CN114428295A - Edge preserving diffusion filtering method based on fault confidence coefficient parameter control - Google Patents

Edge preserving diffusion filtering method based on fault confidence coefficient parameter control Download PDF

Info

Publication number
CN114428295A
CN114428295A CN202011014935.7A CN202011014935A CN114428295A CN 114428295 A CN114428295 A CN 114428295A CN 202011014935 A CN202011014935 A CN 202011014935A CN 114428295 A CN114428295 A CN 114428295A
Authority
CN
China
Prior art keywords
diffusion
fault
tensor
filtering
eigenvalue
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011014935.7A
Other languages
Chinese (zh)
Other versions
CN114428295B (en
Inventor
宋明水
王延光
冯德永
尚新民
李红梅
朱剑兵
王静
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Petroleum and Chemical Corp
Geophysical Research Institute of Sinopec Shengli Oilfield Co
Original Assignee
China Petroleum and Chemical Corp
Geophysical Research Institute of Sinopec Shengli Oilfield Co
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Petroleum and Chemical Corp, Geophysical Research Institute of Sinopec Shengli Oilfield Co filed Critical China Petroleum and Chemical Corp
Priority to CN202011014935.7A priority Critical patent/CN114428295B/en
Publication of CN114428295A publication Critical patent/CN114428295A/en
Application granted granted Critical
Publication of CN114428295B publication Critical patent/CN114428295B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
    • G01V1/364—Seismic filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/30—Noise handling
    • G01V2210/32—Noise reduction
    • G01V2210/324—Filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/64—Geostructures, e.g. in 3D data cubes
    • G01V2210/642—Faults

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention provides an edge preserving diffusion filtering method based on fault confidence coefficient parameter control. The method comprises the following steps: preprocessing the seismic data by using a Gaussian kernel function with a specific noise scale; reconstructing a structure tensor matrix and calculating to obtain eigenvalues and corresponding eigenvectors; analyzing the anisotropic diffusion trend, and redesigning the eigenvalue of the diffusion tensor by combining with the fault confidence coefficient parameters; and reconstructing the diffusion tensor by using the eigenvalue and the eigenvector of the diffusion tensor, solving a three-dimensional anisotropic diffusion filtering equation, and obtaining a filtering result after multiple iterations. The edge preserving diffusion filtering method based on fault confidence coefficient parameters is mainly used for improving the signal-to-noise ratio of seismic data and protecting stratum boundary and breakpoint position information of geologic bodies such as faults and the like.

Description

Edge preserving diffusion filtering method based on fault confidence coefficient parameter control
Technical Field
The invention relates to a fault and fault fracture zone identification and description method, belongs to the field of seismic data interpretation, and particularly relates to an edge preserving diffusion filtering method based on fault confidence coefficient parameter control.
Background
With the continuous improvement of the exploration and development degree of an oil field, the position of a fault block type oil and gas reservoir is more and more important, the geological features of the fault block type oil reservoir are complex, the acquired and processed data still has certain noise, and particularly, the noise of the seismic reflection data of a near fault is stronger than that of the data of other areas. Most fault detection methods in use today are mainly identified and described by local sharp changes at the fault location, making these methods sensitive to noise. Therefore, it is necessary to perform noise suppression on the seismic data in advance while protecting edge information.
