CN114839679A - Method, device and equipment for processing crack detection data and storage medium - Google Patents
Method, device and equipment for processing crack detection data and storage medium Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a storage medium for processing crack detection data, and belongs to the field of oil-gas exploration and development. The method comprises the following steps: determining the data of the section along the layer belonging to the target layer in the fracture detection data, wherein the fracture detection data is used for reflecting the distribution of the fractures in the stratum; decomposing the slab data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab data, wherein the low-frequency component is used for reflecting the overall characteristics of the slab data, the high-frequency component is used for reflecting the local characteristics of the slab data in a target dimension, and the target dimension comprises at least one of a seismic channel direction, a survey line direction and an inclined direction between the seismic channel direction and the survey line direction; amplifying the high-frequency component to obtain an amplified high-frequency component; and determining target crack detection data according to the low-frequency component and the amplified high-frequency component. The method and the device can simplify the processing process while improving the accuracy of determining the cracks.
Description
Technical Field
The application relates to the field of oil and gas exploration and development, in particular to a method, a device, equipment and a storage medium for processing crack detection data.
Background
Formation movement can fracture relatively tight hydrocarbon reservoirs. Fractures in hydrocarbon reservoirs help to form corrosion pores, which are the primary sites of enrichment and migration channels for hydrocarbons. Fractured hydrocarbon reservoirs are therefore important hydrocarbon exploration targets. Seismic exploration, an important oil and gas exploration technique, is often used to collect fracture data in the formation.
At present, because the accuracy of crack detection data reflecting crack distribution acquired through seismic exploration is not high, the accuracy of crack determination can be improved only by detecting cracks based on the crack detection data. Generally, crack detection data are respectively detected in a plurality of modes, and then a result which reflects the most accurate distribution characteristics of cracks is selected from the detection results and is determined as a final crack detection result. The multiple modes comprise: coherent body analysis, curvature analysis, edge detection, and the like.
The method can improve the accuracy of determining the crack, but needs to adopt various modes to process the same crack detection data respectively, and the processing process is complicated.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for processing crack detection data, which can improve the accuracy of crack determination and simplify the processing process. The technical scheme is as follows:
according to an aspect of the application, there is provided a method of processing fracture detection data, the method comprising:
determining the bedding slice data belonging to a target layer position in fracture detection data, wherein the fracture detection data are used for reflecting the distribution of fractures in a stratum, the fracture detection data are three-dimensional data, the dimensionality of the three-dimensional data comprises a seismic channel direction, a line measuring direction and a depth direction, the bedding slice data are two-dimensional data, the dimensionality of the two-dimensional data comprises the seismic channel direction and the line measuring direction, and the target layer position can reflect the target depth in the depth direction;
decomposing the slab-wise slice data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab-wise slice data, wherein the low-frequency component is used for reflecting overall characteristics of the slab-wise slice data, and the high-frequency component is used for reflecting local characteristics of the slab-wise slice data in a target dimension, and the target dimension comprises at least one of the seismic channel direction, the survey line direction and an inclined direction between the seismic channel direction and the survey line direction;
amplifying the high-frequency component to obtain an amplified high-frequency component;
and determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
Optionally, the high frequency components include a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the dip direction.
Optionally, the amplifying the high-frequency component to obtain an amplified high-frequency component includes:
amplifying the first component to obtain a first amplified component;
amplifying the second component to obtain a second amplified component;
amplifying the third component to obtain a third amplified component;
the determining target fracture detection data according to the low frequency component and the amplified high frequency component includes:
and determining the target crack detection data according to the low-frequency component, the first amplification component, the second amplification component and the third amplification component.
Optionally, the determining the target fracture detection data according to the low-frequency component, the first amplification component, the second amplification component, and the third amplification component includes:
processing the low-frequency component and the first amplification component based on two-dimensional wavelet inverse transformation to obtain first enhancement data of the along-layer slice data in the line measuring direction;
processing the low-frequency component and the second amplification component based on two-dimensional wavelet inverse transformation to obtain second enhancement data of the sliced data along the layer in the seismic channel direction;
processing the low-frequency component and the third amplification component based on two-dimensional wavelet inverse transformation to obtain third enhancement data of the sliced data along the layer in the inclined direction;
and determining the target crack detection data according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data, wherein the target crack detection data belongs to the inclination direction.
