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

Abstract

The application 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 the following steps: acquiring regional data of a to-be-explored region according to seismic data of the to-be-explored region, wherein the regional data comprises position information of each point included in the to-be-explored region and initial coherent attribute values of the each point; preprocessing the initial coherent attribute values of the each point to obtain target coherent attribute values of the each point; determining a Gaussian-Laplace filter operator of each point according to the position information of the each point; and performing smoothing and sharpening processing on the target coherent attribute values of the each point according to the Gaussian-Laplace filter operator of the each point to obtain fault enhancement values of the each point, wherein the fault enhancement values of the each point are used for indicating fault information of the to-be-explored region. The accuracy of the fault enhancement values of the each point determined by the method is higher.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of seismic exploration, and in particular, relate to a seismic data processing method, device, equipment and computer readable storage medium. BACKGROUND

[0002] Seismic exploration generally includes three stages of seismic data acquisition, seismic data processing and seismic data interpretation. Among them, 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, the imaging quality is poor, and then the fault information obtained by processing the collected seismic data in the seismic data processing stage is relatively fuzzy, which affects the detection and identification of faults, and further affects the accuracy of seismic data interpretation.

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

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

[0005] In one aspect, the present application provides a seismic data processing method, the method comprising:

[0006] According to the seismic data of the to-be-explored area, the regional data of the to-be-explored area is obtained, the regional data comprising position information of each point included in the to-be-explored area and initial coherence attribute values of the each point, the initial coherence attribute value of any point being used to indicate the correlation of the any point with adjacent points of the any point;

[0007] The initial coherence attribute values of the each point are preprocessed to obtain target coherence attribute values of the each point;

[0008] According to the position information of the each point, a Gaussian-Laplacian filter operator of the each point is determined, the Gaussian-Laplacian filter operator of the any point being used to perform smoothing and sharpening processing on the target coherence attribute value of the any point;

[0009] According to the Gaussian-Laplacian filter operator of the each point, the target coherence attribute values of the each point are subjected to smoothing and sharpening processing to obtain fault enhancement values of the each point, the fault enhancement values of the each point being used to indicate fault information of the to-be-explored area.

[0010] In a possible implementation, the area data further comprises a profile direction dip angle of each of the points and a cross profile direction dip angle of each of the points, the profile direction dip angle of any point being used to indicate an angle between the any point and an observation line in a vertical direction in which the seismic data is collected, and the cross profile direction dip angle of the any point being used to indicate an angle between the any point and the observation line in a horizontal direction;

[0011] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0012] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0013] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0014] In a possible implementation, the smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0015] For a first point in the points, a first area is determined according to position information of the first point, and the first point is any point in the points;

[0016] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0017] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0018] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0019] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0020] The smoothing and sharpening of the target coherent attribute value of each of the points according to the Gaussian-Laplacian filter of each of the points comprises:

[0021] determine the fault enhancement value of the first point according to the reflector 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, the determining the fault enhancement value of the first point according to the reflector 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] determine an included angle between the reflector and the fault of the first point according to the reflector unit normal vector of the first point and the fault unit normal vector of the first point;

[0024] determine a padding filter operator of the first point according to the included angle between the reflector and the fault of the first point, the padding filter operator of the first point being used for padding filter processing on the intermediate enhancement value of the first point;

[0025] perform padding filter processing on the intermediate enhancement value of the first point according to the padding filter operator of the first point to obtain the fault enhancement value of the first point.

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

[0027] determine a second moment tensor of the first point according to the position information corresponding to the plurality of second points respectively and the target coherent attribute values corresponding to the plurality of second points respectively, the second moment tensor of the first point being used for determining the fault unit normal vector of the first point;

[0028] determine a plurality of eigenvalues corresponding to the second moment tensor of the first point;

[0029] determine a feature vector corresponding to a target eigenvalue as the fault unit normal vector of the first point, the target eigenvalue being the eigenvalue with the smallest value in the plurality of eigenvalues corresponding to the second moment tensor of the first point.

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

[0031] determine a first attribute value and a second attribute value according to the initial coherent attribute values of the points, the first attribute value being the initial coherent attribute value with the largest value in the initial coherent attribute values of the points, and the second attribute value being the initial coherent attribute value with the smallest value in the initial coherent attribute values of the points;

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

[0033] In another aspect, an embodiment of the present application provides a device for processing seismic data, the device comprising:

[0034] An acquisition module is configured to acquire regional data of a region to be explored according to seismic data of the region to be explored, the regional data comprising position information of each point comprised in the region to be explored and an initial coherent attribute value of each point, the initial coherent attribute value of any point being used to indicate a correlation between the any point and a neighboring point of the any point;

[0035] A preprocessing module is configured to preprocess the initial coherent attribute value of each point to obtain a target coherent attribute value of each point.

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

[0037] A processing module is configured 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 a fault enhancement value of each point, the fault enhancement value of each point being used to indicate fault information of the region to be explored.

[0038] In a possible implementation, the regional data further comprises a dip angle in a profile direction of each point and a dip angle in a crossline direction of each point, the dip angle in the profile direction of the any point being used to indicate an angle between the any point and an observation route in a vertical direction in which the seismic data is collected, and the dip angle in the crossline direction of the any point being used to indicate an angle between the any point and the observation route in a horizontal direction.

[0039] The processing module is configured 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 an 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 dip angle in the profile direction of each point and the dip angle in the crossline direction of each point.

