CN114839679B - Method, device, equipment and storage medium for processing crack detection data - Google Patents
Method, device, equipment and storage medium for processing crack detection data 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 and gas exploration and development. The method comprises the following steps: determining the slice data of the stratums belonging to the target horizon in the fracture detection data, wherein the fracture detection data is used for reflecting the distribution of the fractures in the stratum; decomposing the slice data on the ground based on two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the slice data, wherein the low-frequency component is used for reflecting the integral characteristics of the slice data on the ground, and the high-frequency component is used for reflecting the local characteristics of the slice data on the target dimension, and the target dimension comprises at least one of a seismic channel direction, a line direction and an inclined direction between the seismic channel direction and the line direction; amplifying the high-frequency component to obtain an amplified high-frequency component; target crack detection data is determined from the low frequency component and the amplified high frequency component. The application can simplify the processing process while improving the accuracy of crack determination.
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
The formation movement can fracture a relatively tight hydrocarbon reservoir. Fractures in hydrocarbon reservoirs help to form eroding pores, which are the primary enrichment sites and migration channels for hydrocarbons. Thus, fractured reservoirs are important hydrocarbon exploration targets. Seismic exploration is often used as an important oil and gas exploration technique to collect fracture data in formations.
At present, because the accuracy of the crack detection data which reflects the crack distribution and is acquired through seismic exploration is not high, the accuracy of determining the crack can be improved only by detecting the crack based on the crack detection data. The crack detection data is usually detected in various ways, and then the most accurate result reflecting the distribution characteristics of the crack is selected from the detected results, and is determined as the final crack detection result. The plurality of modes include: coherence analysis, curvature analysis, edge detection, and the like.
The accuracy of crack determination can be improved by the method, but the same crack detection data needs to be processed in multiple modes 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 present application, there is provided a method of processing crack detection data, the method comprising:
Determining slice data along a layer belonging to a target horizon in crack detection data, wherein the crack detection data are used for reflecting the distribution of cracks in a stratum, the crack detection data are three-dimensional data, the dimension of the three-dimensional data comprises a seismic trace direction, a survey line direction and a depth direction, the slice data along the layer are two-dimensional data, the dimension of the two-dimensional data comprises the seismic trace direction and the survey line direction, and the target horizon can reflect the target depth in the depth direction;
Decomposing the along-layer slice data based on two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the along-layer slice data, wherein the low-frequency component is used for reflecting the integral characteristics of the along-layer slice data, the high-frequency component is used for reflecting the local characteristics of the along-layer slice data on a target dimension, and the target dimension comprises at least one of the seismic trace direction, the survey line direction and an inclined direction between the seismic trace 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 component includes a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the oblique 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 crack detection data from 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 crack detection data according to the low frequency component, the first amplified component, the second amplified component, and the third amplified component includes:
Processing the low-frequency component and the first amplified component based on a two-dimensional wavelet inverse transformation to obtain first enhancement data of the bedding slice data in the direction of the measuring line;
Processing the low-frequency component and the second amplified component based on a two-dimensional wavelet inverse transformation to obtain second enhancement data of the bedding slice data in the seismic channel direction;
processing the low-frequency component and the third amplified component based on a two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the bedding direction;
And determining the target crack detection data according to 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 inclined direction.