The applicant previously applied for CN108415077A a new edge detection low-order fault identification method (publication No. CN 108415077A). The novel edge detection low-order fault identification method comprises the following steps: step 1, suppressing random noise by applying a denoising technology combining three-dimensional multistage blind source separation and structure-preserving filtering in a time-space domain according to a post-stack seismic data body, and improving the signal-to-noise ratio of seismic data; step 2, carrying out dip angle search and estimation of a formation dip angle value in a time domain by applying a multi-channel coherent algorithm, and estimating the formation dip angle by applying time delay of the formation dip angle on seismic records in a frequency domain and carrying out Fourier transform on the time delay characteristic of the formation dip angle; step 3, based on time-frequency domain mixed dip angle scanning, calculating the structure orientation canny attribute in the analysis window on the basis of local layer leveling along the dip angle of the stratum; and 4, according to the structure-oriented canny attribute data volume, carrying out seismic identification on the low-order fault in the forms of well connecting sections, bedding slices and the like. The method suppresses random noise by applying a denoising technology combining three-dimensional multilevel blind source separation and structure-preserving filtering in a time space domain according to a post-stack seismic data volume, and improves the signal-to-noise ratio of seismic data; adopting a multi-channel coherent algorithm to search for the dip angle and estimate a stratum dip angle value; and calculating the structure-oriented canny attribute in the analysis window on the basis of local layer leveling along the dip angle of the stratum to form an effective low-order fault identification method.
However, the invention only solves the problem of low-order fault identification, has poor applicability, and can cause the loss of information data of discontinuous boundaries such as original faults and the like in the data processing process.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a novel fault confidence coefficient parameter control-based edge preserving diffusion filtering method suitable for denoising seismic data and protecting geologic body boundary information such as faults.
The method is mainly used for improving the signal-to-noise ratio of seismic data and protecting stratum boundary and breakpoint position information of geologic bodies such as faults and the like.
The technical solution is as follows:
an edge preserving diffusion filtering method based on fault confidence parameter control comprises the following steps:
step 1: firstly, preprocessing seismic data by using a Gaussian kernel function with a specific noise scale;
step 2: reconstructing a structure tensor matrix;
and step 3: obtaining an eigenvalue of a structure tensor matrix and a corresponding eigenvector;
and 4, step 4: calculating fault confidence coefficient parameters;
and 5: redesigning a diffusion tensor characteristic value according to the fault confidence coefficient parameters obtained in the step 4;
step 6: and after the diffusion tensor is calculated, solving a three-dimensional anisotropic diffusion equation by an iterative method to finally obtain the seismic data after diffusion filtering.
The above scheme further comprises:
the step 2 reconstructs a structure tensor matrix according to the following formula:
Figure BDA0002698744940000031
in the formula, SρStructure tensor, Uσ＝Kσ*U,KσIs a Gaussian kernel function, and the expression of the Gaussian kernel function is shown in formula (2); the operation is convolution operator, sigma is noise scale;
Figure BDA0002698744940000032
representing a matrix transposition and a matrix multiplication; kρThe integration scale, which is a gaussian kernel function, p, should generally be larger than the noise scale sigma,
Figure BDA0002698744940000033
alpha is a stability coefficient with the size between 0 and 1 and is used for controlling the contribution rate of the second derivative,
Figure BDA0002698744940000034
is second derivative information;
Figure BDA0002698744940000035
step 3, the eigenvalue of the structure tensor matrix and the corresponding eigenvector to the structure tensor S are obtainedρPerforming characteristic decomposition to obtain;
Figure BDA0002698744940000036
for the structure tensor SρIn other words, beta1、β2、β3Is three characteristic values thereof and beta1≥β2≥β3The corresponding feature vector is ζ1、ζ2And ζ3，ζ1Indicates the direction in which the gradient changes the greatest, ζ3Indicating the direction in which the gradient changes least and epsilon represents the lateral discontinuity factor.
According to the sizes of the three characteristic values and different corresponding characteristic vectors, the seismic image structure is expressed in four types:
beta (a)1≈0、β2≈0、β 30, the direction of the three eigenvectors has little or no change, which indicates that the region is a smooth region and indicates that the geologic body is isotropic;
(di) beta1>0、β2≈0、β 30, indicating at ζ1The direction change rate is large, the change rates of the other two characteristic directions are small and almost equal, and a planar linear geologic body is represented;
(III) beta1>0、β2>0、β3The value is approximately equal to 0, which indicates that the change rate of one direction is approximately zero, indicates discontinuous discontinuity information appearing at discontinuous boundaries such as faults and the like, and the corresponding model is a linear structure model;
(IV) beta1>0、β2>0、β3>0, the change rates of the three characteristic vector directions are different, and the change rate of each direction is not zero, which indicates that the area has no obvious earthquake structure information and the earthquake reflection is disordered.
