a method for processing seismic data provided in an embodiment of the present application;

[0051] Figure 2 is a flow chart of a method for processing seismic data provided in an embodiment of the present application;

[0052] Figure 3is a schematic diagram of a seismic profile of an area to be explored provided in an embodiment of the present application;

[0053] Figure 4 It is a coherent attribute profile of a region to be explored provided in an embodiment of the present application;

[0054] Figure 5 It is a cross-sectional view of the inclination angle of the area to be explored in the direction of the longitudinal survey line provided in an embodiment of the present application;

[0055] Figure 6 It is a cross-sectional view of the inclination angle of the area to be explored in the direction of the cross-survey line provided in an embodiment of the present application;

[0056] Figure 7 is a schematic diagram of a fault enhancement attribute profile of a region to be explored provided in an embodiment of the present application;

[0057] Figure 8 It is a structural schematic diagram of a seismic data processing device provided in an embodiment of the present application;

[0058] Fig. 9 It is a structural diagram of a terminal device provided in an embodiment of the present application;

[0059] Fig.10 It is a structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0061] Figure 1 Schematic diagram of an implementation environment of a method for processing seismic data provided in an embodiment of the present application, such as Figure 1 As shown, the implementation environment includes: a computer device 101, which may be a terminal device or a server, and the present application embodiment does not limit this. The computer device 101 is used to execute the seismic data processing method provided in the present application embodiment.

[0062] Optionally, the computer device 101 is a terminal device, which can be any electronic device product that can interact with a user through one or more methods such as a keyboard, a touchpad, a remote control, voice interaction, or a handwriting device. For example, PC (Personal Computer), mobile phone, smart phone, PDA (Personal Digital Assistant), wearable device, PPC (Pocket PC), tablet computer, smart car machine, smart TV, smart speaker, smart watch, etc.

[0063] The terminal device may generally refer to one of a plurality of terminal devices. This embodiment is only illustrated by taking the terminal device as an example. Those skilled in the art may know that the number of the terminal devices may be more or less. For example, the terminal device may be only one, or the terminal devices may be dozens or hundreds, or more. The embodiment of this application does not limit the number and device type of the terminal devices.

[0064] When the computer device 101 is a server, the server is a single server, or a server cluster consisting of multiple servers, or any one of a cloud computing platform and a virtualization center, which is not limited in the embodiments of the present application. The server is connected to the terminal device through a wired network or a wireless network. The server has a data receiving function, a data processing function, and a data sending function. Of course, the server may also have other functions, which are not limited in the embodiments of the present application.

[0065] Those skilled in the art should understand that the above-mentioned terminal devices and servers are for illustration only, and other existing or future terminal devices or servers, if applicable to the present application, should also be included in the scope of protection of the present application and are incorporated herein by reference.

[0066] The present application embodiment provides a method for processing seismic data, which can be applied to the above Figure 1 The implementation environment shown is Figure 2 Taking the flowchart of a method for processing seismic data provided by the embodiment of the present application as an example, the method can be Figure 1 The computer device 101 in the embodiment is executed. Figure 2 As shown, the method includes the following steps 201 to 204.

[0067] In step 201, regional data of the area to be explored is acquired based on seismic data of the area to be explored.

[0068] The regional data includes the location information of each point in the area to be explored and the initial coherent attribute value of each point. The initial coherent attribute value of any point is used to indicate the correlation between any point and any adjacent point. The initial coherent attribute value of any point is proportional to the correlation between any point and any adjacent point. That is, the higher the initial coherent attribute value of any point, the more correlated any point is with any adjacent point; conversely, the lower the initial coherent attribute value of any point, the less correlated any point is with any adjacent point.

[0069] In the exemplary embodiment of the present application, the seismic data of the area to be explored includes a seismic profile of the area to be explored, and the seismic profile of the area to be explored is identified to obtain regional data of the area to be explored. Figure 3It is a schematic diagram of a seismic profile of an area to be explored provided in an embodiment of the present application.

[0070] In a possible implementation, after obtaining the initial coherent attribute values ​​of each point included in the area to be explored, the coherent attribute profile of the area to be explored can also be obtained according to the final coherent attribute values ​​of each point. Figure 4 It is a coherent attribute profile of a region to be explored provided in an embodiment of the present application.

[0071] In step 202, the initial coherent attribute value of each point is preprocessed to obtain the target coherent attribute value of each point.

[0072] Optionally, after obtaining the initial coherent attribute value of each point in the above step 201, the initial coherent attribute value of each point may be preprocessed to obtain the target coherent attribute value of each point, and the target coherent attribute value of each point is the coherent attribute value after preprocessing the initial coherent attribute value of each point.

[0073] The process of preprocessing the initial coherent attribute values ​​of each point to obtain the target coherent attribute values ​​of each point includes: determining the first attribute value and the second attribute value according to the initial coherent attribute values ​​of each point; and determining the target coherent attribute value of each point according to the first attribute value, the second attribute value and the initial coherent attribute values ​​of each point.

[0074] Among them, the first attribute value is the initial coherent attribute value with the largest value among the initial coherent attribute values ​​of each point, and the second attribute value is the initial coherent attribute value with the smallest value among the initial coherent attribute values ​​of each point. Exemplarily, the area to be explored includes 5 points. Among them, the initial coherent attribute value of the first point is 0.1, the initial coherent attribute value of the second point is 0.2, the initial coherent attribute value of the third point is 0.9, the initial coherent attribute value of the fourth point is 0.5, and the initial coherent attribute value of the fifth point is 0.6, then the first attribute value is 0.9, and the second attribute value is 0.1.