Optionally, before the decomposing the slice data based on the two-dimensional wavelet transform to obtain the low frequency component and the high frequency component of the slice data, the method further includes:
and denoising the layered slice data to obtain denoised layered slice data.
Optionally, the method further comprises:
and displaying a crack detection image according to the target crack detection data.
According to another aspect of the present application, there is provided an apparatus for processing fracture detection data, the apparatus comprising:
the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining the stratal slice data belonging to a target horizon in fracture detection data, the fracture detection data are used for reflecting the distribution of fractures in a stratum, the fracture detection data are three-dimensional data, the dimensionality of the three-dimensional data comprises a seismic channel direction, a line measuring direction and a depth direction, the stratal slice data are two-dimensional data, the dimensionality of the two-dimensional data comprises the seismic channel direction and the line measuring direction, and the target horizon can reflect the target depth of the depth direction;
a decomposition module, configured to decompose the slab-wise slice data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab-wise slice data, where the low-frequency component is used to reflect an overall feature of the slab-wise slice data, and the high-frequency component is used to reflect a local feature of the slab-wise slice data in a target dimension, where the target dimension includes at least one of the seismic trace direction, the survey line direction, and an oblique direction between the seismic trace direction and the survey line direction;
the amplifying module is used for amplifying the high-frequency component to obtain an amplified high-frequency component;
and the second determination module is used for determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
Optionally, the high frequency components include a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the dip direction.
Optionally, the amplifying module is configured to:
amplifying the first component to obtain a first amplified component;
amplifying the second component to obtain a second amplified component;
amplifying the third component to obtain a third amplified component;
the second determining module is configured to:
and determining the target crack detection data according to the low-frequency component, the first amplification component, the second amplification component and the third amplification component.
Optionally, the second determining module is configured to:
processing the low-frequency component and the first amplification component based on two-dimensional wavelet inverse transformation to obtain first enhancement data of the along-layer slice data in the line measuring direction;
processing the low-frequency component and the second amplification component based on two-dimensional wavelet inverse transformation to obtain second enhancement data of the sliced data along the layer in the seismic channel direction;
processing the low-frequency component and the third amplification component based on two-dimensional wavelet inverse transformation to obtain third enhancement data of the sliced data along the layer in the inclined direction;
and determining the target crack detection data according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data, wherein the target crack detection data belongs to the inclination direction.
Optionally, the apparatus further comprises:
and the denoising module is used for denoising the layered slice data to obtain denoised layered slice data.
Optionally, the apparatus further comprises:
and the display module is used for displaying the crack detection image according to the target crack detection data.
According to another aspect of the present application, there is provided a computer device comprising a processor and a memory having stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded and executed by the processor to implement a method of processing crack detection data as described above.
According to another aspect of the present application, there is provided a computer readable storage medium having stored therein at least one program code, which is loaded and executed by a processor, to implement the method of processing crack detection data as described above.
According to another aspect of the application, a computer program product or computer program is provided, comprising computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of a computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the method of processing fracture detection data provided in the various alternative implementations of the above aspects.
The beneficial effect that technical scheme that this application provided brought includes at least:
the low frequency component and the high frequency component of the slice data along the layer can be resolved by the two-dimensional wavelet transform. Wherein the low frequency component can reflect the global characteristics of the slice data, and the high frequency component can reflect the local characteristics of the slice data. And amplifying the high-frequency component, namely amplifying the local characteristics of the sliced data along the layer, so that the sliced data along the layer can reflect the distribution of local cracks more accurately, and the accuracy of determining the cracks is improved. In the process, other modes are not needed to be adopted to reprocess the crack detection data, and the processing process is simplified.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating the principle of crack detection provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for processing fracture detection data according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another method for processing fracture detection data provided by embodiments of the present application;
FIG. 4 is a schematic diagram of an implementation process for determining target fracture detection data provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a crack detection image provided by an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an apparatus for processing crack detection data according to an embodiment of the present disclosure;
FIG. 7 is a schematic structural diagram of another apparatus for processing crack detection data according to an embodiment of the present disclosure;
FIG. 8 is a schematic structural diagram of another apparatus for processing crack detection data according to an embodiment of the present disclosure;
fig. 9 is a schematic structural diagram of a terminal according to an embodiment of the present application.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
First, terms referred to in the embodiments of the present application are described:
crack detection data: the fracture monitoring data is data which is acquired by seismic exploration technology and is used for reflecting the distribution of fractures in the stratum, and can also be called as a fracture detection data body. The fracture detection data in the embodiment of the present application refers to a three-dimensional seismic data volume.