[0040] In a possible implementation, the processing module is configured to: for a first point in the points, determine a first region according to position information of the first point, the first point being any point in the points; perform smoothing and sharpening processing on target coherent attribute values corresponding to a plurality of second points included in the first region respectively according to Gaussian-Laplace filter operators corresponding to the plurality of second points respectively, to obtain smoothing and sharpening results of the plurality of second points respectively; determine an intermediate enhancement value of the first point according to the smoothing and sharpening results of the plurality of second points; determine a reflection layer unit normal vector of the first point according to a dip angle of a profile direction of the first point and a dip angle of a trace direction of the first point; determine a fault unit normal vector of the first point according to position information corresponding to the plurality of second points and the target coherent attribute values corresponding to the plurality of second points; and determine a 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 configured to: determine an included angle between a reflection layer and a 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; determine a matting filter operator of the first point according to the included angle between the reflection layer and the fault of the first point, the matting filter operator of the first point being used for performing matting filter processing on the intermediate enhancement value of the first point; and perform matting filter processing on the intermediate enhancement value of the first point according to the matting 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 configured to: determine a second moment tensor of the first point according to position information corresponding to the plurality of second points and the target coherent attribute values corresponding to the plurality of second points, the second moment tensor of the first point being used for determining the fault unit normal vector of the first point; determine a plurality of eigenvalues corresponding to the second moment tensor of the first point; and determine the fault unit normal vector of the first point as an eigenvector corresponding to a target eigenvalue, the target eigenvalue being an eigenvalue with the smallest value in the plurality of eigenvalues corresponding to the second moment tensor of the first point.

[0043] In a possible implementation, the preprocessing module is configured to: determine a first attribute value and a second attribute value according to the initial coherent attribute values of the points, the first attribute value being an initial coherent attribute value with the largest value in the initial coherent attribute values of the points, and the second attribute value being an initial coherent attribute value with the smallest value in the initial coherent attribute values of the points; and determine the target coherent attribute values of the points according to the first attribute value, the second attribute value, and the initial coherent attribute values of the points.

[0044] In another aspect, the embodiments of the present application provide a computer device, comprising a processor and a memory, wherein the memory stores at least one program code, the at least one program code is loaded and executed by the processor, so that the computer device implements the processing method of seismic data as described above.

[0045] In another aspect, a computer readable storage medium is also provided, wherein the computer readable storage medium stores at least one program code, the at least one program code is loaded and executed by a processor, so that the computer implements the processing method of seismic data as described above.

[0046] In another aspect, a computer program or computer program product is also provided, wherein the computer program or computer program product stores at least one computer instruction, the at least one computer instruction is loaded and executed by a processor, so that the computer implements the processing method of seismic data as described above.

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

[0048] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects: BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0050] Figure 1 is an implementation environment schematic diagram of the seismic data processing method provided by the embodiments of the present application;

[0051] Figure 2 is a flowchart of the seismic data processing method provided by the embodiments of the present application;

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

[0053] Figure 4 This is a coherent attribute profile of an area to be explored, provided in an embodiment of this application.

[0054] Figure 5 This is a cross-sectional view of the area to be explored along the longitudinal survey line, provided in an embodiment of this application.

[0055] Figure 6 This is a cross-sectional view of the area to be explored along the transverse survey line direction, provided in an embodiment of this application;

[0056] Figure 7 This is a schematic diagram of a fault enhancement property profile of an area to be explored, provided in an embodiment of this application;

[0057] Figure 8 This is a schematic diagram of the structure of a seismic data processing device provided in an embodiment of this application;

[0058] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0059] Figure 10 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0061] Figure 1 This is a schematic diagram illustrating the implementation environment of a seismic data processing method provided in an embodiment of this application, such as... Figure 1 As shown, the implementation environment includes a computer device 101, which can be a terminal device or a server; this embodiment does not limit the specific type of device. The computer device 101 is used to execute the seismic data processing method provided in this embodiment.

[0062] Optionally, computer device 101 is a terminal device. A terminal device can be any electronic device that allows human-computer interaction with a user through one or more methods such as a keyboard, touchpad, remote control, voice interaction, or handwriting device. Examples include PCs (Personal Computers), mobile phones, smartphones, PDAs (Personal Digital Assistants), wearable devices, PPCs (Pocket PCs), tablets, smart car systems, smart TVs, smart speakers, and smartwatches.

[0063] The terminal device can refer to one of a plurality of terminal devices, and the embodiment is only exemplified by the terminal device. Those skilled in the art can know that the number of the terminal device can be more or less. For example, the terminal device can be only one, or the terminal device can be dozens or hundreds, or more, and the number and type of the terminal device are not limited in the embodiment.

[0064] When the computer device 101 is a server, the server can be a single server, or a server cluster composed of a plurality of servers, or any one of a cloud computing platform and a virtualization center, and the embodiment is not limited thereto. The server is in communication connection with 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 can also have other functions, and the embodiment is not limited thereto.

[0065] Those skilled in the art should understand that the terminal device and the server are only examples, and other existing or future terminal devices or servers, such as those applicable to the present application, should also be included in the protection scope of the present application and are hereby incorporated by reference.

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

[0067] In step 201, according to the seismic data of the to-be-explored region, the regional data of the to-be-explored region is obtained.

[0068] The regional data comprises position information of each point included in the to-be-explored region and an initial coherence attribute value of each point. The initial coherence attribute value of any point is used to indicate the correlation of any point with any adjacent point. The initial coherence attribute value of any point is directly proportional to the correlation of any point with any adjacent point, that is, the higher the initial coherence attribute value of any point, the more relevant any point is to any adjacent point; on the contrary, the lower the initial coherence attribute value of any point, the less relevant any point is to any adjacent point.