[0075] Optionally, the process of determining the target coherent attribute value of each point based on the first attribute value, the second attribute value and the initial coherent attribute value of each point includes: determining the intermediate attribute value of each point based on the first attribute value, the second attribute value and the initial coherent attribute value of each point; and determining the target coherent attribute value of each point based on the intermediate attribute value of each point.

[0076] The process of determining the intermediate attribute value of each point based on the first attribute value, the second attribute value and the initial coherent attribute value of each point includes: for any point among the points, determining the first difference between the initial coherent attribute value and the second attribute value of any point; determining the second difference between the first attribute value and the second attribute value; and taking the quotient between the first difference and the second difference as the intermediate attribute value of any point.

[0077] Optionally, the intermediate attribute value of each point is determined according to the first attribute value, the second attribute value and the initial coherent attribute value of each point according to the following formula (1).

[0078]

[0079] In the above formula (1), c n is the intermediate attribute value of any point, c is the initial coherent attribute value of any point, c max is the first attribute value, c min Is the second attribute value.

[0080] For example, the first attribute value is 0.9, the second attribute value is 0.1, and the initial coherent attribute value of any point is 0.2. Then, according to the above formula (1), the intermediate attribute value of any point is determined to be 0.125.

[0081] The intermediate attribute values ​​of each point obtained by the above method are all in [0, 1].

[0082] In a possible implementation, the process of determining the target coherent attribute value of each point according to the intermediate attribute value of each point includes: taking the intermediate attribute value of each point as the target coherent attribute value of each point. Alternatively, for any point among the points, determine the third difference between the intermediate attribute value of any point and the first numerical value; determine the fourth difference between the second numerical value and the intermediate attribute value of any point; based on the third difference being less than the fourth difference, take the fourth difference as the target coherent attribute value of any point; based on the third difference being not less than the fourth difference, take the intermediate attribute value of any point as the target coherent attribute value of any point. Wherein, the third numerical value is less than the fourth numerical value, and the third numerical value and the fourth numerical value are set according to experience, or adjusted based on the implementation environment, which is not limited in the embodiments of the present application. For example, the third numerical value is 0 and the fourth numerical value is 1.

[0083] Exemplarily, the intermediate attribute value of any point is 0.125, the third difference between the intermediate attribute value of any point and the first value is 0.125, and the fourth difference between the second value and the intermediate attribute value of any point is 0.875. Since the third difference is smaller than the fourth difference, the fourth difference is used as the target coherent attribute value of any point, that is, the target coherent attribute value of any point is 0.875.

[0084] In step 203, the Gaussian-Laplacian filter operator of each point is determined according to the position information of each point.

[0085] Among them, the Gaussian-Laplacian filter operator of any point is used to smooth and sharpen the target coherent attribute value of any point. The position information of each point refers to the position of each point in three-dimensional space, and the position information of each point includes the position information of each point in the first direction, the position information of the second direction and the position information of the third direction. The first direction, the second direction and the third direction are different. For example, the first direction is horizontal, the second direction is vertical, and the third direction is vertical.

[0086] In a possible implementation, before determining the Gaussian-Laplacian filter operator of each point based on the position information of each point, it is necessary to first obtain the Gaussian-Laplacian filter function. The process of obtaining the Gaussian-Laplacian filter function includes: obtaining a three-dimensional Gaussian kernel function; performing a Laplace operator operation on the three-dimensional Gaussian kernel function to obtain the Gaussian-Laplacian filter function.

[0087] Optionally, the three-dimensional Gaussian kernel function used in the embodiment of the present application can be expressed as the following formula (2).

[0088]

[0089] In the above formula (2), G(x, y, z) is a three-dimensional Gaussian kernel function, exp is an exponential function with the natural constant e as the base, σ is the standard deviation, and (x, y, z) is the position information. The standard deviation is a factor that affects the smoothness of the three-dimensional Gaussian kernel function.

[0090] In a possible implementation, the three-dimensional Gaussian kernel function shown in the above formula (2) is subjected to Laplace operator operation, and the obtained Gaussian-Laplace filter function can be expressed as the following formula (3).

[0091]

[0092] Since the second-order partial derivatives of the three-dimensional Gaussian kernel function in the x, y, and z directions can be expressed as the following formula (4), the Gaussian-Laplacian filter function obtained by substituting the content of formula (4) into formula (3) can be expressed as the following formula (5).

[0093]

[0094]

[0095] In a possible implementation, after obtaining the Gaussian-Laplace filter function, the position information of each point is substituted into the Gaussian-Laplace function to obtain the Gaussian-Laplace filter operator of each point. The Gaussian-Laplace filter function has rotational symmetry, and the edge enhancement effect is achieved by smoothing and sharpening the edge of the seismic profile.

[0096] In step 204, the target coherent attribute value of each point is smoothed and sharpened according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point.

[0097] The fault enhancement value of each point is used to indicate the fault information of the area to be explored.

[0098] In a possible implementation, the target coherence attribute value of each point is smoothed and sharpened according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point. The process includes: according to the Gaussian-Laplacian filter operator of each point, the target coherence attribute value of each point is smoothed and sharpened to obtain the intermediate enhancement value of each point; according to the intermediate enhancement value of each point, the fault enhancement value of each point is determined.

[0099] According to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the intermediate enhancement value of each point. The process includes: for the first point among the points, according to the position information of the first point, a first area is determined, and the first point is any point among the points; according to the Gaussian-Laplacian filter operators corresponding to the multiple second points included in the first area, the target coherent attribute values ​​corresponding to the multiple second points are smoothed and sharpened to obtain the smoothed and sharpened results of each second point; according to the smoothed and sharpened results of each second point, the intermediate enhancement value of the first point is determined.