Seismic exploration: seismic exploration refers to a geophysical exploration method for deducing the properties and forms of underground rock strata by observing and analyzing the propagation rule of seismic waves generated by artificial earthquake in the underground by utilizing the difference between the elasticity and the density of underground media caused by artificial excitation.
By way of example, fig. 1 is a schematic diagram of a principle of detecting cracks provided by an embodiment of the present application. As shown in fig. 1, a plurality of rows of detectors 101 are arranged on the ground by the detection personnel in a line, a plurality of detectors 101 are arranged in each row, and the detectors 101 connected together in each row are called a survey line. Seismic waves are then generated in the formation by the source vehicle 102. Signals generated by the propagation of seismic waves in the earth formation are acquired by receivers 101. The instrumentation carriage 103 acquires (and amplifies) the signal acquired by the detector 101. And then analyzing the signals acquired by the instrument trucks 101 through computer equipment, thereby obtaining a crack detection data volume. The direction of the straight line where each row of detectors 101 is located is generally referred to as a line direction, and the direction perpendicular to the line is referred to as a seismic channel direction.
Fig. 2 is a schematic flowchart of a method for processing crack detection data according to an embodiment of the present disclosure. The method may be used in a computer device. As shown in fig. 2, the method includes:
The fracture detection data is used to reflect the distribution of fractures in the formation. The crack detection data is three-dimensional data, and the dimensionality of the three-dimensional data comprises a seismic channel direction, a survey line direction and a depth direction. The slab-wise slice data is two-dimensional data, and the dimensions of the two-dimensional data comprise a seismic trace direction and a survey line direction. The target horizon can reflect a target depth in the depth direction.
The low frequency component is used to reflect global features of the slab-wise slice data, and the high frequency component is used to reflect local features of the slab-wise slice data in the target dimension. The target dimension includes at least one of a seismic trace direction, a line direction, and a dip direction between the seismic trace direction and the line direction.
And step 203, amplifying the high-frequency component to obtain an amplified high-frequency component.
And 204, determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
In summary, the method for processing crack detection data provided in the embodiment of the present application can decompose the low frequency component and the high frequency component of the sliced data along the layer by two-dimensional wavelet transform. Wherein the low frequency component can reflect the global characteristics of the slice data, and the high frequency component can reflect the local characteristics of the slice data. And amplifying the high-frequency component, namely amplifying the local characteristics of the sliced data along the layer, so that the sliced data along the layer can reflect the distribution of local cracks more accurately, and the accuracy of determining the cracks is improved. In the process, other modes are not needed to be adopted to reprocess the crack detection data, and the processing process is simplified.
FIG. 3 is a schematic flow chart of another method for processing fracture detection data according to an embodiment of the present disclosure. The method may be used in a computer device. As shown in fig. 3, the method includes:
and 301, determining the data of the boundary slices belonging to the target layer in the crack detection data.
The fracture detection data is used to reflect the distribution of fractures in the formation. The crack detection data is three-dimensional data, and the dimensionality of the three-dimensional data comprises a seismic channel direction, a survey line direction and a depth direction. Wherein the survey line direction is perpendicular to the seismic channel direction. Illustratively, the fracture detection data is data determined by way of seismic exploration as shown in FIG. 1.
The target horizon can reflect a target depth in the depth direction. The target depth is the depth of the formation for which fracture distribution needs to be determined. The formation may be planar or may be an uneven surface. The slab-wise slice data is two-dimensional data, the dimensions of which include seismic trace directions and line directions.
Optionally, the crack detection data and the target horizon are manually uploaded to a computer device, and the computer device is capable of extracting the edgewise slice data from the crack detection data according to the target horizon.
And 302, denoising the layered slice data to obtain denoised layered slice data.
The computer equipment carries out denoising processing on the layered slice data, noise such as isolated points, burrs and the like in the layered slice data can be eliminated, the noise cannot reflect the distribution of real cracks, and the accuracy of finally determined cracks can be improved by eliminating the noise.