[0069] In the exemplary embodiment of the present application, the seismic data of the to-be-explored region comprises a seismic profile of the to-be-explored region, and the seismic profile of the to-be-explored region is identified to obtain the regional data of the to-be-explored region. As shown in Figure 3is a schematic diagram of a seismic profile of a region to be explored provided by an embodiment of the present application.

[0070] In a possible implementation, after obtaining the initial coherent attribute values of the points comprised in the region to be explored, a coherent attribute profile of the region to be explored can be obtained according to the initial coherent attribute values of the points. Figure 4 is a coherent attribute profile of a region to be explored provided by an embodiment of the present application.

[0071] In step 202, the initial coherent attribute values of the points are preprocessed to obtain target coherent attribute values of the points.

[0072] Optionally, after obtaining the initial coherent attribute values of the points in step 201, the initial coherent attribute values of the points can be preprocessed to obtain target coherent attribute values of the points, the target coherent attribute values of the points being the coherent attribute values of the initial coherent attribute values of the points after preprocessing.

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

[0074] 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. For example, the region to be explored comprises five points. 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. 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 values of the points according to the first attribute value, the second attribute value and the initial coherent attribute values of the points includes: determining intermediate attribute values of the points according to the first attribute value, the second attribute value and the initial coherent attribute values of the points; and determining the target coherent attribute values of the points according to the intermediate attribute values of the points.

[0076] The process of determining the intermediate attribute values of the points according to the first attribute value, the second attribute value and the initial coherent attribute values of the points includes: for any point of the points, determining a first difference value between the initial coherent attribute value of the point and the second attribute value; determining a second difference value between the first attribute value and the second attribute value; and taking a quotient between the first difference value and the second difference value as the intermediate attribute value of the point.

[0077] Optionally, according to the first attribute value, the second attribute value and the initial coherent attribute value of each point, the intermediate attribute value of each point is determined 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, and 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. According to the above formula (1), the intermediate attribute value of any point is 0.125.

[0081] The intermediate attribute value of each point obtained in the above manner is located 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, a third difference between the intermediate attribute value of the point and the first value is determined; a fourth difference between the second value and the intermediate attribute value of the point is determined; based on the third difference being less than the fourth difference, the fourth difference is taken as the target coherent attribute value of the point; and based on the third difference not being less than the fourth difference, the intermediate attribute value of the point is taken as the target coherent attribute value of the point. The third value is less than the fourth value, and the third value and the fourth value are set according to experience or adjusted based on an implementation environment, which is not limited in the embodiments of the present application. For example, the third value is 0, and the fourth value is 1.

[0083] For example, the intermediate attribute value of any point is 0.125, the third difference between the intermediate attribute value of the point and the first value is 0.125, and the fourth difference between the second value and the intermediate attribute value of the point is 0.875. Since the third difference is less than the fourth difference, the fourth difference is taken as the target coherent attribute value of the point, that is, the target coherent attribute value of the 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] The Gaussian-Laplacian filter operator of any point is used to perform smoothing and sharpening processing on the target coherence attribute value of the any point. The position information of each point refers to the position of each point in a three-dimensional space, and includes position information of each point in a first direction, position information of each point in a second direction, and position information of each point in a third direction. The first direction, the second direction, and the third direction are different. For example, the first direction is a horizontal direction, the second direction is a vertical direction, and the third direction is a vertical direction.

[0086] In a possible implementation, before determining the Gaussian-Laplacian filter operator of each point according to the position information of each point, a Gaussian-Laplacian filter function needs to be obtained. The process of obtaining the Gaussian-Laplacian filter function includes: obtaining a three-dimensional Gaussian kernel function; and performing Laplacian 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 a standard deviation, and (x, y, z) is position information. The standard deviation is a factor that affects the smoothing degree of the three-dimensional Gaussian kernel function.

[0090] In a possible implementation, the Gaussian-Laplacian filter function obtained by performing Laplacian operator operation on the three-dimensional Gaussian kernel function shown in the above formula (2) can be expressed as the following formula (3).

[0091]

[0092] Since the second-order partial derivative 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-Laplacian filter function, the position information of each point is substituted into the Gaussian-Laplacian function to obtain the Gaussian-Laplacian filter operator of each point. The Gaussian-Laplacian filter function has rotational symmetry, and through smoothing and sharpening of the edges of a seismic profile, the effect of edge enhancement is achieved.

[0096] In step 204, the target coherent attribute value of each point is subjected to smoothing and sharpening processing according to a Gaussian-Laplace filter operator of each point, to obtain a fault enhancement value of each point.

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

[0098] In a possible implementation, the process of smoothing and sharpening the target coherent attribute value of each point according to the Gaussian-Laplace filter operator of each point to obtain the fault enhancement value of each point includes: smoothing and sharpening the target coherent attribute value of each point according to the Gaussian-Laplace filter operator of each point to obtain an intermediate enhancement value of each point; and determining the fault enhancement value of each point according to the intermediate enhancement value of each point.

[0099] The process of smoothing and sharpening the target coherent attribute value of each point according to the Gaussian-Laplace filter operator of each point to obtain the intermediate enhancement value of each point includes: for a first point in the points, determining a first region according to position information of the first point, the first point being any one of the points; smoothing and sharpening the target coherent attribute value corresponding to each of a plurality of second points included in the first region according to the Gaussian-Laplace filter operator corresponding to each of the plurality of second points, to obtain a smoothing and sharpening result of each of the plurality of second points; and determining the intermediate enhancement value of the first point according to the smoothing and sharpening result of each of the plurality of second points.