[0100] Optionally, the Gaussian-Laplacian filter operator corresponding to each second point is multiplied by the target coherence attribute value of each second point to obtain a smoothed and sharpened result of each second point, and the sum of the smoothed and sharpened results of each second point is used as the intermediate enhancement value of the first point.

[0101] Among them, according to the position information of the first point, the process of determining the first area includes: taking the first point indicated by the position information of the first point as the center and the first length as the side length, determining the filter window, and taking the area corresponding to the filter window as the first area. The first area includes M points, and M is a positive integer greater than or equal to 1. Optionally, the first length is set based on experience or adjusted according to the implementation environment, which is not limited in the embodiment of the present application.

[0102] Optionally, the intermediate enhancement value of the first point is determined according to the following formula (6) based on the Gaussian-Laplacian filter operators corresponding to the multiple second points included in the first area and the target coherence property values ​​corresponding to the multiple second points.

[0103]

[0104] In the above formula (6), a log is the intermediate enhancement value of the first point, ΔGm (x, y, z) is the Gaussian-Laplacian filter operator of the mth second point, a m is the target coherent attribute value of the mth second point, and M is the number of second points included in the first area.

[0105] After the fault enhancement value of each point is determined, the process of determining the fault enhancement value of each point according to the intermediate enhancement value of each point includes: using the intermediate enhancement value of each point as the fault enhancement value of each point.

[0106] In a possible implementation, the regional data also includes the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point. The inclination angle of the longitudinal survey line direction of any point is used to indicate the longitudinal angle between any point and the observation route for collecting seismic data, and the inclination angle of the transverse survey line direction of any point is used to indicate the transverse angle between any point and the observation route. It is also possible to obtain a profile of the inclination angle of the longitudinal survey line direction of the area to be explored based on the inclination angle of the longitudinal survey line direction of each point, and to obtain a profile of the inclination angle of the transverse survey line direction of the area to be explored based on the inclination angle of the transverse survey line direction of each point. Figure 5 A cross-sectional view of the inclination angle of the area to be explored in the longitudinal survey line direction provided in an embodiment of the present application, such as Figure 6 A cross-sectional view of the inclination angle of the area to be explored in the direction of the cross-survey line provided in an embodiment of the present application.

[0107] The process of determining the fault enhancement value of each point according to the intermediate enhancement value of each point may also include: determining the fault enhancement value of each point according to the intermediate enhancement value of each point, the longitudinal survey line direction inclination of each point and the transverse survey line direction inclination of each point.

[0108] Optionally, the process of determining the fault enhancement value of each point based on the intermediate enhancement value of each point, the inclination of the longitudinal survey line direction of each point and the inclination of the transverse survey line direction of each point includes: for the first point among the points, determining the reflection layer unit normal vector of the first point based on the inclination of the longitudinal survey line direction of the first point and the inclination of the transverse survey line direction of the first point; determining the fault unit normal vector of the first point based on the position information corresponding to multiple second points and the target coherent attribute values ​​corresponding to the multiple second points; determining the fault enhancement value of the first point based on the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point.

[0109] The reflection layer unit normal vector of the first point refers to the unit normal vector corresponding to the reflection layer where the first point is located, and the fault unit normal vector of the first point refers to the unit normal vector corresponding to the fault where the first point is located. The process of determining the intermediate enhancement value of the first point has been described in the above process and will not be repeated here.

[0110] In a possible implementation, the reflection layer unit normal vector of the first point is determined according to the following formula (7) based on the inclination angle of the longitudinal survey line direction of the first point and the inclination angle of the transverse survey line direction of the first point.

[0111]

[0112] In the above formula (7), N is the unit normal vector of the reflection layer at the first point, p is the inclination angle of the longitudinal survey line direction of the first point, and q is the inclination angle of the transverse survey line direction of the first point.

[0113] In a possible implementation, the process of determining the fault unit normal vector of the first point based on the position information corresponding to the multiple second points and the target coherent attribute values ​​corresponding to the multiple second points includes: determining the second-order moment tensor of the first point based on the position information corresponding to the multiple second points and the target coherent attribute values ​​corresponding to the multiple second points, the second-order moment tensor of the first point being used to determine the fault unit normal vector of the first point; determining multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point; using the eigenvector corresponding to the target eigenvalue as the fault unit normal vector of the first point, the target eigenvalue being the eigenvalue with the smallest value among the multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

[0114] Among them, the second-order moment tensor of the first point is a 3*3 matrix, and the second-order moment tensor of the first point includes 9 elements, namely the first element C 11 , the second element C 12 、The third element C 13 、The fourth element C 21 、The Fifth Element C 22 、The sixth element C 23 、The seventh element C 31 、The eighth element C 32 and the ninth element C 33 .

[0115] According to the position information corresponding to the multiple second points and the target coherent attribute values ​​corresponding to the multiple second points, the elements included in the second-order moment tensor of the first point are determined; and then according to the elements included in the second-order moment tensor of the first point, the second-order moment tensor of the first point is determined.