Illustratively, the implementation process of the computer device for denoising the slice along the layer includes the following steps:
in step s1, the regularization processing is performed on the sliced data along the layer to obtain the regularized sliced data along the layer AA 0 (line，trace)。
Since the slice data along the layer is two-dimensional data, which includes information acquired by each detector, referring to the example in fig. 1, the slice data along the layer is two-dimensional data. Wherein the row number of the two-dimensional array is the total measuring line number N _ line, the column number of the two-dimensional data is the total seismic channel number N _ trace, and A is adopted 0 (line, trace) represents the two-dimensional array.
The computer equipment firstly calculates two-dimensional data A 0 (line, trace) average value M satisfying:
then the computer device adopts the average value M to the two-dimensional array A 0 (line, trace) is regularized. I.e. two-dimensional array A 0 The numerical value in the (line, trace) is regularized to be close to the value range of the average value M, so that the abnormal large value and the abnormal small value (noise) can be eliminated, and the processed two-dimensional array AA 0 (line, trace) satisfies:
in step s2, the regularized slice data is divided into subblocks of size w ≧ w, and the average value for each subblock is calculated. And the value in each sub-block is respectively counted by taking the average value of the sub-blocks as a threshold valuePerforming line binarization (taking 1 when the average value is larger than the value, and taking 0 when the average value is smaller than the value) to obtain the binarized edgewise slice data AA 1 (line，trace)。
Wherein the average value AV of each sub-block satisfies:
in step s3, pass through two-dimensional array A 0 (line, trace) and two-dimensional array AA 1 (line, trace) denoising the slab-wise slice data.
For two dimensional array AA 1 (line, trace) of each sample (value) if AA 1 (line, trace) ═ 0, and the value of more than three adjacent points in the adjacent points of the sampling point is 1, then the sampling point is placed in two-dimensional array A 0 The value in (line, trace) is modified to the average value of the sub-block where the sample point is located.
For two dimensional array AA 1 (line, trace) of each sample point, if AA 1 (line, trace) is 1, and the sample point is not an endpoint and satisfies:
then the sampling point is located in the two-dimensional array A 0 The value in (line, trace) is modified to 0. Therefore, denoising processing of the slice data along the layer is achieved.
And 303, decomposing the layered slice data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the layered slice data.
The low frequency component (also referred to as an approximate component) is used for reflecting overall characteristics of the slab data, and the high frequency component is used for reflecting local characteristics of the slab data in a target dimension, wherein the target dimension comprises at least one of a seismic channel direction, a survey line direction and a tilt direction between the seismic channel direction and the survey line direction, and the tilt direction belongs to the same plane with the seismic channel direction and the survey line direction. Optionally, the computer device decomposes the layered slice data into de-noised data.
Optionally, the high frequency component includes a first component belonging to a line direction, a second component belonging to a seismic trace direction, and a third component belonging to a dip direction. That is, the first component is a high-frequency component of the sliced along-layer data in the direction of the survey line, the second component is a high-frequency component of the sliced along-layer data in the direction of the seismic trace, and the third component is a high-frequency component of the sliced along-layer data in the oblique direction. Optionally, the dip direction is a direction intermediate the line direction and the seismic trace direction, e.g. in a plane, the line direction is 0 °, the seismic trace direction is 90 °, and the dip direction is 45 °.
Illustratively, the computer device decomposes the layered slice data based on two-dimensional wavelet transform to obtain a two-dimensional array A corresponding to the layered slice data 0 Low frequency component A of (line, trace) j+1 (line, trace), first component
Second component
And a third component
Satisfies the following conditions:
A j+1 (line，trace)=(low(line)*A j (line，trace)*low(trace)；
wherein, low (line) represents the measuring line direction (two-dimensional array A) 0 (line, trace) line direction) low pass filter, low (trace) representing seismic trace direction (two-dimensional array A) 0 Low pass filters (line, trace) in the column direction), high (line) high pass filters in the line direction, and high (trace) high pass filters in the seismic trace direction. The orientation of the filter indicates that the filter is used with a two-dimensional array A 0 And (line, trace) calculating the numerical value corresponding to the direction. Denotes convolution operation. According to two-dimensional array A 0 And (line, trace) performing high-pass filtering or low-pass filtering decomposition in the row direction and the column direction, increasing j by 1, wherein j is more than or equal to 0. The two-dimensional wavelet transform has a scale of 2 j 。
And step 304, amplifying the high-frequency component to obtain an amplified high-frequency component.