Step 4, the fault confidence coefficient parameters including the linear confidence measure M are obtainedlineAnd area confidence measure Mplane(formula (4)), which is used to detect the relationship between the image structure of the seismic data and the structure tensor eigenvalue:
Figure BDA0002698744940000041
Mplaneand MlineThe value range is between 0 and 1, and the two respectively represent the neighborhood of a certain pointDegree of similarity between the planar structure and the linear structure, MlineAnd MplaneTaken together, a confidence measure M for detecting discontinuity boundary information including faultsfault：
Figure BDA0002698744940000042
Mline、MplaneAnd MfaultThe relationship to the formation structure is shown in the following table:
Figure BDA0002698744940000044
step 5 is to redesign the diffusion tensor eigenvalue according to the fault confidence coefficient parameters obtained in step 4, and in order to ensure that the diffusion direction is along the structure direction, the eigenvector of the diffusion tensor D should be matched with the structure tensor SρThe agreement gives:
Figure BDA0002698744940000043
wherein gamma is1、γ2And gamma3For the three eigenvalues of the diffusion tensor D, the corresponding eigenvector is ζ1、ζ2And ζ3。
In three-dimensional anisotropic diffusion filtering, the magnitude of the diffusion filtering strength depends on the eigenvalues γ of the diffusion tensor D1、γ2And gamma3The closer the eigenvalue is to 0, the smaller the intensity of diffusion filtering; the eigenvalue approaches 1 and the diffusion filter strength is greater.
Will Mline、MplaneAnd fault confidence measure MfaultThe eigenvalues introduced into the diffusion tensor D are designed to have the following form:
Figure BDA0002698744940000051
wherein a is a positive number close to 0,
Figure BDA0002698744940000052
for Sigmoid function, s and τ represent threshold and slope, respectively, function hτ(x) The intensity of the diffusion filtering between the two eigenvectors can be effectively ensured.
In summary, the design method of the characteristic value can enhance the diffusion filtering effect and simultaneously maintain the boundary information of special geologic body structures such as good faults and the like.
After the diffusion tensor D is calculated in the step 6, the three-dimensional anisotropic diffusion equation is solved through an iteration method, and finally the seismic data after diffusion filtering are obtained:
Figure BDA0002698744940000053
Ukand Uk+1Respectively representing data obtained by the k-th iterative computation and the k + 1-th iterative computation, wherein delta t is an iterative step length, and the value of delta t is small for stability in practical application.
Compared with the prior art, the fault confidence coefficient parameter is introduced into the anisotropic filtering, along the direction with the maximum gradient change, namely along the direction with the maximum characteristic vector, the diffusion coefficient of the direction approaches to 0, the intensity of the diffusion filtering is small, and the boundary information of discontinuous structures such as faults and the like can be protected; along the directions of the other two feature vectors, the size of the feature value can be adjusted according to three confidence parameters, namely, the planar confidence, the linear confidence and the fault confidence. If the fault confidence coefficient approaches to 0, the stratum structure characteristic is a planar structure, and the diffusion model is along gamma2And gamma3The formed plane has high filtering strength; if the fault confidence tends to 1, gamma2And gamma3The value of (a) is an integer close to 0, and the diffusion filtering strength is weak along the directions of the two eigenvectors; the information of discontinuous boundaries such as faults can be effectively protected.
Drawings
FIG. 1 is a flowchart illustrating an implementation of an edge preserving diffusion filtering method based on fault confidence parameter control according to the present invention.