Optionally, before decomposing the slice data based on the two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slice data, the method further includes:
and denoising the surface slice data to obtain denoised surface 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 crack detection data, the apparatus comprising:
The first determining module is used for determining slice along a layer belonging to a target horizon in crack detection data, wherein the crack detection data are used for reflecting the distribution of cracks in a stratum, the crack detection data are three-dimensional data, the dimension of the three-dimensional data comprises a seismic trace direction, a survey line direction and a depth direction, the slice along the layer is two-dimensional data, the dimension of the two-dimensional data comprises the seismic trace direction and the survey line direction, and the target horizon can reflect the target depth of the depth direction;
The system comprises a decomposition module, a detection module and a detection module, wherein the decomposition module is used for decomposing the along-layer slice data based on two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the along-layer slice data, the low-frequency component is used for reflecting the integral characteristics of the along-layer slice data, the high-frequency component is used for reflecting the local characteristics of the along-layer slice data on a target dimension, and the target dimension comprises at least one of the seismic channel direction, the survey line direction and the inclination direction between the seismic channel 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 determining 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 component includes a first component belonging to the line direction, a second component belonging to the seismic trace direction, and a third component belonging to the oblique 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 amplified component based on a two-dimensional wavelet inverse transformation to obtain first enhancement data of the bedding slice data in the direction of the measuring line;
Processing the low-frequency component and the second amplified component based on a two-dimensional wavelet inverse transformation to obtain second enhancement data of the bedding slice data in the seismic channel direction;
processing the low-frequency component and the third amplified component based on a two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the bedding direction;
And determining the target crack detection data according to 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 inclined direction.
Optionally, the apparatus further comprises:
and the denoising module is used for denoising the surface slice data to obtain denoised surface 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, code set or instruction set loaded and executed by the processor to implement a method of processing crack detection data as described in the above aspect.
According to another aspect of the present application, there is provided a computer readable storage medium having stored therein at least one program code loaded and executed by a processor to implement a method of processing crack detection data as described in the above aspect.
According to another aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the method of processing crack detection data provided in various alternative implementations of the above aspects.
The technical scheme provided by the application has the beneficial effects that at least:
The low frequency component and the high frequency component of the slice data can be decomposed by two-dimensional wavelet transformation. 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 surface slice data, so that the surface slice data reflects the distribution of local cracks more accurately, and the accuracy of crack determination is improved. In the process, other modes are not needed to be adopted for reprocessing crack detection data, so that the processing process is simplified.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the principle of crack detection provided by an embodiment of the present application;
FIG. 2 is a flow chart of a method for processing crack detection data according to an embodiment of the present application;
FIG. 3 is a flow chart of another method for processing crack detection data according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an implementation process for determining target crack detection data according to an embodiment of the present application;
FIG. 5 is a schematic illustration 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 application;
FIG. 7 is a schematic diagram of another apparatus for processing crack detection data according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of yet another apparatus for processing crack detection data according to an embodiment of the present application;
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 application and together with the description, serve to explain the principles of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.
First, the nouns involved in the embodiments of the present application will be described:
Crack detection data: the fracture monitoring data is data which is acquired through a seismic exploration technology and is used for reflecting the distribution of the fractures in the stratum, and can be also called a fracture detection data body. The crack detection data in the embodiment of the application refers to a three-dimensional seismic data volume.
Seismic prospecting: seismic exploration refers to a geophysical exploration method for deducing the properties and morphology of underground rock formations by observing and analyzing the propagation rule of seismic waves generated by artificial earthquakes in the underground by utilizing the elasticity and density differences of underground media of elastic waves caused by artificial excitation.
Fig. 1 is a schematic diagram illustrating a principle of detecting cracks according to an embodiment of the present application. As shown in fig. 1, the inspector arranges a plurality of rows of detectors 101 in a line on the ground, and a plurality of detectors 101 are arranged in each row, and the detectors 101 connected together in each row are called a line. Seismic waves are then generated in the formation by the source vehicle 102. Signals generated by the propagation of seismic waves in the formation are acquired by detectors 101. The instrument car 103 acquires (and amplifies) the signal acquired by the detector 101. And then analyzing the signals acquired by the instrument car 101 by the computer equipment, thereby obtaining a crack detection data body. The direction of the straight line of each row of detectors 101 is generally referred to as a line direction, and the direction perpendicular to the line is referred to as a seismic trace direction.
Fig. 2 is a flowchart of a method for processing crack detection data according to an embodiment of the present application. The method may be used with a computer device. As shown in fig. 2, the method includes:
Step 201, determining slice data of the boundary layer belonging to the target horizon in the crack detection data.
The fracture detection data is used to reflect the distribution of fractures in the formation. The crack detection data are three-dimensional data, and the dimensions of the three-dimensional data comprise the direction of a seismic trace, the direction of a survey line and the direction of depth. The slice data is two-dimensional data, and the dimensions of the two-dimensional data comprise the direction of the seismic trace and the direction of the survey line. The target layer can reflect a target depth in a depth direction.