[0100] Optionally, the Gaussian-Laplace filter operator corresponding to each of the plurality of second points is multiplied by the target coherent attribute value corresponding to each of the plurality of second points to obtain the smoothing and sharpening result of each of the plurality of second points. A sum value of the smoothing and sharpening result of each of the plurality of second points is taken as the intermediate enhancement value of the first point.

[0101] The process of determining the first region according to the position information of the first point includes: taking the first point indicated by the position information of the first point as a center, and determining a filter window with a first length as a side length, and taking a region corresponding to the filter window as the first region. The first region 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 an implementation environment, which is not limited in the embodiments of the present application.

[0102] Optionally, the intermediate enhancement value of the first point is determined according to the Gaussian-Laplace filter operator corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points according to the following formula (6).

[0103]

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

[0105] After the fault enhancement values of the points are determined, the process of determining the fault enhancement values of the points according to the intermediate enhancement values of the points includes: taking the intermediate enhancement values of the points as the fault enhancement values of the points.

[0106] In a possible implementation, the region data further includes a dip angle of a profile line direction of each point and a dip angle of a cross-line direction of each point. The dip angle of the profile line direction of any point is used to indicate an angle between the any point and an observation line in a vertical direction in which seismic data is collected, and the dip angle of the cross-line direction of any point is used to indicate an angle between the any point and the observation line in a horizontal direction. A profile graph of the region to be explored in the dip angle of the profile line direction can be obtained according to the dip angle of the profile line direction of each point, and a profile graph of the region to be explored in the dip angle of the cross-line direction can be obtained according to the dip angle of the cross-line direction of each point. For example, Figure 5 A profile graph of the region to be explored in the dip angle of the profile line direction provided by an embodiment of the present application is shown in FIG. 2. Figure 6 A profile graph of the region to be explored in the dip angle of the cross-line direction provided by an embodiment of the present application is shown in FIG. 3.

[0107] The process of determining the fault enhancement values of the points according to the intermediate enhancement values of the points can further include: determining the fault enhancement values of the points according to the intermediate enhancement values of the points, the dip angle of the profile line direction of each point, and the dip angle of the cross-line direction of each point.

[0108] Optionally, the process of determining the fault enhancement values of the points according to the intermediate enhancement values of the points, the dip angle of the profile line direction of each point, and the dip angle of the cross-line direction of each point includes: for a first point of the points, determining a unit normal vector of a reflection layer of the first point according to the dip angle of the profile line direction of the first point and the dip angle of the cross-line direction of the first point; determining a fault unit normal vector of the first point according to the position information of the plurality of second points and the target coherence attribute values of the plurality of second points; and determining the fault enhancement value of the first point according to the unit normal vector of the reflection layer of the first point, the fault unit normal vector of the first point, and the intermediate enhancement value of the first point.

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

[0110] In a possible implementation, the reflection layer unit normal vector of the first point is determined according to the dip angle of the first point in the NGL direction and the dip angle of the first point in the TGL direction, according to the following formula (7).

[0111]

[0112] In the formula (7), N is the reflection layer unit normal vector of the first point, p is the dip angle of the first point in the NGL direction, and q is the dip angle of the first point in the TGL direction.

[0113] In a possible implementation, the process of determining the fault unit normal vector of the first point according to the position information corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points includes: determining a second moment tensor of the first point according to the position information corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points, the second moment tensor of the first point being used to determine the fault unit normal vector of the first point; determining a plurality of eigenvalues corresponding to the second moment tensor of the first point; and taking an eigenvector corresponding to a target eigenvalue as the fault unit normal vector of the first point, the target eigenvalue being the smallest eigenvalue in the plurality of eigenvalues corresponding to the second moment tensor of the first point.

[0114] The second moment tensor of the first point is a 3*3 matrix, and the second moment tensor of the first point includes nine elements, which are a first element C 11 , a second element C 12 , a third element C 13 , a fourth element C 21 , a fifth element C 22 , a sixth element C 23 , a seventh element C 31 , an eighth element C 32 , and a ninth element C 33 .

[0115] The second moment tensor of the first point is determined according to the position information corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points, and each element included in the second moment tensor of the first point is determined; and then the second moment tensor of the first point is determined according to each element included in the second moment tensor of the first point.

[0116] The process of determining each element included in the second moment tensor of the first point according to the position information of each second point and the target coherent attribute value corresponding to each second point comprises: 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 attribute value of each second point. The second element and the fourth element are determined 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 attribute value of each second point. The third element and the seventh element are determined 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 attribute value of each second point. The fifth element is determined according to the distance between each second point and the center point of the first region in the second direction and the target coherent attribute 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 region in the second direction, the distance between each second point and the center point of the first region in the third direction, and the target coherent 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 region in the third direction and the target coherent 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 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 comprises: for any one of the second points, taking the difference between the component in the first direction included in the position information of the any one second point and the component in the first direction included in the position information of the center point of the first region as the distance between the any one second point and the center point of the first region in the first direction; taking the difference between the component in the second direction included in the position information of the any one second point and the component in the second direction included in the position information of the center point of the first region as the distance between the any one second point and the center point of the first region in the second direction; and taking the difference between the component in the third direction included in the position information of the any one second point and the component in the third direction included in the position information of the center point of the first region as the distance between the any one second point and the center point of the first region in the third direction.

[0118] Optionally, according to the position information corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points, each element included in the second moment tensor of the first point is determined according to the following formula (8).

[0119]

[0120] In the above formula (8), C is the second moment tensor of the first point, d ij is any element included in the second moment tensor of the second point, d im is the distance between the mth second point and the center point of the first region in the i direction, d jm is the distance between the mth second point and the center point of the first region in the j 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 region, and i and j take values of 1, 2, and 3.