[0116] The process of determining each element included in the second-order moment tensor of the first point according to the position information corresponding to the plurality of second points and the target coherent property values ​​corresponding to the plurality of second points includes: determining the position information of the center point of the first region; determining the distance between each second point and the center point of the first region in the first direction, the distance between each second point and the center point of the first region in the second direction, and the distance between each second point and the center point of the first region in the third direction according to the position information of each second point and the position information of the center point of the first region; determining the first element according to the distance between each second point and the center point of the first region in the first direction and the target coherent property value of each second point. Determine the second element and the fourth element according to the distance between each second point and the center point of the first region in the first direction, the distance between each second point and the center point of the first region in the second direction, and the target coherent property value of each second point. Determine the third element and the seventh element according to the distance between each second point and the center point of the first region in the first direction, the distance between each second point and the center point of the first region in the third direction, and the target coherent property value of each second point. Determine the fifth element according to the distance between each second point and the center point of the first region in the second direction and the target coherent property value of each second point. The sixth element and the eighth element are determined according to the distance between each second point and the center point of the first area in the second direction, the distance between each second point and the center point of the first area in the third direction, and the target coherence attribute value of each second point. The ninth element is determined according to the distance between each second point and the center point of the first area in the third direction and the target coherence attribute value of each second point.

[0117] In a possible implementation, the process of determining the distance between each second point and the center point of the first area in the first direction, the distance between each second point and the center point of the first area in the second direction, and the distance between each second point and the center point of the first area in the third direction based on the position information of each second point and the position information of the center point of the first area includes: for any second point among the second points, taking the difference between the component in the first direction included in the position information of any second point and the component in the first direction included in the position information of the center point of the first area as the distance between any second point and the center point of the first area in the first direction; taking the difference between the component in the second direction included in the position information of any second point and the component in the second direction included in the position information of the center point of the first area as the distance between any second point and the center point of the first area in the second direction; taking the difference between the component in the third direction included in the position information of any second point and the component in the third direction included in the position information of the center point of the first area as the distance between any second point and the center point of the first area in the third direction.

[0118] Optionally, the elements included in the second-order moment tensor of the first point are determined according to the following formula (8) based on the position information corresponding to the plurality of second points and the target coherent attribute values ​​corresponding to the plurality of second points.

[0119]

[0120] In the above formula (8), C ij Any element of the second-order moment tensor of the second point, d im is the distance between the mth second point and the center point of the first area in the i-th direction, d jm is the distance between the mth second point and the center point of the first area in the jth direction, a m is the target coherent attribute value of the mth second point. M is the number of second points included in the first area, and the values ​​of i and j are 1, 2, and 3.

[0121] Optionally, after determining the second-order moment tensor of the first point according to the above process, the second-order moment tensor of the first point is solved to obtain multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point; the eigenvector corresponding to the target eigenvalue is used as the fault unit normal vector of the first point. The target eigenvalue is the eigenvalue with the smallest value among the multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point. The following formula (9) is the second-order moment tensor of the first point.

[0122]

[0123] In the above formula (9), C is the second-order moment tensor of the first point, C 11 is the first element, C 12 is the second element, C 13 is the third element, C 21 is the fourth element, C 22 C is the fifth element. 23 is the sixth element, C 31 The seventh element, C 32 is the eighth element, C 33 The ninth element.

[0124] The second-order moment tensor of the first point is solved according to the following formula (10) to obtain multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

[0125] |C-λE|=0 Formula (10)

[0126] In the above formula (10), C is the second-order moment tensor of the first point, E is the unit matrix, and the dimension of E is the same as the dimension of the second-order moment tensor of the first point. Formula (10) is solved to obtain multiple eigenvalues ​​λ corresponding to the second-order moment tensor of the first point.

[0127] After obtaining multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point, determine the target eigenvalue with the smallest value among the multiple eigenvalues, determine the eigenvector corresponding to the target eigenvalue, and use the eigenvector corresponding to the target eigenvalue as the fault unit normal vector of the first point.

[0128] Optionally, the eigenvector corresponding to the target eigenvalue is determined according to the following formula (11).

[0129] |C-λ1E|X=0 Formula (11)

[0130] In the above formula (11), C is the second-order moment tensor of the first point, λ1 is the target eigenvalue, and E is the unit matrix. Formula (11) is solved to obtain the eigenvector X corresponding to the target eigenvalue.

[0131] In a possible implementation, after obtaining the reflection layer unit normal vector of the first point and the fault unit normal vector of the first point, the process of determining the fault enhancement value of the first point according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point includes: determining the angle between the reflection layer and the fault of the first point according to the reflection layer unit normal vector of the first point and the fault unit normal vector of the first point; determining the edging filter operator of the first point according to the angle between the reflection layer and the fault of the first point, the edging filter operator being used to perform edging filter processing on the intermediate enhancement value of the first point; and performing edging filter processing on the intermediate enhancement value of the first point according to the edging filter operator of the first point to obtain the fault enhancement value of the first point.

[0132] Among them, the process of determining the angle between the reflection layer and the fault at the first point according to the reflection layer unit normal vector of the first point and the fault unit normal vector of the first point includes: determining the first modulus of the reflection layer unit normal vector of the first point, determining the second modulus of the fault unit normal vector of the first point; determining the first inner product of the reflection layer unit normal vector of the first point and the fault unit normal vector of the first point; determining the second product of the first modulus and the second modulus; determining the quotient between the first inner product and the second product, and taking the arc cosine of the quotient between the first inner product and the second product as the angle between the reflection layer and the fault at the first point.

[0133] Optionally, according to the unit normal vector of the reflection layer at the first point and the unit normal vector of the fault at the first point, the angle between the reflection layer and the fault at the first point is determined according to the following formula (12).

[0134]

[0135] In the above formula (12), θ is the angle between the reflection layer and the fault at the first point, N is the unit normal vector of the reflection layer at the first point, V3 is the unit normal vector of the fault at the first point, and cos -1 is the arccosine.