And 202, decomposing the slice data on the surface based on the two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the slice data on the surface.
The low frequency component is used to reflect the global characteristics of the slice data and the high frequency component is used to reflect the local characteristics of the slice data in the target dimension. The target dimension includes at least one of a seismic trace direction, a survey line direction, and a tilt direction between the seismic trace direction and the survey line direction.
And 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, according to the method for processing crack detection data provided by the embodiment of the application, the low-frequency component and the high-frequency component of the slice data can be decomposed through two-dimensional wavelet transformation. 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 surface slice data, so that the surface slice data reflects the distribution of local cracks more accurately, and the accuracy of crack determination is improved. In the process, other modes are not needed to be adopted for reprocessing crack detection data, so that the processing process is simplified.
Fig. 3 is a flowchart of another method for processing crack detection data according to an embodiment of the present application. The method may be used with a computer device. As shown in fig. 3, the method includes:
Step 301, determining slice data of the fracture detection data belonging to the target horizon.
The fracture detection data is used to reflect the distribution of fractures in the formation. The crack detection data are three-dimensional data, and the dimensions of the three-dimensional data comprise the direction of a seismic trace, the direction of a survey line and the direction of depth. The direction of the measuring line is perpendicular to the direction of the seismic channel. Illustratively, the fracture detection data is data determined by means of a seismic survey as shown in FIG. 1.
The target layer can reflect a target depth in a depth direction. The target depth is the depth of the formation where the fracture distribution needs to be determined. The formation is planar or uneven. The along slice data is two-dimensional data, and the dimensions of the two-dimensional data include the direction of the seismic trace and the direction of the survey line.
Optionally, the fracture detection data and the target horizon are manually uploaded to a computer device, from which the computer device can extract the along slice data from the fracture detection data.
Step 302, denoising the slice data of the strandlayer to obtain denoised slice data of the strandlayer.
The computer equipment performs denoising processing on the slice data of the strandlayer, so that noise such as isolated points, burrs and the like in the slice data of the strandlayer can be eliminated, the noise cannot reflect the distribution of real cracks, and the accuracy of the finally determined cracks can be improved by eliminating the noise.
Illustratively, the implementation process of denoising the slice of the layer by the computer device comprises the following steps:
In step s1, the slice data is regularized to obtain regularized slice data AA 0 (line).
Since the slice data is two-dimensional data, referring to the example in fig. 1, it includes information acquired by each detector, and thus the slice data is two-dimensional data. The number of lines of the two-dimensional array is the total line number N_line, the number of columns of the two-dimensional data is the total seismic trace number N_trace, and A 0 (line, trace) is adopted to represent the two-dimensional array.
The computer device first calculates an average value M of the two-dimensional data a 0 (line, trace), which satisfies:
The computer device then regularizes the two-dimensional array a 0 (line) using the average value M. Namely, the values in the two-dimensional array A 0 (line) are regularized to the vicinity of 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) meets the following conditions:
In step s2, the regularized slice data are divided into sub-blocks with the size w (w is greater than or equal to 9), and the average value of each sub-block is calculated. And binarizing the numerical value in each sub-block by taking the average value of the sub-block as a threshold value (more than average value is taken as 1 and less than average value is taken as 0) to obtain the binarized slice data AA 1 (line).
Wherein the average value AV of each sub-block satisfies:
In step s3, the slice data is denoised by two-dimensional array a 0 (line) and two-dimensional array AA 1 (line).
For each sampling point (value) in the two-dimensional array AA 1 (line, trace), if AA 1 (line, trace) =0 and more than three adjacent points in the adjacent points of the sampling point have a value of 1, the value of the sampling point in the two-dimensional array a 0 (line, trace) is modified to be the average value of the sub-blocks where the sampling point is located.