[0121] Optionally, after the second moment tensor of the first point is determined according to the above process, the second moment tensor of the first point is solved to obtain a plurality of eigenvalues corresponding to the second moment tensor of the first point; and a characteristic vector corresponding to a target eigenvalue is taken as a fault unit normal vector of the first point. The target eigenvalue is the eigenvalue with the smallest value among the plurality of eigenvalues corresponding to the second moment tensor of the first point. The following formula (9) is the second moment tensor of the first point.

[0122]

[0123] In the above formula (9), C is the second 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 is the fifth element, C 23 is the sixth element, C 31 is the seventh element, C 32 is the eighth element, C 33 is the ninth element.

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

[0125] |C-λE|=0 Formula (10)

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

[0127] After obtaining the plurality of eigenvalues corresponding to the second moment tensor of the first point, a target eigenvalue with the smallest value is determined from the plurality of eigenvalues, a eigenvector corresponding to the target eigenvalue is determined, and the eigenvector corresponding to the target eigenvalue is taken as a 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 moment tensor of the first point, λ1 is the target eigenvalue, E is a unit matrix, and formula (11) is solved to obtain the eigenvector X corresponding to the target eigenvalue.

[0131] In a possible implementation, after obtaining the reflector 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 reflector 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 an included angle between the reflector and the fault of the first point according to the reflector unit normal vector of the first point and the fault unit normal vector of the first point; determining a padding filter operator for the first point according to the included angle between the reflector and the fault of the first point, the padding filter operator being used for padding filter processing on the intermediate enhancement value of the first point; and performing padding filter processing on the intermediate enhancement value of the first point according to the padding filter operator of the first point to obtain the fault enhancement value of the first point.

[0132] wherein the process of determining the included angle between the reflector and the fault of the first point according to the reflector unit normal vector of the first point and the fault unit normal vector of the first point includes: determining a first modulus of the reflector unit normal vector of the first point, determining a second modulus of the fault unit normal vector of the first point; determining a first inner product of the reflector unit normal vector of the first point and the fault unit normal vector of the first point; determining a second product of the first modulus and the second modulus; determining a quotient between the first inner product and the second product, and taking an inverse cosine of the quotient between the first inner product and the second product as the included angle between the reflector and the fault of the first point.

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

[0134]

[0135] In the above formula (12), θ is the included angle between the reflector and the fault of the first point, N is the reflector unit normal vector of the first point, V3 is the fault unit normal vector of the first point, cos -1 is the inverse cosine.

[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 formula (12) can be simplified to the following formula (13), and the included angle between the reflection layer and the fault at the first point is determined 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 according to the following formula (13).

[0137] θ = cos -1 (N·V3) Formula (13)

[0138] In a possible implementation, the process of determining the padding filter operator at the first point according to the included angle between the reflection layer and the fault at the first point includes: obtaining a first boundary value and a second boundary value, the first boundary value and the second boundary value being any one of a maximum boundary value and a minimum boundary value of the padding filter respectively, and the first boundary value being different from the second boundary value. The padding filter operator at the first point is determined according to the first boundary value, the second boundary value and the included angle between the reflection layer and the fault at the first point. Exemplarily, the maximum boundary value of the padding filter is 25, and the minimum boundary value is 10, 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 padding filter operator at the first point according to the first boundary value, the second boundary value and the included angle between the reflection layer and the fault at the first point includes the following three cases.

[0140] Case one, based on that the first boundary value is less than the second boundary value, and the included 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 taken as the padding filter operator at the first point; based on that the first boundary value is less than the second boundary value, and the included 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 taken as the padding filter operator at the first point; based on that the first boundary value is less than the second boundary value, and the included angle between the reflection layer and the fault at the first point is between the first boundary value and the second boundary value, the padding filter operator at the first point is determined according to the first boundary value, the second boundary value and the included angle between the reflection layer and the fault at the first point.

[0141] Wherein, 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 embodiments of the present application. Exemplarily, the first reference value is 0, and the second reference value is 1.

[0142] Optionally, based on the first boundary value being less than the second boundary value and the angle between the reflector and the fault at the first point being between the first boundary value and the second boundary value, the first point's matting filter is determined according to the first boundary value, the second boundary value and the angle between the reflector and the fault at the first point, according to the following formula (14).

[0143]

[0144] In the above formula (14), f(θ) is the first point's matting filter, θ is the angle between the reflector and the fault at the first point, θ1 is the first boundary value, and θ2 is the second boundary value.

[0145] Case two, based on the first boundary value being greater than the second boundary value and the angle between the reflector and the fault at the first point being not greater than the second boundary value, the second reference value is taken as the first point's matting filter; based on the first boundary value being greater than the second boundary value and the angle between the reflector and the fault at the first point being not less than the first boundary value, the first reference value is taken as the first point's matting filter; based on the first boundary value being greater than the second boundary value and the angle between the reflector and the fault at the first point being between the first boundary value and the second boundary value, the first point's matting filter is determined according to the first boundary value, the second boundary value and the angle between the reflector and the fault at the first point.

[0146] Optionally, based on the first boundary value being greater than the second boundary value and the angle between the reflector and the fault at the first point being between the first boundary value and the second boundary value, the first point's matting filter is determined according to the first boundary value, the second boundary value and the angle between the reflector and the fault at the first point, according to the following formula (15).

[0147]

[0148] In the above formula (15), f(θ) is the first point's matting filter, θ is the angle between the reflector and the fault at the first point, θ1 is the first boundary value, and θ2 is the second boundary value.

[0149] Case three, based on the first boundary value being equal to the second boundary value, the second reference value is taken as the first point's matting filter.