Fig. 2 is an effect diagram of theoretical verification of a three-dimensional Qdome noisy model (signal-to-noise ratios are 5, 3, and 1, respectively) by using the method, wherein: (a) the seismic data processing method includes the steps of (a) an original seismic section (SNR ═ 1), (b) a filtered seismic section (SNR ═ 1), (c) a pre-filter similarity attribute (SNR ═ 1), (d) a post-filter similarity attribute (SNR ═ 1), (e) an original seismic section (SNR ═ 3), (f) a post-filter seismic section (SNR ═ 3), (g) a pre-filter similarity attribute (SNR ═ 3), (h) a post-filter similarity attribute (SNR ═ 3), (i) an original seismic section (SNR ═ 5), (j) a post-filter seismic section (SNR ═ 5), (k) a pre-filter similarity attribute (SNR ═ 5), and (l) a post-filter similarity attribute (SNR ═ 5).
FIG. 3 is a diagram of an actual seismic profile before and after filtering using the method, wherein: (a) a filtered front inline seismic profile, (b) a filtered rear inline seismic profile.
FIG. 4 is a sectional view of the similarity attribute of actual seismic data before and after filtering by the method, wherein: (a) a pre-filtering similarity attribute, and (b) a post-filtering similarity attribute.
FIG. 5 is a coherent slice of actual seismic data before and after filtering using the method, wherein: (a) pre-filtering coherence time slices, (b) post-filtering coherence time slices.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
Example 1
A three-dimensional anisotropic diffusion filtering method based on confidence coefficient parameter control comprises the following steps.
Step 1: seismic data is first preprocessed using a gaussian kernel function of a particular noise scale.
Step 2: a structure tensor matrix is reconstructed.
Figure BDA0002698744940000071
In the formula of Uσ＝Kσ*U,KσIs a Gaussian kernel function, and the expression of the Gaussian kernel function is shown in formula (2); the operation is convolution operator, sigma is noise scale;
Figure BDA0002698744940000072
representing a matrix transposition and a matrix multiplication; kρThe integration scale, which is a gaussian kernel function, p, should generally be larger than the noise scale sigma,
Figure BDA0002698744940000073
alpha is a stability coefficient with the size between 0 and 1 and is used for controlling the contribution rate of the second derivative,
Figure BDA0002698744940000074
is the second derivative information.
Figure BDA0002698744940000075
And step 3: and solving the eigenvalue of the structure tensor matrix and the corresponding eigenvector.
For structure tensor SρPerforming characteristic decomposition to obtain;
Figure BDA0002698744940000076
for the structure tensor SρIn other words, beta1、β2、β3Is three characteristic values thereof and beta1≥β2≥β3The corresponding feature vector is ζ1、ζ2And ζ3，ζ1Indicates the direction in which the gradient changes the greatest, ζ3Indicating the direction in which the gradient change is minimal. ε represents the lateral discontinuity factor.
The seismic image structure can be expressed into several types described in table 1 according to the size of the eigenvalue and different corresponding eigenvectors.
TABLE 1 relationship of Structure tensor eigenvalues to seismic image Structure
Figure BDA0002698744940000081
And 4, step 4: and (6) solving a fault confidence coefficient parameter.
Two confidence parameters-line confidence measures MlineAnd area confidence measure Mplane(formula (4)), which is used to detect the relationship between the image structure of the seismic data and the structure tensor eigenvalue:
Figure BDA0002698744940000082
Mplaneand MlineThe value range is between 0 and 1, the value range and the value range respectively represent the similarity degree of the neighborhood homofacial structure and the linear structure of a certain point, MplaneAnd MlineThe magnitude relationship between the same characteristic values is shown in table 2. Will MlineAnd MplaneTaken together, a confidence measure M for detecting discontinuity boundary information such as faults can be obtainedfault：
Figure BDA0002698744940000083
On a seismic section, if the continuity of the seismic event is good, the line-shaped confidence measure MlineTrend toward 0, planar confidence measure MplaneTrend to 1, fault confidence metric CfaultTends to 0; similarly, in the region with discontinuous phase axis such as fault, the linear confidence coefficient MlineTrend to 1, planar confidence MplaneTrend to 0, fault confidence parameter MfaultTending to 1.