For each sample point in the two-dimensional array AA 1 (line, trace), if AA 1 (line, trace) =1, and the sample point is not an endpoint and satisfies:
The value of the sample point in two-dimensional array a 0 (line) is modified to 0. Thereby realizing denoising processing of the slice data on the layers.
And 303, decomposing the slice data on the basis of the two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the slice data on the surface.
The low frequency component (also referred to as an approximation component) is used to reflect the global characteristics of the along-slice data, and the high frequency component is used to reflect the local characteristics of the along-slice data in a target dimension including at least one of a trace direction, a line direction, and an oblique direction between the trace direction and the line direction, the oblique direction being in the same plane as the trace direction and the line direction. Optionally, the computer device-decomposed slice data is denoising 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 an oblique direction. That is, the first component is a high frequency component of the along slice data in the line direction, the second component is a high frequency component of the along slice data in the seismic trace direction, and the third component is a high frequency component of the along slice data in the dip direction. Optionally, the oblique direction is a direction intermediate the line direction and the trace direction, for example, in a plane, the line direction is 0 °, the trace direction is 90 °, and the oblique direction is 45 °.
Illustratively, the computer device decomposes the slice data based on a two-dimensional wavelet transform to obtain a low-frequency component A j+1 (line, trace) and a first component of a two-dimensional array A 0 (line, trace) corresponding to the slice dataSecond component/>Third component/>The method meets the following conditions:
Aj+1(line,trace)=(low(line)*Aj(line，trace)*low(trace)；
Here, low (line) indicates a low-pass filter in a line direction (a line direction of the two-dimensional array a 0), low (trace) indicates a low-pass filter in a trace direction (a column direction of the two-dimensional array a 0), high (line) indicates a high-pass filter in a line direction, and high (trace) indicates a high-pass filter in a trace direction. The direction of the filter indicates that the filter is used to calculate a value corresponding to the direction in the two-dimensional array a 0 (line). * Representing a convolution operation. And performing high-pass filtering or low-pass filtering decomposition once according to the row direction and the column direction of the two-dimensional array A 0 (line), and 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 304, amplifying the high-frequency component to obtain an amplified high-frequency component.
Alternatively, the computer device performs the amplification processing of the high-frequency component by multiplying the objective function with the high-frequency component. The value of the objective function is equal to or greater than one. Compared with the high-frequency component, the amplified high-frequency component can improve the accuracy of the reflected distribution of the local cracks.
Optionally, the computer device amplifies 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 class, and the third class, respectively, with an objective function.
Illustratively, the objective function is a (2 j), which is an increasing function of the scale 2 j of the two-dimensional wavelet transform, and a (2 j). Gtoreq.1. The first amplified component isThe second amplified component is/>The third amplification component is/>
Step 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 two-dimensional wavelet inverse transformation 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 based on the low frequency component, the first amplified component, the second amplified component, and the third amplified component. As shown in fig. 4, the implementation procedure of step 305 includes the following steps 3051 and 3052:
In step 3051, 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 a two-dimensional inverse wavelet transform to obtain first enhancement data of the along-slice data in the line direction. And processing the low-frequency component and the second amplified component based on the two-dimensional wavelet inverse transformation to obtain second enhancement data of the slice data along the layer in the direction of the seismic channel. And processing the low-frequency component and the third amplified component based on the two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the slice in the oblique direction.
In step 3052, target fracture detection data is determined based on root mean square of the first enhancement data and the second enhancement data and the third enhancement data.
The fractures are typically distributed in the formation in an oblique direction. The root mean square of the determined 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 cracks can be reflected. According to the root mean square determination target crack detection data, the accuracy of crack determination can be further improved. The third enhancement data belongs to the tilt direction.
Optionally, the computer device determines an average of the root mean square and the third enhancement data as the target crack detection data. The computer device is further capable of determining a weighted average of the root mean square and third enhancement data as the target crack detection data. The weights in determining the weighted average may be manually determined empirically. The target crack detection data belongs to an oblique direction.
Illustratively, the root mean square YY 1 of the first enhancement data and the second enhancement data satisfies:
Wherein YY k is first enhancement data and YY v is second enhancement data.