[0150] In a possible implementation, the process of determining the fault enhancement value of the first point according to the first point's matting filter and the intermediate enhancement value of the first point comprises: taking the product between the first point's matting filter 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 first point's matting filter and the intermediate enhancement value of the first point, according to the following formula (16).

[0152] a f =a log ×f(θ) Formula (16)

[0153] In the above formula (16), a f Let f(θ) be the fault enhancement value at the first point, f(θ) be the fringe filter operator at the first point, and a log This is the intermediate enhancement value for the first point.

[0154] Optionally, after obtaining the fault enhancement values ​​at each point, a fault enhancement attribute profile of the area to be explored can be obtained based on the fault enhancement values ​​at each point, such as... Figure 7 This is a schematic diagram of a fault enhancement property profile of an area to be explored, provided as an embodiment of this application. (Comparison is needed for clarity.) Figure 4 and Figure 7 The schematic diagrams shown before and after fault enhancement reveal that the diagram before fault enhancement treatment (…) Figure 4 Several obvious defects exist in the image after fault enhancement processing, including random background noise, coherent noise caused by stratigraphic shadows, and fault step artifacts. Figure 7 The fault line was significantly altered, and the linear anomaly characteristics indicating the fault were significantly enhanced.

[0155] The above method determines the Gaussian-Laplacian filter operator for each point based on its location information. Then, based on the Gaussian-Laplacian filter operator, the target coherence attribute values ​​at each point are smoothed and sharpened to obtain the fault enhancement values. Since the Gaussian-Laplacian filter operator is a merging operator, it can both smooth noisy data in seismic data and sharpen fault information. This simultaneous smoothing and sharpening makes the obtained fault enhancement values ​​for each point more accurate, and the fault information obtained from these values ​​is clearer. This improves the detection and identification of faults in seismic data and enhances the accuracy of seismic data interpretation.

[0156] Figure 8 The diagram shown is a structural schematic of a seismic data processing device provided in an embodiment of this application. Figure 8 As shown, the device includes:

[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. The regional data includes the location information of each point in the area to be explored and the initial coherence attribute value of each point. The initial coherence attribute value of any point is used to indicate the correlation between any point and any adjacent points.

[0158] The preprocessing module 802 is used to preprocess the initial coherence attribute values ​​of each point to obtain the target coherence attribute values ​​of each point.

[0159] The determining module 803 is configured to determine a Gaussian-Laplacian filter of each point according to the position information of the point, and the Gaussian-Laplacian filter of any point is used for smoothing and sharpening processing of the target coherence attribute value of the point.

[0160] The processing module 804 is configured to perform smoothing and sharpening processing on the target coherence attribute value of each point according to the Gaussian-Laplacian filter of the point, to obtain a 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.

[0161] In a possible implementation, the area data further includes a profile direction dip angle of each point and a cross profile direction dip angle of each point, the profile direction dip angle of any point is used to indicate the angle between the point and an observation route in a vertical direction in which seismic data is collected, and the cross profile direction dip angle of any point is used to indicate the angle between the point and the observation route in a horizontal direction.

[0162] The processing module 804 is configured to perform smoothing and sharpening processing on the target coherence attribute value of each point according to the Gaussian-Laplacian filter of the point, to obtain an 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 profile direction dip angle of each point and the cross profile direction dip angle of each point.

[0163] In a possible implementation, the processing module 804 is configured to, for a first point in the points, determine a first area according to the position information of the first point, the first point being any point in the points, perform smoothing and sharpening processing on the target coherence attribute values corresponding to a plurality of second points included in the first area according to the Gaussian-Laplacian filters corresponding to the second points respectively, to obtain smoothing and sharpening results of the second points respectively, determine the intermediate enhancement value of the first point according to the smoothing and sharpening results of the second points, determine the reflection layer unit normal vector of the first point according to the profile direction dip angle of the first point and the cross profile direction dip angle of the first point, determine the fault unit normal vector of the first point according to the position information corresponding to the second points respectively and the target coherence attribute values corresponding to the second points respectively, and 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 a possible implementation, the processing module 804 is configured to determine 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, determine the padding filter of the first point according to the angle between the reflection layer and the fault of the first point, the padding filter of the first point being used for padding filter processing on the intermediate enhancement value of the first point, and perform padding filter processing on the intermediate enhancement value of the first point according to the padding filter of the first point, to obtain the fault enhancement value of the first point.

[0165] In a possible implementation, the processing module 804 is configured to determine a second moment tensor of the first point according to the position information corresponding to each of the plurality of second points and the target coherent attribute value corresponding to each of the plurality of second points, determine a plurality of eigenvalues corresponding to the second moment tensor of the first point, and determine the fault unit normal vector of the first point as an eigenvector corresponding to a target eigenvalue, the target eigenvalue being the smallest eigenvalue in the plurality of eigenvalues corresponding to the second moment tensor of the first point.

[0166] In a possible implementation, the preprocessing module 802 is configured to determine a first attribute value and a second attribute value according to the initial coherent attribute values of the points, the first attribute value being the initial coherent attribute value with the largest value among the initial coherent attribute values of the points, and the second attribute value being the initial coherent attribute value with the smallest value among the initial coherent attribute values of the points, and determine the target coherent attribute value of each point according to the first attribute value, the second attribute value, and the initial coherent attribute value of each point.

[0167] The device determines the Gaussian-Laplacian filter operator of each point according to the position information of each point, and performs 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. Since the Gaussian-Laplacian filter operator is a merging operator, it can smooth the noise data in the seismic data and sharpen the fault information in the seismic data, and smoothing and sharpening are performed simultaneously, so that the fault enhancement value of each point is more accurate, the fault information obtained according to the fault enhancement value of each point is clearer, and therefore the detection and identification of the fault in the seismic data are improved, and the accuracy of the interpretation of the seismic data is improved.