Alternatively, the computer device performs an amplification process of the high frequency component by multiplying the objective function by the high frequency component. The value of the objective function is equal to or greater than one. The amplified high frequency component can enhance the accuracy of the reflected local crack distribution compared to the high frequency component.
Optionally, the computer device performs an amplification process on the first component to obtain a first amplified component. And amplifying the second component to obtain a second amplified component. And amplifying the third component to obtain a third amplified component. The computer device obtains the first amplified component, the second amplified component, and the third amplified component by multiplying the first component, the second classification, and the third classification, respectively, by an objective function.
Illustratively, the objective function is a (2) j ) Scale 2 for two-dimensional wavelet transform j And a (2) is j ) Not less than 1. The first amplification component is
The second amplification component is
The third amplification component is
And 305, determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
The computer device can obtain the target crack detection data by performing a two-dimensional inverse wavelet transform on the low frequency component and the amplified high frequency component. The high-frequency component of the crack detection data is amplified, so that the accuracy of the distribution of the local cracks reflected by the target crack detection data is improved.
Optionally, the computer device determines the target fracture detection data according to the low frequency component, the first amplified component, the second amplified component, and the third amplified component. As shown in fig. 4, the implementation process of step 305 includes the following steps 3051 and 3052:
in step 3051, a two-dimensional inverse wavelet transform is performed on the low frequency component and the amplified high frequency component to obtain first enhancement data, second enhancement data, and third enhancement data.
Optionally, the computer device processes the low frequency component and the first amplified component based on an inverse two-dimensional wavelet transform, resulting in first enhancement data along the layer slice data in a line-measuring direction. And processing the low-frequency component and the second amplification component based on the two-dimensional wavelet inverse transformation to obtain second enhancement data of the sliced data along the layer in the seismic channel direction. And processing the low-frequency component and the third amplification component based on the two-dimensional wavelet inverse transformation to obtain third enhancement data of the sliced data along the layer in the oblique direction.
In step 3052, target fracture detection data is determined according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data.
Fractures are generally distributed in an inclined direction in the formation. The determined root mean square of the first enhancement data and the second enhancement data can reflect the characteristics of the first enhancement data and the second enhancement data in the inclined direction, so that the distribution characteristics of the cracks can be reflected. And determining target crack detection data according to the root-mean-square, so that the accuracy of crack determination can be further improved. The third enhancement data belongs to a tilt direction.
Optionally, the computer device determines an average of the root mean square and the third enhancement data as the target fracture detection data. The computer device can also determine a weighted average of the root mean square and the third enhancement data as the target fracture detection data. The weights in determining the weighted average may be determined manually and empirically. The target crack detection data belongs to the oblique direction.
Illustratively, the root mean square YY of the first enhancement data and the second enhancement data 1 Satisfies the following conditions:
wherein, YY k As first enhancement data, YY v Is the second enhancement data.
And step 306, displaying a crack detection image according to the target crack detection data.
Compared with the crack detection data before processing, the target crack detection data can improve the accuracy of the distribution of the reflected local cracks. When the crack detection image is displayed, the effect of enhancing the image boundary and improving the continuity of the lines of the displayed crack can be achieved.
Exemplarily, fig. 5 is a schematic diagram of a crack detection image provided in an embodiment of the present application. As shown in fig. 5, a first crack detection image 501 is an image displayed based on original crack detection data, and a second crack detection image 502 is an image displayed based on processed target crack detection data. Compared with the first crack detection image 501, the second crack detection image 502 has significantly enhanced image boundaries, more display details of local cracks, and improved continuity of lines of the displayed cracks.
It should be noted that the method may be performed by a computer device, where the computer device includes a server, a server cluster, a virtual server, and the like, and the computer device may also be a mobile phone, a desktop computer, a notebook computer, a tablet, and the like. The method can also be performed by a computer device by installing a client for implementing the method.
In summary, the method for processing crack detection data provided in the embodiment of the present application can decompose the low frequency component and the high frequency component of the sliced data along the layer by two-dimensional wavelet transform. Wherein the low frequency component can reflect the global characteristics of the slice data, and the high frequency component can reflect the local characteristics of the slice data. And amplifying the high-frequency component, namely amplifying the local characteristics of the sliced data along the layer, so that the sliced data along the layer can reflect the distribution of local cracks more accurately, and the accuracy of determining the cracks is improved. In the process, other modes are not needed to be adopted to reprocess the crack detection data, and the processing process is simplified.