Mline、MplaneAnd MfaultThe relationship to the formation structure is shown in the following table:
TABLE 2 relationship between confidence parameters and stratigraphic structure and eigenvalues
Figure BDA0002698744940000091
And 5: redesigning diffusion tensor eigenvalue according to fault confidence coefficient parameters obtained in step 4
To ensure that the diffusion direction is along the structure direction, the eigenvectors of the diffusion tensor D should correspond to the structure tensor SρCan be derived from the agreement that:
Figure BDA0002698744940000092
wherein gamma is1、γ2And gamma3For the three eigenvalues of the diffusion tensor D, the corresponding eigenvector is ζ1、ζ2And ζ3。
In three-dimensional anisotropic diffusion filtering, the magnitude of the diffusion filtering strength depends on the eigenvalues γ of the diffusion tensor D1、γ2And gamma3. The closer the eigenvalue is to 0, the smaller the intensity of diffusion filtering; the eigenvalue approaches 1 and the diffusion filter strength is greater.
In order to overcome the defect that the conventional diffusion filtering method can not keep the data edge information, M is usedline、MplaneAnd fault confidence measure MfaultThe following form can be obtained by introducing the eigenvalues of the diffusion tensor D into the design:
Figure BDA0002698744940000093
wherein a is a positive number close to 0,
Figure BDA0002698744940000094
for Sigmoid function, s and τ represent threshold and slope, respectively, function hτ(x) The intensity of the diffusion filtering between the two eigenvectors can be effectively ensured.
Is represented by formula (7)The principle of the eigenvalue design scheme of the diffusion tensor is as follows: along the direction of greatest change in gradient, i.e. along the eigenvector ζ1Direction, gamma1The value of (a) is that the diffusion coefficient in the direction approaches to 0, the intensity of diffusion filtering is small, and the boundary information of discontinuous structures such as faults and the like can be protected; along a feature vector ζ2And ζ3The magnitude of the eigenvalues may be according to Mplane、MlineAnd MfaultThese three confidence parameters are adjusted. If M isfault→ 0, the formation structure is characterized by a planar structure, γ2And gamma3Is close to 1, diffusion model is along zeta2And ζ3The formed plane has high filtering strength; if M isfault→1，γ1And gamma2Is an integer a close to 0, the diffusion filtering strength is weak along the two eigenvector directions; the information of discontinuous boundaries such as faults can be effectively protected.
In summary, the design method of the characteristic value can enhance the diffusion filtering effect and simultaneously maintain the boundary information of special geologic body structures such as good faults and the like.
Example 2
Based on the above example 1, step 6: after the diffusion tensor D is calculated, the three-dimensional anisotropic diffusion equation can be solved through an iterative method, and finally, the seismic data after diffusion filtering are obtained:
Figure BDA0002698744940000101
Ukand Uk+1Respectively representing data obtained by the k-th iterative computation and the k + 1-th iterative computation, wherein delta t is an iterative step length, and the value of delta t is small for stability in practical application.
Example 3
With reference to the attached drawings, an edge preserving diffusion filtering method based on fault confidence coefficient parameter control mainly comprises the following steps:
s1: seismic data is first preprocessed using a gaussian kernel function of a particular noise scale.
S2: a structure tensor matrix is reconstructed.
S3: and solving the eigenvalue of the structure tensor matrix and the corresponding eigenvector.