[0136] Since the first modulus of the unit normal vector of the reflection layer at the first point is 1, and the second modulus of the unit normal vector of the fault at the first point is 1, the above formula (12) can be simplified to obtain the following formula (13). According to the unit normal vector of the reflection layer at the first point and the unit normal vector of the fault at the first point, the angle between the reflection layer and the fault at the first point is determined according to the following formula (13).

[0137] θ=cos -1 (N·V3) Formula (13)

[0138] In a possible implementation, the process of determining the edging filter operator of the first point according to the angle between the reflection layer and the fault of the first point includes: obtaining a first boundary value and a second boundary value, the first boundary value and the second boundary value are respectively any one of the maximum boundary value and the minimum boundary value of the edging filter, and the first boundary value and the second boundary value are different. According to the first boundary value, the second boundary value and the angle between the reflection layer and the fault of the first point, the edging filter operator of the first point is determined. Exemplarily, if the maximum boundary value of the edging filter is 25 and the minimum boundary value is 10, then the first boundary value is 10 and the second boundary value is 25, or the first boundary value is 25 and the second boundary value is 10.

[0139] Optionally, the process of determining the edge filtering operator of the first point according to the first boundary value, the second boundary value and the angle between the reflection layer and the fault at the first point includes the following three cases:

[0140] Case 1: Based on the fact that the first boundary value is less than the second boundary value and the angle between the reflection layer and the fault at the first point is not greater than the first boundary value, the first reference value is used as the edge filtering operator of the first point; based on the fact that the first boundary value is less than the second boundary value and the angle between the reflection layer and the fault at the first point is not less than the second boundary value, the second reference value is used as the edge filtering operator of the first point; based on the fact that the first boundary value is less than the second boundary value and the angle between the reflection layer and the fault at the first point is between the first boundary value and the second boundary value, the edge filtering operator of the first point is determined according to the first boundary value, the second boundary value and the angle between the reflection layer and the fault at the first point.

[0141] The first reference value and the second reference value are set based on experience or adjusted according to the implementation environment, which is not limited in the embodiment of the present application. For example, the first reference value is 0 and the second reference value is 1.

[0142] Optionally, based on the fact that the first boundary value is less than the second boundary value and the angle between the reflection layer and the fault at the first point is between the first boundary value and the second boundary value, the edging filter operator of the first point is determined according to the first boundary value, the second boundary value and the angle between the reflection layer and the fault at the first point according to the following formula (14).

[0143]

[0144] In the above formula (14), f(θ) is the edge filter operator of the first point, θ is the angle between the reflection layer and the fault at the first point, θ1 is the first boundary value, and θ2 is the second boundary value.

[0145] Case 2: Based on the fact that the first boundary value is greater than the second boundary value and the angle between the reflection layer and the fault at the first point is not greater than the second boundary value, the second reference value is used as the edging filter operator for the first point; based on the fact that the first boundary value is greater than the second boundary value and the angle between the reflection layer and the fault at the first point is not less than the first boundary value, the first reference value is used as the edging filter operator for the first point; based on the fact that the first boundary value is greater than the second boundary value and the angle between the reflection layer and the fault at the first point is between the first boundary value and the second boundary value, the edging filter operator for the first point is determined according to the first boundary value, the second boundary value and the angle between the reflection layer and the fault at the first point.

[0146] Optionally, based on the fact that the first boundary value is greater than the second boundary value and the angle between the reflection layer and the fault at the first point is between the first boundary value and the second boundary value, the edging filter operator of the first point is determined according to the first boundary value, the second boundary value and the angle between the reflection layer and the fault at the first point according to the following formula (15).

[0147]

[0148] In the above formula (15), f(θ) is the edge filter operator of the first point, θ is the angle between the reflection layer and the fault at the first point, θ1 is the first boundary value, and θ2 is the second boundary value.

[0149] Case 3: Based on the fact that the first boundary value and the second boundary value are equal, the second reference value is used as the edge filtering operator of the first point.

[0150] In a possible implementation, the process of determining the fault enhancement value of the first point according to the edge filter operator of the first point and the intermediate enhancement value of the first point includes: taking the product of the edge filter operator of the first point and the intermediate enhancement value of the first point as the fault enhancement value of the first point.

[0151] Optionally, the fault enhancement value of the first point is determined according to the following formula (16) based on the edge filtering operator of the first point and the intermediate enhancement value of the first point.

[0152] a f =a log ×f(θ) Formula (16)

[0153] In the above formula (16), a f is the fault enhancement value of the first point, f(θ) is the edge filter operator of the first point, a log is the intermediate enhancement value of the first point.

[0154] Optionally, after obtaining the fault enhancement value of each point, the fault enhancement attribute profile of the area to be explored can also be obtained according to the fault enhancement value of each point, such as Figure 7 A schematic diagram of a fault enhancement attribute profile of a region to be explored provided in an embodiment of the present application. Figure 4 and Figure 7 It can be found from the schematic diagrams before and after fault enhancement that the image before fault enhancement processing ( Figure 4 ) have several obvious defects, including random background noise, coherent noise caused by stratum shadows and fault step artifacts. Figure 7 ) has been significantly reshaped, and the linear anomaly characteristics indicating the fault have been significantly enhanced.