In addition, the accuracy of crack determination can be further improved by carrying out denoising processing on the sliced data along the layer. The target crack detection data are determined according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data, the information of the first enhancement data and the second enhancement data in the distribution direction of the crack can be used, and the accuracy of determining the crack can be further improved. Displaying the crack detection image provides a way to visually demonstrate the distribution of cracks.
It should be noted that, the order of the steps of the method provided in the embodiments of the present application may be appropriately adjusted, and the steps may also be increased or decreased according to the circumstances, and any method that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application shall be covered by the protection scope of the present application, and therefore, the detailed description thereof is omitted.
Fig. 6 is a schematic structural diagram of an apparatus for processing crack detection data according to an embodiment of the present disclosure. The apparatus may be for a computer device. As shown in fig. 6, the apparatus 60 includes:
the first determining module 601 is configured to determine the bedding slice data belonging to a target horizon in the fracture detection data, where the fracture detection data is used to reflect the distribution of fractures in a formation, the fracture detection data is three-dimensional data, the dimensionality of the three-dimensional data includes a seismic channel direction, a survey line direction and a depth direction, the bedding slice data is two-dimensional data, the dimensionality of the two-dimensional data includes the seismic channel direction and the survey line direction, and the target horizon can reflect a target depth in the depth direction.
The decomposition module 602 is configured to decompose the slab-wise data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab-wise data, where the low-frequency component is used to reflect an overall characteristic of the slab-wise data, and the high-frequency component is used to reflect a local characteristic of the slab-wise data in a target dimension, where the target dimension includes at least one of a seismic trace direction, a survey line direction, and an oblique direction between the seismic trace direction and the survey line direction.
And an amplifying module 603, configured to amplify the high-frequency component to obtain an amplified high-frequency component.
A second determining module 604 for determining target fracture detection data according to the low frequency component and the amplified high frequency component.
Optionally, the high frequency components include a first component belonging to a line direction, a second component belonging to a seismic trace direction, and a third component belonging to a dip direction.
Optionally, the amplifying module 603 is configured to:
and amplifying the first component to obtain a first amplified component. And amplifying the second component to obtain a second amplified component. And amplifying the third component to obtain a third amplified component.
A second determining module 604 for:
and determining target crack detection data according to the low-frequency component, the first amplification component, the second amplification component and the third amplification component.
Optionally, the second determining module 604 is configured to:
and processing the low-frequency component and the first amplification component based on the two-dimensional wavelet inverse transformation to obtain first enhancement data of the in-line slice data. And processing the low-frequency component and the second amplification component based on the two-dimensional wavelet inverse transformation to obtain second enhancement data of the sliced data along the layer in the seismic channel direction. And processing the low-frequency component and the third amplification component based on the two-dimensional wavelet inverse transformation to obtain third enhancement data of the sliced data along the layer in the oblique direction. And determining target crack detection data according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data, wherein the target crack detection data belongs to the inclination direction.
Optionally, as shown in fig. 7, the apparatus 60 further comprises:
and the denoising module 605 is configured to perform denoising processing on the slab-following slice data to obtain denoised slab-following slice data.
Optionally, as shown in fig. 8, the apparatus 60 further comprises:
and the display module is used for displaying the crack detection image according to the target crack detection data.
It should be noted that: the apparatus for processing crack detection data provided in the above embodiment is only illustrated by dividing each functional module, and in practical applications, the function distribution may 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 apparatus for processing crack detection data and the method for processing crack detection data provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments, and are not described herein again.
Embodiments of the present application further provide a computer device, including: the system includes a processor and a memory, the memory having at least one instruction, at least one program, a set of codes, or a set of instructions stored therein, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the method of processing fracture detection data provided by the method embodiments described above.
Optionally, the computer device is a terminal. Illustratively, fig. 9 is a schematic structural diagram of a terminal provided in an embodiment of the present application.
In general, terminal 900 includes: a processor 901 and a memory 902.
In some embodiments, terminal 900 can also optionally include: a peripheral interface 903 and at least one peripheral. The processor 901, memory 902, and peripheral interface 903 may be connected by buses or signal lines. Various peripheral devices may be connected to the peripheral interface 903 via a bus, signal line, or 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, a positioning assembly 908, and a power supply 909.