The seismic image structure can be expressed into four types according to the size of the characteristic value and different corresponding characteristic vectors:
when beta is1≈β2≈β3When the value is approximately equal to 0, the change rate of the image point in the three characteristic vector directions is small or unchanged, which indicates that the region is a smooth region without any abnormal construction condition, and the corresponding model is a uniform isotropic structure model;
when in use
Figure BDA0002698744940000113
When at ζ1The change rate of the direction is large, the change rates of the other two directions are not obvious, relatively flat stratum information is reflected, and the corresponding model is an approximate planar linear structure;
when in use
Figure BDA0002698744940000114
When the three direction change rates are different, one direction change rate is approximately zero, which represents discontinuous discontinuity information appearing at discontinuous boundaries such as faults, and the corresponding model is a linear structure model;
when in use
Figure BDA0002698744940000115
In the process, the change rates of the three directions are different, and the change rate of each direction is not zero, which indicates that the area has no obvious earthquake structure information and the earthquake reflection is disordered.
S4: and (6) solving a fault confidence coefficient parameter.
Figure BDA0002698744940000111
Figure BDA0002698744940000112
MplaneAnd MlineAnd when the value range is between 0 and 1, the two values respectively represent the similarity degree of the neighborhood of a certain point with the planar structure and the linear structure.
On a seismic section, if the continuity of the seismic event is good, the line-shaped confidence measure MlineTrend toward 0, planar confidence measure MplaneTrend to 1, fault confidence metric CfaultTends to 0; similarly, in the region with discontinuous phase axis such as fault, the linear confidence coefficient MlineTrend to 1, planar confidence MplaneTrend to 0, fault confidence parameter MfaultTending to 1.
S5: and (4) redesigning the characteristic value of the diffusion tensor according to the fault confidence coefficient parameters obtained in the step (4).
Figure BDA0002698744940000121
Wherein a is a positive number close to 0,
Figure BDA0002698744940000122
sigmoid functions proposed for Terebes et al (2005), s and τ representing threshold and slope, respectively, function hτ(x) The intensity of the diffusion filtering between the two eigenvectors can be effectively ensured.
The principle of the eigenvalue design scheme of the diffusion tensor shown in equation (3) is: along the direction of greatest change in gradient, i.e. along the eigenvector ζ1Direction, gamma1The value of (a) is that the diffusion coefficient in the direction approaches to 0, the intensity of diffusion filtering is small, and the boundary information of discontinuous structures such as faults and the like can be protected; along a feature vector ζ2And ζ3The magnitude of the eigenvalues may be according to Mplane、MlineAnd MfaultThese three confidence parameters are adjusted. If M isfault→ 0, the formation structure is characterized by a planar structure, γ2And gamma3Is close to 1, diffusion model is along zeta2And ζ3The formed plane has high filtering strength; if M isfault→1，γ1And gamma2Is an integer a close to 0, the diffusion filtering strength is weak along the two eigenvector directions; the information of discontinuous boundaries such as faults can be effectively protected.
S6: after the diffusion tensor is calculated, the three-dimensional anisotropic diffusion equation can be solved through an iterative method, and finally the seismic data after diffusion filtering are obtained.
The invention obtains the final diffusion-filtered seismic data. Compared with the prior art, the method can effectively improve the signal-to-noise ratio of the seismic data, the form and the spread of faults on the seismic section after filtering can be described accurately, and small faults which are difficult to identify in the original data can be identified accurately and clearly after filtering.
Example 4
The following are specific examples of applications of the present invention:
the method is applied to certain three-dimensional post-stack seismic data, and finally the longitudinal line seismic profile shown in fig. 2, the similarity attribute profile shown in fig. 3 and the coherent body slice shown in fig. 4 are obtained.
As can be seen from FIG. 2, the filtering method of the present invention can better control the diffusion strength, better protect the fault edge information while denoising, make the fault boundary clearer after filtering, improve the similarity attribute obviously after filtering, and make the fault edge easier to identify on the attribute section.