[0155] The above method determines the Gaussian-Laplacian filter operator of each point according to the position information of each point; according to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the fault enhancement value of each point. Since the Gaussian-Laplacian filter operator is a merging operator, it can not only smooth the noise data in the seismic data, but also sharpen the fault information in the seismic data. Smoothing and sharpening are performed simultaneously, so that the fault enhancement value of each point is more accurate, and the fault information obtained according to the fault enhancement value of each point is clearer, thereby improving the detection and identification of faults in the seismic data and improving the accuracy of the interpretation of the seismic data.

[0156] Figure 8 FIG. 1 is a schematic diagram of a structure of a seismic data processing device provided in an embodiment of the present application. Figure 8 As shown, the device comprises:

[0157] The acquisition module 801 is used to acquire regional data of the area to be explored based on the seismic data of the area to be explored, wherein the regional data includes the position information of each point included in the area to be explored and the initial coherent attribute value of each point, and the initial coherent attribute value of any point is used to indicate the correlation between any point and any adjacent point of the point;

[0158] A preprocessing module 802 is used to preprocess the initial coherent attribute value of each point to obtain the target coherent attribute value of each point;

[0159] A determination module 803 is used to determine the Gaussian-Laplacian filter operator of each point according to the position information of each point, and the Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherent attribute value of any point;

[0160] The processing module 804 is used to perform smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point. The fault enhancement value of each point is used to indicate the fault information of the area to be explored.

[0161] In a possible implementation, the regional data also includes the longitudinal survey line direction inclination angle of each point and the transverse survey line direction inclination angle of each point. The longitudinal survey line direction inclination angle of any point is used to indicate the longitudinal angle between any point and the observation route for collecting seismic data, and the transverse survey line direction inclination angle of any point is used to indicate the transverse angle between any point and the observation route;

[0162] Processing module 804 is used to smooth and sharpen the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the intermediate enhancement value of each point; determine the fault enhancement value of each point according to the intermediate enhancement value of each point, the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point.

[0163] In a possible implementation, the processing module 804 is used to determine, for a first point among the points, a first area according to the position information of the first point, where the first point is any point among the points; perform smoothing and sharpening processing on the target coherence attribute values ​​corresponding to the multiple second points respectively according to the Gaussian-Laplacian filter operators respectively corresponding to the multiple second points included in the first area, to obtain smoothing and sharpening results of each second point; determine the intermediate enhancement value of the first point according to the smoothing and sharpening results of each second point; determine the reflection layer unit normal vector of the first point according to the inclination angle of the longitudinal survey line direction of the first point and the inclination angle of the transverse survey line direction of the first point; determine the fault unit normal vector of the first point according to the position information respectively corresponding to the multiple second points and the target coherence attribute values ​​respectively corresponding to the multiple second points; determine the fault enhancement value of the first point according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point.

[0164] In one possible implementation, the processing module 804 is used to determine the angle between the reflection layer and the fault at the first point based on the unit normal vector of the reflection layer at the first point and the unit normal vector of the fault at the first point; determine the edging filter operator of the first point based on the angle between the reflection layer and the fault at the first point, and the edging filter operator of the first point is used to perform edging filter processing on the intermediate enhancement value of the first point; and perform edging filter processing on the intermediate enhancement value of the first point based on the edging filter operator of the first point to obtain the fault enhancement value of the first point.

[0165] In one possible implementation, the processing module 804 is used to determine the second-order moment tensor of the first point based on the position information corresponding to the multiple second points and the target coherent attribute values ​​corresponding to the multiple second points, the second-order moment tensor of the first point is used to determine the fault unit normal vector of the first point; determine multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point; use the eigenvector corresponding to the target eigenvalue as the fault unit normal vector of the first point, the target eigenvalue is the eigenvalue with the smallest value among the multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

[0166] In one possible implementation, the preprocessing module 802 is used to determine a first attribute value and a second attribute value based on the initial coherent attribute values ​​of each point, wherein the first attribute value is the initial coherent attribute value with the largest value among the initial coherent attribute values ​​of each point, and the second attribute value is the initial coherent attribute value with the smallest value among the initial coherent attribute values ​​of each point; and determine the target coherent attribute value of each point based on the first attribute value, the second attribute value and the initial coherent attribute value of each point.

[0167] The above device determines the Gaussian-Laplace filter operator of each point according to the position information of each point; according to the Gaussian-Laplace filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the fault enhancement value of each point. Since the Gaussian-Laplace filter operator is a merging operator, it can not only smooth the noise data in the seismic data, but also sharpen the fault information in the seismic data. Smoothing and sharpening are performed simultaneously, so that the fault enhancement value of each point obtained is more accurate, and the fault information obtained according to the fault enhancement value of each point is clearer, thereby improving the detection and identification of faults in the seismic data and improving the accuracy of the interpretation of the seismic data.

[0168] It should be understood that the above-mentioned device only uses the division of the above-mentioned functional modules as an example to illustrate when implementing its functions. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0169] Fig. 9The structure block diagram of a terminal device 900 provided by an exemplary embodiment of the present application is shown. The terminal device 900 may be a portable mobile terminal, such as a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer or a desktop computer. The terminal device 900 may also be called a user device, a portable terminal, a laptop terminal, a desktop terminal or other names.

[0170] Typically, the terminal device 900 includes: a processor 901 and a memory 902 .

[0171] The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 901 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 901 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 901 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 901 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0172] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one instruction, which is used to be executed by the processor 901 to implement the method for processing seismic data provided in the method embodiment of the present application.

[0173] In some embodiments, the terminal device 900 may further optionally include: a peripheral device interface 903 and at least one peripheral device. The processor 901, the memory 902 and the peripheral device interface 903 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 903 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 904, a display screen 905, a camera assembly 906, an audio circuit 907 and a power supply 909.