The peripheral interface 903 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 901 and the memory 902. In some embodiments, the processor 901, memory 902, and peripheral 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 interface 903 may be implemented on a separate chip or circuit board, which is not limited in this application.
The Radio Frequency circuit 904 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 904 communicates with communication networks and other communication devices via electromagnetic signals. The radio frequency circuit 904 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 904 comprises: 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 so forth. The radio frequency circuit 904 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, generations of mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 904 may also include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 905 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 905 is a touch display screen, the display screen 905 also has the ability to capture touch signals on or over the surface of the display screen 905. The touch signal may be input to the processor 901 as a control signal for processing. At this point, the display 905 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 905 may be one, providing the front panel of the terminal 900; in other embodiments, the number of the display panels 905 may be at least two, and each of the display panels is disposed on a different surface of the terminal 900 or is in a foldable design; in still other embodiments, the display 905 may be a flexible display disposed on a curved surface or a folded surface of the terminal 900. Even more, the display screen 905 may be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display panel 905 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), and other materials.
The camera assembly 906 is used to capture images or video. Optionally, camera assembly 906 includes a front camera and a rear camera. Typically, the front camera is disposed on the front panel of the terminal 900 and the rear camera is disposed on the rear side of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 906 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The positioning component 908 is used to locate the current geographic Location of the terminal 900 for navigation or LBS (Location Based Service). The Positioning component 908 may be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.
In some embodiments, terminal 900 can also include one or more sensors 910. The one or more sensors 910 include, but are not limited to: acceleration sensor 911, gyro sensor 912, pressure sensor 913, fingerprint sensor 914, optical sensor 915, and proximity sensor 916.
The acceleration sensor 911 can detect the magnitude of acceleration in three coordinate axes of the coordinate system established with the terminal 900. For example, the acceleration sensor 911 may be used to detect the components of the gravitational acceleration in three coordinate axes. The processor 901 can control the touch display 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 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 912 may detect a body direction and a rotation angle of the terminal 900, and the gyro sensor 912 may cooperate with the acceleration sensor 911 to acquire a 3D motion of the user on the terminal 900. The processor 901 can implement the following functions according to the data collected by the gyro sensor 912: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
Pressure sensors 913 may be disposed on the side bezel of terminal 900 and/or underneath touch display 905. When the pressure sensor 913 is disposed on the side frame of the terminal 900, the user's holding signal of the terminal 900 may be detected, and the processor 901 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 913. When the pressure sensor 913 is disposed at a lower layer of the touch display 905, the processor 901 controls the operability control on the UI interface according to the pressure operation of the user on the touch display 905. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 914 is used for collecting a fingerprint of the user, and the processor 901 identifies the user according to the fingerprint collected by the fingerprint sensor 914, or the fingerprint sensor 914 identifies the user according to the collected fingerprint. Upon identifying that the user's identity is a trusted identity, processor 901 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 914 may be disposed on the front, back, or side of the terminal 900. When a physical key or vendor Logo is provided on the terminal 900, the fingerprint sensor 914 may be integrated with the physical key or vendor Logo.
The optical sensor 915 is used to collect ambient light intensity. In one embodiment, the processor 901 may control the display brightness of the touch 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 touch display screen 905 is increased; when the ambient light intensity is low, the display brightness of the touch display screen 905 is turned down. In another embodiment, the processor 901 can also dynamically adjust the shooting parameters of the camera assembly 906 according to the ambient light intensity collected by the optical sensor 915.
Proximity sensor 916, also known as a distance sensor, is typically disposed on the front panel of terminal 900. The proximity sensor 916 is used to collect the distance between the user and the front face of the terminal 900. In one embodiment, when the proximity sensor 916 detects that the distance between the user and the front face of the terminal 900 gradually decreases, the processor 901 controls the touch display 905 to switch from the bright screen state to the dark screen state; when the proximity sensor 916 detects that the distance between the user and the front surface of the terminal 900 gradually becomes larger, the processor 901 controls the touch display 905 to switch from the breath screen state to the bright screen state.
Those skilled in the art will appreciate that the configuration shown in fig. 9 does not constitute a limitation of terminal 900, and may include more or fewer components than those shown, or may combine certain components, or may employ a different arrangement of components.
The embodiment of the present application further provides a computer-readable storage medium, where at least one program code is stored, and when the program code is loaded and executed by a processor of a computer device, the method for processing crack detection data provided by the above method embodiments is implemented.