Step 306, displaying the 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 reflected local crack distribution. 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.
Fig. 5 is a schematic diagram of a crack detection image according to an embodiment of the present application. As shown in fig. 5, the first crack detection image 501 is an image displayed based on original crack detection data, and the second crack detection image 502 is an image displayed based on target crack detection data obtained after processing. The second crack detection image 502 has significantly enhanced image boundaries, more display details of the local crack, and improved continuity of the lines of the displayed crack compared to the first crack detection image 501.
It should be noted that the above 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 above method can also be performed by a computer device through an installation client for implementing the above method.
In summary, according to the method for processing crack detection data provided by the embodiment of the application, the low-frequency component and the high-frequency component of the slice data can be decomposed through two-dimensional wavelet transformation. 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 surface slice data, so that the surface slice data reflects the distribution of local cracks more accurately, and the accuracy of crack determination is improved. In the process, other modes are not needed to be adopted for reprocessing crack detection data, so that the processing process is simplified.
In addition, denoising processing is carried out on the surface slice data, so that the accuracy of crack determination can be further improved. And determining target crack detection data according to root mean square of the first enhancement data and the second enhancement data and the third enhancement data, wherein 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 sequence of the steps of the method provided in the embodiment of the present application may be appropriately adjusted, the steps may also be increased or decreased according to the situation, and any method that is easily conceivable to be changed by those skilled in the art within the technical scope of the present disclosure should be covered within the protection scope of the present disclosure, so that no further description is given.
Fig. 6 is a schematic structural diagram of an apparatus for processing crack detection data according to an embodiment of the present application. The apparatus may be used in a computer device. As shown in fig. 6, the apparatus 60 includes:
the first determining module 601 is configured to determine slice along a slice of the target horizon in the fracture detection data, where the fracture detection data is configured to reflect a distribution of fractures in the stratum, the fracture detection data is three-dimensional data, dimensions of the three-dimensional data include a seismic trace direction, a survey line direction, and a depth direction, the slice along the slice is two-dimensional data, dimensions of the two-dimensional data include the seismic trace direction and the survey line direction, and the target horizon is configured to reflect a target depth in the depth direction.
The decomposition module 602 is configured to decompose the slice data based on the two-dimensional wavelet transform to obtain a low-frequency component and a high-frequency component of the slice data, where the low-frequency component is used to reflect an overall feature of the slice data, and the high-frequency component is used to reflect a local feature of the slice 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.
The amplifying module 603 is configured to amplify the high-frequency component to obtain an amplified high-frequency component.
A second determining module 604 for determining target crack detection data based on 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 an oblique 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, configured to:
The target crack detection data is determined from the low frequency component, the first amplified component, the second amplified component, and the third amplified component.
Optionally, the second determining module 604 is configured to:
And processing the low-frequency component and the first amplified component based on the two-dimensional wavelet inverse transformation to obtain first enhancement data of the bedding slice data in the direction of the measuring line. And processing the low-frequency component and the second amplified component based on the two-dimensional wavelet inverse transformation to obtain second enhancement data of the slice data along the layer in the direction of the seismic channel. And processing the low-frequency component and the third amplified component based on the two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the slice in the oblique direction. And determining target crack detection data according to 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 an oblique direction.
Optionally, as shown in fig. 7, the apparatus 60 further includes:
And the denoising module 605 is used for denoising the surface slice data to obtain denoised surface slice data.
Optionally, as shown in fig. 8, the apparatus 60 further includes:
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 device for processing crack detection data provided in the above embodiment is only exemplified by the division of the above functional modules, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for processing the crack detection data provided in the above embodiment belongs to the same concept as the method embodiment for processing the crack detection data, and the specific implementation process is detailed in the method embodiment, which is not described herein again.
Embodiments of the present application also provide a computer device comprising: the crack detection data processing device comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the method for processing the crack detection data provided by each method embodiment.
Optionally, the computer device is a terminal. Fig. 9 is a schematic structural diagram of a terminal according to an embodiment of the present application.
In general, the terminal 900 includes: a processor 901 and a memory 902.
Processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 901 may be implemented in at least one hardware form of DSP (DIGITAL SIGNAL Processing), FPGA (Field-Programmable gate array), PLA (Programmable Logic Array ). Processor 901 may also include a main processor, which is a processor for processing data in an awake state, also referred to as a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 901 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 901 may also include an AI (ARTIFICIAL INTELLIGENCE ) processor for processing computing operations related to machine learning.
The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 902 is used to store at least one instruction for execution by processor 901 to implement a method of processing crack detection data provided by a method embodiment of the present application.
In some embodiments, the terminal 900 may further optionally include: a peripheral interface 903, and at least one peripheral. The processor 901, memory 902, and peripheral interface 903 may be connected by a bus or signal line. The individual peripheral devices may be connected to the peripheral device interface 903 via buses, signal lines, or circuit boards. Specifically, the peripheral device includes: at least one of radio frequency circuitry 904, a display 905, a camera assembly 906, audio circuitry 907, a positioning assembly 908, and a power source 909.
The peripheral interface 903 may be used to connect at least one peripheral device associated with an 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, either or both of the processor 901, the memory 902, and the peripheral interface 903 may be implemented on separate chips or circuit boards, as embodiments of the application are not limited in this respect.
The Radio Frequency circuit 904 is configured 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 via electromagnetic signals. The radio frequency circuit 904 converts an electrical signal into an electromagnetic signal for transmission, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 904 includes: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, subscriber identity module cards, and so forth. The radio frequency circuit 904 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: the world wide web, metropolitan area networks, intranets, generation mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (WIRELESS FIDELITY ) networks. In some embodiments, the radio frequency circuit 904 may further include NFC (NEAR FIELD Communication) related circuits, which is not limited by the present application.
The display 905 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display 905 is a touch display, the display 905 also has the ability to capture touch signals at or above the surface of the display 905. The touch signal may be input as a control signal to the processor 901 for processing. At this time, 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 a front panel of the terminal 900; in other embodiments, the display 905 may be at least two, respectively disposed on different surfaces of the terminal 900 or in a folded 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 905 may be arranged in an irregular pattern other than rectangular, i.e., a shaped screen. The display 905 may be made of LCD (Liquid CRYSTAL DISPLAY), OLED (Organic Light-Emitting Diode) or other materials.
The camera assembly 906 is used to capture images or video. 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 900 and the rear camera is disposed on the rear surface of the terminal. In some embodiments, the at least two rear cameras are any one of a main camera, a depth camera, a wide-angle camera and a tele camera, so as to realize that the main camera and the depth camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize a panoramic shooting and Virtual Reality (VR) shooting function or other fusion shooting functions. In some embodiments, camera assembly 906 may also include a flash. The flash lamp can be a single-color temperature flash lamp or a double-color temperature flash lamp. The dual-color temperature flash lamp refers to a combination of a warm light flash lamp and a cold light flash lamp, and can be used for light compensation under different color temperatures.
The audio circuit 907 may include a microphone and a speaker. The microphone is used for collecting sound waves of users and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 901 for processing, or inputting the electric signals to the radio frequency circuit 904 for voice communication. For purposes of stereo acquisition or noise reduction, the microphone may be plural and disposed at different portions of the terminal 900. The microphone may also be an array microphone or an omni-directional pickup microphone. The speaker is used to convert electrical signals from the processor 901 or the radio frequency circuit 904 into sound waves. The speaker may be a conventional thin film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only the electric signal can be converted into a sound wave audible to humans, but also the electric signal can be converted into a sound wave inaudible to humans for ranging and other purposes. In some embodiments, the audio circuit 907 may also include a headphone jack.
The location component 908 is used to locate the current geographic location of the terminal 900 to enable navigation or LBS (Location Based Service, location-based services). The positioning component 908 may be a positioning component based on the United states GPS (Global Positioning System ), the Beidou system of China, or the Galileo system of Russia.