[0174] The peripheral device interface 903 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 901 and the memory 902. In some embodiments, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0175] The radio frequency circuit 904 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 904 communicates with communication networks and other communication devices through electromagnetic signals. The radio frequency circuit 904 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 904 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 904 can communicate with other terminal devices through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 904 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0176] The display screen 905 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 905 is a touch display screen, the display screen 905 also has the ability to collect touch signals on the surface or above the surface of the display screen 905. The touch signal can be input to the processor 901 as a control signal for processing. At this time, the display screen 905 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 905 can be one, set on the front panel of the terminal device 900; in other embodiments, the display screen 905 can be at least two, respectively set on different surfaces of the terminal device 900 or in a folding design; in other embodiments, the display screen 905 can be a flexible display screen, set on the curved surface or folding surface of the terminal device 900. Even, the display screen 905 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 905 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0177] The camera assembly 906 is used to capture images or videos. Optionally, the camera assembly 906 includes a front camera and a rear camera. Typically, the front camera is arranged on the front panel of the terminal device 900, and the rear camera is arranged on the back of the terminal device 900. 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 the panoramic shooting and VR (Virtual Reality) shooting function or other fusion shooting functions. In some embodiments, the camera assembly 906 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.

[0178] The audio circuit 907 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 901 for processing, or input them into the radio frequency circuit 904 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal device 900. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 907 may also include a headphone jack.

[0179] The power supply 909 is used to power various components in the terminal device 900. The power supply 909 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 909 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0180] In some embodiments, the terminal device 900 further includes one or more sensors 910 . The one or more sensors 910 include but are not limited to: an acceleration sensor 911 , a gyroscope sensor 912 , a pressure sensor 913 , an optical sensor 915 , and a proximity sensor 916 .

[0181] The acceleration sensor 911 can detect the magnitude of acceleration on the three coordinate axes of the coordinate system established by the terminal device 900. For example, the acceleration sensor 911 can be used to detect the components of gravity acceleration on the three coordinate axes. The processor 901 can control the display screen 905 to display the user interface in a horizontal view or a vertical view according to the gravity acceleration signal collected by the acceleration sensor 911. The acceleration sensor 911 can also be used for collecting game or user motion data.

[0182] The gyroscope sensor 912 can detect the body direction and rotation angle of the terminal device 900. The gyroscope sensor 912 can cooperate with the acceleration sensor 911 to collect the user's 3D actions on the terminal device 900. The processor 901 can implement the following functions based on the data collected by the gyroscope sensor 912: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control, and inertial navigation.

[0183] The pressure sensor 913 can be set on the side frame of the terminal device 900 and / or the lower layer of the display screen 905. When the pressure sensor 913 is set on the side frame of the terminal device 900, it can detect the user's holding signal of the terminal device 900, and the processor 901 performs left and right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 913. When the pressure sensor 913 is set on the lower layer of the display screen 905, the processor 901 controls the operability controls on the UI interface according to the user's pressure operation on the display screen 905. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0184] The optical sensor 915 is used to collect the ambient light intensity. In one embodiment, the processor 901 can control the display brightness of the display screen 905 according to the ambient light intensity collected by the optical sensor 915. Specifically, when the ambient light intensity is high, the display brightness of the display screen 905 is increased; when the ambient light intensity is low, the display brightness of the display screen 905 is reduced. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera component 906 according to the ambient light intensity collected by the optical sensor 915.

[0185] The proximity sensor 916, also called a distance sensor, is usually arranged on the front panel of the terminal device 900. The proximity sensor 916 is used to collect the distance between the user and the front of the terminal device 900. In one embodiment, when the proximity sensor 916 detects that the distance between the user and the front of the terminal device 900 is gradually decreasing, the processor 901 controls the display screen 905 to switch from the screen-on state to the screen-off state; when the proximity sensor 916 detects that the distance between the user and the front of the terminal device 900 is gradually increasing, the processor 901 controls the display screen 905 to switch from the screen-off state to the screen-on state.

[0186] Those skilled in the art will understand that Fig. 9 The structure shown in the figure does not constitute a limitation on the terminal device 900, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0187] Fig.10The schematic diagram of the structure of the server provided in the embodiment of the present application, the server 1000 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 1001 and one or more memories 1002, wherein the one or more memories 1002 store at least one program code, and the at least one program code is loaded and executed by the one or more processors 1001 to implement the processing method of seismic data provided in the above-mentioned various method embodiments. Of course, the server 1000 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the server 1000 may also include other components for realizing device functions, which will not be described in detail here.

[0188] In an exemplary embodiment, a computer-readable storage medium is also provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to enable a computer to implement any of the above-mentioned methods for processing seismic data.

[0189] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0190] In an exemplary embodiment, a computer program or a computer program product is also provided, wherein at least one computer instruction is stored in the computer program or the computer program product, and the at least one computer instruction is loaded and executed by a processor so that the computer implements any of the above-mentioned methods for processing seismic data.

[0191] 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, stored data, displayed data, 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 relevant laws, regulations and standards of relevant countries and regions. For example, the seismic data involved in this application were obtained with full authorization.

[0192] It should be understood that the "plurality" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0193] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0194] The above description is only an exemplary embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for processing seismic data, characterized in that: The method comprises: According to the seismic data of the area to be explored, regional data of the area to be explored are obtained, wherein the regional data includes the position information of each point included in the area to be explored and the initial coherent attribute value of each point, and the initial coherent attribute value of any point is used to indicate the correlation between the any point and its adjacent points; Preprocessing the initial coherent attribute values ​​of the respective points to obtain target coherent attribute values ​​of the respective points; Determine the Gaussian-Laplacian filter operator of each point according to the position information of each point, and the Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherent attribute value of any point; According to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the fault enhancement value of each point, and the fault enhancement value of each point is used to indicate the fault information of the area to be explored.