The present application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read by a processor of the computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to execute the method for processing crack detection data provided by the method embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer readable storage medium, and the above readable storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only an example of the present application and should not be taken as limiting, and any modifications, equivalent switches, improvements, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method of processing fracture detection data, the method comprising:
determining the bedding slice data belonging to a target layer position in fracture detection data, wherein the fracture detection data are used for reflecting the distribution of fractures in a stratum, the fracture detection data are three-dimensional data, the dimensionality of the three-dimensional data comprises a seismic channel direction, a line measuring direction and a depth direction, the bedding slice data are two-dimensional data, the dimensionality of the two-dimensional data comprises the seismic channel direction and the line measuring direction, and the target layer position can reflect the target depth in the depth direction;
decomposing the slab-wise slice data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab-wise slice data, wherein the low-frequency component is used for reflecting overall characteristics of the slab-wise slice data, and the high-frequency component is used for reflecting local characteristics of the slab-wise slice data in a target dimension, and the target dimension comprises at least one of the seismic channel direction, the survey line direction and an inclined direction between the seismic channel direction and the survey line direction;
amplifying the high-frequency component to obtain an amplified high-frequency component;
and determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
2. The method of claim 1, wherein the high frequency components include a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the dip direction.
3. The method according to claim 2, wherein the amplifying the high frequency component to obtain an amplified high frequency component comprises:
amplifying the first component to obtain a first amplified component;
amplifying the second component to obtain a second amplified component;
amplifying the third component to obtain a third amplified component;
the determining target fracture detection data according to the low frequency component and the amplified high frequency component includes:
and determining the target crack detection data according to the low-frequency component, the first amplification component, the second amplification component and the third amplification component.
4. The method of claim 3, wherein determining the target fracture detection data from the low frequency component, the first amplified component, the second amplified component, and the third amplified component comprises:
processing the low-frequency component and the first amplification component based on two-dimensional wavelet inverse transformation to obtain first enhancement data of the along-layer slice data in the line measuring direction;
processing the low-frequency component and the second amplification component based on two-dimensional wavelet inverse transformation to obtain second enhancement data of the sliced data along the layer in the seismic channel direction;
processing the low-frequency component and the third amplification component based on two-dimensional wavelet inverse transformation to obtain third enhancement data of the sliced data along the layer in the inclined direction;
and determining the target crack detection data according to the root mean square of the first enhancement data and the second enhancement data and the third enhancement data, wherein the target crack detection data belongs to the inclination direction.
5. The method of any one of claims 1 to 4, wherein before decomposing the slice data based on the two-dimensional wavelet transform to obtain low frequency components and high frequency components of the slice data, the method further comprises:
and denoising the layered slice data to obtain denoised layered slice data.
6. The method of any of claims 1 to 4, further comprising:
and displaying a crack detection image according to the target crack detection data.
7. An apparatus for processing fracture detection data, the apparatus comprising:
the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for determining the stratal slice data belonging to a target horizon in fracture detection data, the fracture detection data are used for reflecting the distribution of fractures in a stratum, the fracture detection data are three-dimensional data, the dimensionality of the three-dimensional data comprises a seismic channel direction, a line measuring direction and a depth direction, the stratal slice data are two-dimensional data, the dimensionality of the two-dimensional data comprises the seismic channel direction and the line measuring direction, and the target horizon can reflect the target depth of the depth direction;
a decomposition module, configured to decompose the slab-wise slice data based on two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slab-wise slice data, where the low-frequency component is used to reflect an overall feature of the slab-wise slice data, and the high-frequency component is used to reflect a local feature of the slab-wise slice data in a target dimension, where the target dimension includes at least one of the seismic trace direction, the survey line direction, and an oblique direction between the seismic trace direction and the survey line direction;
the amplifying module is used for amplifying the high-frequency component to obtain an amplified high-frequency component;
and the second determination module is used for determining target crack detection data according to the low-frequency component and the amplified high-frequency component.
8. The apparatus of claim 7, wherein the high frequency components include a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the dip direction.
9. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement a method of processing fracture detection data according to any of claims 1 to 6.
10. A computer-readable storage medium having stored therein at least one program code, the program code being loaded into and executed by a processor to implement a method of processing fracture detection data according to any of claims 1 to 6.
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