[0168] It should be understood that the device provided by the above embodiment is only used as an example for the division of the functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, 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 by the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be described here.

[0169] Figure 9A structure block diagram of a terminal device 900 provided by an example embodiment of the present application is shown. The terminal device 900 can be a portable mobile terminal, such as a smartphone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a notebook computer, or a desktop computer. The terminal device 900 can also be referred to as a user device, a portable terminal, a laptop terminal, a desktop terminal, or other names.

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

[0171] The processor 901 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 901 can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also referred to 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 can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing content required to be displayed by a display screen. In some embodiments, the processor 901 can further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

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

[0173] In some embodiments, the terminal device 900 can 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 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 903 through 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 component 906, an audio circuit 907 and a power supply 909.

[0174] The peripheral device interface 903 can 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 can be implemented on a separate chip or circuit board, and the present embodiments are not limited in this regard.

[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 a communication network 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 subscriber 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 can also include NFC (Near Field Communication) related circuitry, which is not limited in the present application.

[0176] The display screen 905 is configured to display a UI (User Interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 905 is a touch display screen, the display screen 905 is further configured to capture touch signals on or above the surface of the display screen 905. The touch signals can be input to the processor 901 as control signals for processing. In this case, the display screen 905 can also be configured to provide virtual buttons and / or virtual keyboard, also known as soft buttons and / or soft keyboard. In some embodiments, the display screen 905 can be one, disposed on the front panel of the terminal device 900; in other embodiments, the display screen 905 can be at least two, respectively disposed 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, disposed on a curved surface or a folding surface of the terminal device 900. Even, the display screen 905 can also be disposed in an irregular shape, i.e., a special-shaped screen. The display screen 905 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0177] The camera assembly 906 is configured to capture images or videos. Optionally, the camera assembly 906 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal device 900, and the rear camera is disposed on the back of the terminal device 900. In some embodiments, the rear camera is at least two, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, to realize the background blur function by fusing the main camera and the depth-of-field camera, the panoramic shooting and VR (Virtual Reality) shooting function by fusing the main camera and the wide-angle camera, or other fusion shooting functions. In some embodiments, the camera assembly 906 can further include a flash. The flash can be a single-color-temperature flash or a dual-color-temperature flash. The dual-color-temperature flash refers to the combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0178] The audio circuit 907 can include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into an electrical signal input to the processor 901 for processing, or input to the radio frequency circuit 904 to realize voice communication. For the purpose of stereo sound collection or noise reduction, the microphone can be multiple, respectively arranged at different parts of the terminal device 900. The microphone can also be an array microphone or an omnidirectional collection type 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 can be a traditional diaphragm speaker, or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only can it convert electrical signals into sound waves that humans can hear, but it can also convert electrical signals into sound waves that humans cannot hear for ranging purposes. In some embodiments, the audio circuit 907 can also include a headphone jack.

[0179] The power supply 909 is used to supply power to each component in the terminal device 900. The power supply 909 can be alternating current, direct current, disposable battery or rechargeable battery. When the power supply 909 includes a rechargeable battery, the rechargeable battery can be a wired charging battery or a wireless charging battery. The wired charging battery is a battery charged through a wired line, and the wireless charging 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 acceleration magnitude in 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 the gravitational acceleration in three coordinate axes. The processor 901 can control the display screen 905 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 911. The acceleration sensor 911 can also be used for game or user motion data collection.

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

[0183] The pressure sensor 913 can be disposed on the side bezel of the terminal device 900 and / or the lower layer of the display screen 905. When the pressure sensor 913 is disposed on the side bezel of the terminal device 900, it can detect the user's grip signal on the terminal device 900, and the processor 901 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 913. When the pressure sensor 913 is disposed on the lower layer of the display screen 905, the processor 901 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 905. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0184] An optical sensor 915 is used to collect ambient light intensity. In one embodiment, the processor 901 can control the display brightness of the display screen 905 based on 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 decreased. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera assembly 906 based on the ambient light intensity collected by the optical sensor 915.

[0185] The proximity sensor 916, also known as a distance sensor, is typically located on the front panel of the terminal device 900. The proximity sensor 916 is used to detect 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 a screen-on state to a 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 a screen-off state to a screen-on state.

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

[0187] Figure 10A structural diagram of a server provided in the embodiments of the present application is shown in FIG. 10. The server 1000 can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) 1001 and one or more memories 1002. The one or more memories 1002 store at least one piece of program code, which is loaded and executed by the one or more processors 1001 to implement the seismic data processing method provided in each of the above method embodiments. Of course, the server 1000 can also have a wired or wireless network interface, a keyboard, an input and output interface, and other components for implementing device functions, and the like, so as to perform input and output. The server 1000 can also include other components for implementing device functions, which are not described herein.

[0188] In exemplary embodiments, a computer readable storage medium is also provided, which stores at least one piece of program code, which is loaded and executed by a processor to enable a computer to implement any of the above seismic data processing methods.

[0189] Optionally, the above computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0190] In exemplary embodiments, a computer program or computer program product is also provided, which stores at least one computer instruction, which is loaded and executed by a processor to enable a computer to implement any of the above seismic data processing methods.

[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 for analysis, stored data, displayed data, etc.), and signals involved in the present application are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data need to comply with relevant laws, regulations, and standards of relevant countries and regions. For example, the seismic data involved in the present application is acquired under full authorization.