2. The method according to claim 1, characterized in that The regional data also includes the longitudinal survey line direction inclination angle of each point and the transverse survey line direction inclination angle of each point, the longitudinal survey line direction inclination angle of any point is used to indicate the longitudinal angle between any point and the observation route for collecting the seismic data, and the transverse survey line direction inclination angle of any point is used to indicate the transverse angle between any point and the observation route; The step of performing smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point includes: According to the Gaussian-Laplacian filter operator of each point, the target coherent attribute value of each point is smoothed and sharpened to obtain the intermediate enhancement value of each point; The fault enhancement value of each point is determined according to the intermediate enhancement value of each point, the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point.

3. The method according to claim 2, characterized in that The step of performing smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the intermediate enhancement value of each point includes: For a first point among the points, determining a first area according to position information of the first point, wherein the first point is any point among the points; According to the Gaussian-Laplacian filter operators respectively corresponding to the plurality of second points included in the first area, smoothing and sharpening processing is performed on the target coherent attribute values ​​respectively corresponding to the plurality of second points to obtain smoothing and sharpening results of the respective second points; Determining an intermediate enhancement value of the first point according to the smoothing and sharpening results of each of the second points; Determining the fault enhancement value of each point according to the intermediate enhancement value of each point, the longitudinal survey line direction inclination angle of each point and the transverse survey line direction inclination angle of each point comprises: Determine a unit normal vector of a reflection layer at the first point according to an inclination angle of a longitudinal survey line direction of the first point and an inclination angle of a transverse survey line direction of the first point; Determine a fault unit normal vector of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points; The fault enhancement value of the first point is determined according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point.

4. The method according to claim 3, characterized in that The determining the fault enhancement value of the first point according to the reflection layer unit normal vector of the first point, the fault unit normal vector of the first point and the intermediate enhancement value of the first point comprises: determining an angle between the reflection layer and the fault at the first point according to a unit normal vector of the reflection layer at the first point and a unit normal vector of the fault at the first point; determining, according to an angle between a reflection layer and a fault at the first point, an edge filtering operator of the first point, wherein the edge filtering operator of the first point is used to perform edge filtering processing on an intermediate enhancement value of the first point; According to the fringing filter operator of the first point, fringing filter processing is performed on the intermediate enhancement value of the first point to obtain the fault enhancement value of the first point.

5. The method according to claim 3, characterized in that: The determining the fault unit normal vector of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points comprises: Determine a second-order moment tensor of the first point according to the position information respectively corresponding to the plurality of second points and the target coherent attribute values ​​respectively corresponding to the plurality of second points, wherein the second-order moment tensor of the first point is used to determine a fault unit normal vector of the first point; determining a plurality of eigenvalues ​​corresponding to the second-order moment tensor of the first point; The eigenvector corresponding to the target eigenvalue is used as the fault unit normal vector of the first point, and the target eigenvalue is the eigenvalue with the smallest value among multiple eigenvalues ​​corresponding to the second-order moment tensor of the first point.

6. The method according to claim 1, characterized in that The preprocessing of the initial coherent attribute values ​​of the respective points to obtain target coherent attribute values ​​of the respective points includes: Determine a first attribute value and a second attribute value according to the initial coherent attribute values ​​of the points, wherein the first attribute value is the initial coherent attribute value with the largest value among the initial coherent attribute values ​​of the points, and the second attribute value is the initial coherent attribute value with the smallest value among the initial coherent attribute values ​​of the points; The target coherent attribute value of each point is determined according to the first attribute value, the second attribute value and the initial coherent attribute value of each point.

7. A seismic data processing device, characterized in that: The device comprises: An acquisition module, used for acquiring regional data of the area to be explored according to seismic data of the area to be explored, wherein the regional data includes position information of each point included in the area to be explored and initial coherent attribute values ​​of each point, and the initial coherent attribute value of any point is used to indicate the correlation between the any point and its adjacent points; A preprocessing module, used for preprocessing the initial coherent attribute value of each point to obtain the target coherent attribute value of each point; A determination module, used to determine the Gaussian-Laplacian filter operator of each point according to the position information of each point, and the Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherent attribute value of any point; A processing module is used to perform smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the fault enhancement value of each point, and the fault enhancement value of each point is used to indicate the fault information of the area to be explored.

8. The device according to claim 7, characterized in that The regional data also includes the longitudinal survey line direction inclination angle of each point and the transverse survey line direction inclination angle of each point, the longitudinal survey line direction inclination angle of any point is used to indicate the longitudinal angle between any point and the observation route for collecting the seismic data, and the transverse survey line direction inclination angle of any point is used to indicate the transverse angle between any point and the observation route; The processing module is used to perform smoothing and sharpening processing on the target coherent attribute value of each point according to the Gaussian-Laplacian filter operator of each point to obtain the intermediate enhancement value of each point; and determine the fault enhancement value of each point according to the intermediate enhancement value of each point, the inclination angle of the longitudinal survey line direction of each point and the inclination angle of the transverse survey line direction of each point.

9. A computer device, characterized in that: The computer device includes 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 so that the computer device implements the seismic data processing method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor so that the computer implements the seismic data processing method as described in any one of claims 1 to 6.

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