The power supply 909 is used to supply power to the various components in the terminal 900. The power supply 909 may be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power source 909 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, terminal 900 can further include one or more sensors 910. The one or more sensors 910 include, but are not limited to: acceleration sensor 911, gyroscope sensor 912, pressure sensor 913, fingerprint sensor 914, optical sensor 915, and proximity sensor 916.
The acceleration sensor 911 can detect the magnitudes of accelerations on three coordinate axes of the coordinate system established with the terminal 900. For example, the acceleration sensor 911 may be used to detect components of gravitational acceleration in three coordinate axes. The processor 901 may control the touch display 905 to display a user interface in a landscape view or a portrait view according to the gravitational acceleration signal acquired by the acceleration sensor 911. The acceleration sensor 911 may also be used for the 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 collect a 3D motion of the user on the terminal 900 in cooperation with the acceleration sensor 911. The processor 901 may implement the following functions according to the data collected by the gyro sensor 912: motion sensing (e.g., changing UI according to a tilting operation by a user), image stabilization at shooting, game control, and inertial navigation.
The pressure sensor 913 may be provided at a side frame of the terminal 900 and/or a lower layer of the touch display 905. When the pressure sensor 913 is provided at a side frame of the terminal 900, a grip signal of the user to the terminal 900 may be detected, and the processor 901 performs left-right hand recognition or shortcut operation according to the grip signal collected by the pressure sensor 913. When the pressure sensor 913 is disposed at the lower layer of the touch display 905, the processor 901 performs control of the operability control on the UI interface according to the pressure operation of the user on the touch display 905. The operability controls include at least one of a button control, a scroll bar control, an icon control, and a menu control.
The fingerprint sensor 914 is used for collecting the fingerprint of the user, and the processor 901 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 914 or the fingerprint sensor 914 identifies the identity of the user according to the collected fingerprint. Upon recognizing that the user's identity is a trusted identity, the processor 901 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying for and changing settings, etc. The fingerprint sensor 914 may be provided on the front, back or side of the terminal 900. When a physical key or a vendor Logo is provided on the terminal 900, the fingerprint sensor 914 may be integrated with the physical key or the vendor Logo.
The optical sensor 915 is used to collect the intensity of ambient light. In one embodiment, the processor 901 may control the display brightness of the touch display 905 based on the intensity of ambient light collected by the optical sensor 915. Specifically, when the ambient light intensity is high, the display brightness of the touch display 905 is turned up; when the ambient light intensity is low, the display brightness of the touch display panel 905 is turned down. In another embodiment, the processor 901 may also dynamically adjust the shooting parameters of the camera assembly 906 based on the ambient light intensity collected by the optical sensor 915.
A proximity sensor 916, also referred to as a distance sensor, is typically provided on the front panel of the terminal 900. Proximity sensor 916 is used to collect the distance between the user and the front of 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 off screen state; when the proximity sensor 916 detects that the distance between the user and the front surface of the terminal 900 gradually increases, the processor 901 controls the touch display 905 to switch from the off-screen state to the on-screen state.
Those skilled in the art will appreciate that the structure shown in fig. 9 is not limiting and that more or fewer components than shown may be included or certain components may be combined or a different arrangement of components may be employed.
The embodiment of the application also provides a computer readable storage medium, at least one program code is stored in the computer readable storage medium, 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 realized.
The present application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method for processing crack detection data provided by the above-mentioned 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 for instructing relevant hardware, where 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 foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (6)
1. A method of processing crack detection data, the method comprising:
Determining slice data along a layer belonging to a target horizon in crack detection data, wherein the crack detection data are used for reflecting the distribution of cracks in a stratum, the crack detection data are three-dimensional data, the dimension of the three-dimensional data comprises a seismic trace direction, a survey line direction and a depth direction, the slice data along the layer are two-dimensional data, the dimension of the two-dimensional data comprises the seismic trace direction and the survey line direction, and the target horizon can reflect the target depth in the depth direction;
Decomposing the along-layer slice data based on two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the along-layer slice data, wherein the low-frequency component is used for reflecting the integral characteristics of the along-layer slice data, the high-frequency component is used for reflecting the local characteristics of the along-layer slice data on a target dimension, the target dimension comprises the seismic trace direction, the line direction and an oblique direction between the seismic trace direction and the line direction, and the high-frequency component comprises a first component belonging to the line direction, a second component belonging to the seismic trace direction and a third component belonging to the oblique direction;
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;
processing the low-frequency component and the first amplified component based on a two-dimensional wavelet inverse transformation to obtain first enhancement data of the bedding slice data in the direction of the measuring line; processing the low-frequency component and the second amplified component based on a two-dimensional wavelet inverse transformation to obtain second enhancement data of the bedding slice data in the seismic channel direction; processing the low-frequency component and the third amplified component based on a two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the bedding direction;
And determining target crack detection data according to 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 inclined direction.