[0192] It should be understood that the "multiple" mentioned herein refers to two or more than two. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents that the associated objects before and after it are in an "or" relationship.

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

[0194] The above-mentioned only for the exemplary embodiments of the present application, and does not limit the present application, any modification, equivalent replacement, improvement, etc. made within the principles of the present application, should be included in the protection scope of the present application.

Claims

1. A method for processing seismic data, characterized in that, The method includes: Based on the seismic data of the area to be explored, regional data of the area to be explored is obtained. The regional data includes the location information, initial coherence attribute value, longitudinal line dip angle, and transverse line dip angle of each point in the area to be explored. The initial coherence attribute value of any point is used to indicate the correlation between any point and its neighboring points. The longitudinal line dip angle of any point is used to indicate the longitudinal angle between any point and the observation route for acquiring the seismic data. The transverse line dip angle of any point is used to indicate the transverse angle between any point and the observation route. The initial coherence attribute values ​​of each point are preprocessed to obtain the target coherence attribute values ​​of each point. Based on the location information of each point, the Gaussian-Laplacian filter operator for each point is determined. The Gaussian-Laplacian filter operator for any point is used to smooth and sharpen the target coherence attribute value of any point. For the first point among the points, a first region is determined based on the location information of the first point, where the first point is any one of the points. Based on the Gaussian-Laplace filter operator corresponding to the multiple second points included in the first region, the target coherence attribute values ​​corresponding to the multiple second points are smoothed and sharpened to obtain the smoothed and sharpened results of each second point. Based on the smoothing and sharpening results of each of the second points, determine the intermediate enhancement value of the first point; Based on the dip angle of the longitudinal survey line and the dip angle of the transverse survey line at the first point, determine the unit normal vector of the reflective layer at the first point; Based on the location information corresponding to the plurality of second points and the target coherence attribute values ​​corresponding to the plurality of second points, the fault unit normal vector of the first point is determined; 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, the fault enhancement value of the first point is determined. The fault enhancement values ​​of each point are used to indicate the fault information of the area to be explored.

2. The method according to claim 1, characterized in that, The step of determining the fault enhancement value of the first point based on the unit normal vector of the reflection layer at the first point, the unit normal vector of the fault at the first point, and the intermediate enhancement value at the first point includes: Determine the angle between the reflective layer and the fault at the first point based on the unit normal vector of the reflective layer at the first point and the unit normal vector of the fault at the first point; Based on the angle between the reflection layer and the fault at the first point, the edge filtering operator at the first point is determined. The edge filtering operator at the first point is used to perform edge filtering on the intermediate enhancement value at the first point. Based on the edging filter operator of the first point, the intermediate enhancement value of the first point is subjected to edging filter processing to obtain the fault enhancement value of the first point.

3. The method according to claim 1, characterized in that, The step of determining the fault unit normal vector of the first point based on the location information corresponding to the plurality of second points and the target coherence attribute values ​​corresponding to the plurality of second points includes: Based on the location information corresponding to the plurality of second points and the target coherence attribute values ​​corresponding to the plurality of second points, the second-order moment tensor of the first point is determined, and 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; The eigenvector corresponding to the target eigenvalue is used as the fault unit normal vector of the first point, where 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.

4. The method according to claim 1, characterized in that, The preprocessing of the initial coherence attribute values ​​of each point to obtain the target coherence attribute values ​​of each point includes: Based on the initial coherence attribute values ​​of each point, a first attribute value and a second attribute value are determined. The first attribute value is the initial coherence attribute value with the largest value among the initial coherence attribute values ​​of each point, and the second attribute value is the initial coherence attribute value with the smallest value among the initial coherence attribute values ​​of each point. The target coherence attribute value of each point is determined based on the first attribute value, the second attribute value, and the initial coherence attribute value of each point.

5. A seismic data processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire regional data of the area to be explored based on the seismic data of the area to be explored. The regional data includes the location information, initial coherence attribute value, longitudinal line dip angle and transverse line dip angle of each point included in the area to be explored. The initial coherence attribute value of any point is used to indicate the correlation between any point and its adjacent points. The longitudinal line dip angle of any point is used to indicate the longitudinal angle between any point and the observation route for acquiring the seismic data. The transverse line dip angle of any point is used to indicate the transverse angle between any point and the observation route. The preprocessing module is used to preprocess the initial coherence attribute values ​​of each point to obtain the target coherence attribute values ​​of each point. The determination module is used to determine the Gaussian-Laplacian filter operator for each point based on the position information of each point. The Gaussian-Laplacian filter operator for any point is used to smooth and sharpen the target coherence attribute value of any point. The processing module is configured to: for a first point among the points, determine a first region based on the location information of the first point, where the first point is any one of the points; perform smoothing and sharpening processing on the target coherence attribute values ​​corresponding to the plurality of second points included in the first region using Gaussian-Laplace filter operators respectively, to obtain smoothing and sharpening results for each second point; determine the intermediate enhancement value of the first point based on the smoothing and sharpening results of each second point; determine the reflector unit normal vector of the first point based on the dip angle of the longitudinal survey line and the dip angle of the transverse survey line of the first point; determine the fault unit normal vector of the first point based on the location information and target coherence attribute values ​​corresponding to the plurality of second points respectively; and determine the fault enhancement value of the first point based on the reflector unit normal vector, the fault unit normal vector, and the intermediate enhancement value of the first point, wherein the fault enhancement value of each point is used to indicate the fault information of the area to be explored.

6. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one piece of program code, which is loaded and executed by the processor to enable the computer device to implement the seismic data processing method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to enable the computer to implement the seismic data processing method as described in any one of claims 1 to 4.

Images