2. The method of claim 1, wherein prior to 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 surface slice data to obtain denoised surface slice data.
3. The method according to claim 1, wherein the method further comprises:
and displaying a crack detection image according to the target crack detection data.
4. An apparatus for processing crack detection data, the apparatus comprising:
The first determining module is used for determining slice along a layer belonging to a target horizon in crack detection data, wherein the crack detection data are used for reflecting the distribution of cracks in a stratum, the crack detection data are three-dimensional data, the dimension of the three-dimensional data comprises a seismic trace direction, a survey line direction and a depth direction, the slice along the layer is two-dimensional data, the dimension of the two-dimensional data comprises the seismic trace direction and the survey line direction, and the target horizon can reflect the target depth of the depth direction;
the system comprises a decomposition module, a processing module and a processing module, wherein the decomposition module is used for decomposing the slice-along data based on two-dimensional wavelet transformation to obtain a low-frequency component and a high-frequency component of the slice-along data, the low-frequency component is used for reflecting the integral characteristics of the slice-along data, the high-frequency component is used for reflecting the local characteristics of the slice-along data in a target dimension, the target dimension comprises the seismic channel direction, the line direction and an oblique direction between the seismic channel direction and the line direction, and the high-frequency component comprises a first component belonging to the line direction, a second component belonging to the seismic channel direction and a third component belonging to the oblique direction;
The amplifying module is used for 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 used for processing the low-frequency component and the first amplified component based on the two-dimensional wavelet inverse transformation to obtain first enhancement data of the bedding slice data in the direction of the measuring line; processing the low-frequency component and the second amplified component based on a two-dimensional wavelet inverse transformation to obtain second enhancement data of the bedding slice data in the seismic channel direction; processing the low-frequency component and the third amplified component based on a two-dimensional wavelet inverse transformation to obtain third enhancement data of the slice data along the bedding direction; and determining target crack detection data according to 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 inclined direction.
5. A computer device comprising a processor and a memory, wherein the memory has stored therein at least one program that is loaded and executed by the processor to implement the method of processing crack detection data as claimed in any one of claims 1 to 3.
6. A computer readable storage medium having stored therein at least one program code loaded and executed by a processor to implement the method of processing crack detection data as claimed in any one of claims 1 to 3.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101545984A (en) * | 2009-05-05 | 2009-09-30 | 中国石油集团西北地质研究所 | Seismic coherence algorithm based on wavelet transformation |
| CN104880730A (en) * | 2015-03-27 | 2015-09-02 | 西安交通大学 | Seismic data time-frequency analysis and attenuation estimation method based on Synchrosqueezing transform |
Family Cites Families (3)
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| RU2567434C2 (en) * | 2014-05-08 | 2015-11-10 | Алексей Алексеевич Никитин | Method for seismic data processing and interpreting |
| CN109782340A (en) * | 2019-01-14 | 2019-05-21 | 西安交通大学 | A kind of earthquake data before superposition frequency spectrum expansion method based on subangle processing |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101545984A (en) * | 2009-05-05 | 2009-09-30 | 中国石油集团西北地质研究所 | Seismic coherence algorithm based on wavelet transformation |
| CN104880730A (en) * | 2015-03-27 | 2015-09-02 | 西安交通大学 | Seismic data time-frequency analysis and attenuation estimation method based on Synchrosqueezing transform |
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