As can be seen from FIG. 3, the in-phase axis is more continuous and the fault is more easily identified on the actual seismic section after the filtering by the method. As can be seen from fig. 4 and 5, on the filtered attribute graph, the form and the distribution of the fault are more easily and accurately described, and the form distribution of some small faults which are not easily identified in the original data is accurately and clearly identified after filtering, which also proves the effectiveness of the filtering method in maintaining fault edge information.
The technical contents not mentioned in the above modes can be realized by adopting or referring to the prior art.
It is noted that those skilled in the art, having the benefit of the teachings of this specification, may effect these and other changes in a manner similar to the equivalents thereof, or obvious variations thereof. All such variations are intended to be within the scope of the present invention.

Claims (11)

1. An edge preserving diffusion filtering method based on fault confidence coefficient parameter control is characterized by comprising the following steps:
step 1: firstly, preprocessing seismic data by using a Gaussian kernel function with a specific noise scale;
step 2: reconstructing a structure tensor matrix;
and step 3: obtaining an eigenvalue of a structure tensor matrix and a corresponding eigenvector;
and 4, step 4: calculating fault confidence coefficient parameters;
and 5: redesigning a diffusion tensor characteristic value according to the fault confidence coefficient parameters obtained in the step 4;
step 6: and after the diffusion tensor is calculated, solving a three-dimensional anisotropic diffusion equation by an iterative method to finally obtain the seismic data after diffusion filtering.
2. The method of claim 1, wherein the step 2 reconstructs a structure tensor matrix according to the following formula:
Figure FDA0002698744930000011
in the formula, SρStructure tensor, Uσ＝Kσ*U,KσIs a Gaussian kernel function, and the expression of the Gaussian kernel function is shown in formula (2); the operation is convolution operator, sigma is noise scale;
Figure FDA0002698744930000012
representing a matrix transposition and a matrix multiplication; kρThe integration scale, which is a gaussian kernel function, p, should generally be larger than the noise scale sigma,
Figure FDA0002698744930000013
alpha is a stability coefficient with the size between 0 and 1 and is used for controlling the contribution rate of the second derivative,
Figure FDA0002698744930000014
is second derivative information;
Figure FDA0002698744930000015
3. the method of claim 1 or 2, wherein the step 3 of finding the eigenvalue of the structure tensor matrix and the corresponding eigenvector to structure tensor SρPerforming characteristic decomposition to obtain;
Figure FDA0002698744930000021
for the structure tensor SρIn other words, beta1、β2、β3Is three characteristic values thereof and beta1≥β2≥β3The corresponding feature vector is ζ1、ζ2And ζ3，ζ1Indicates the direction in which the gradient changes the greatest, ζ3Indicating the direction in which the gradient changes least and epsilon represents the lateral discontinuity factor.
4. The edge preserving diffusion filtering method based on fault confidence coefficient parameter control according to claim 3, characterized in that the seismic image structure is expressed in four types according to the sizes of three eigenvalues and corresponding different eigenvectors:
beta (a)1≈0、β2≈0、β3Approximately equal to 0, with little or no change in the direction of the three eigenvectors, indicating that the region is a smooth region, representational groundThe plastid is isotropic;
(di) beta1>0、β2≈0、β30, indicating at ζ1The direction change rate is large, the change rates of the other two characteristic directions are small and almost equal, and a planar linear geologic body is represented;
(III) beta1>0、β2>0、β3The value is approximately equal to 0, which indicates that the change rate of one direction is approximately zero, indicates discontinuous discontinuity information appearing at discontinuous boundaries such as faults and the like, and the corresponding model is a linear structure model;
(IV) beta1>0、β2>0、β3>0, the change rates of the three characteristic vector directions are different, and the change rate of each direction is not zero, which indicates that the area has no obvious earthquake structure information and the earthquake reflection is disordered.
5. The method of claim 3 for edge preserving diffusion filtering based on fault confidence parameter control, characterized by:
step 4, the fault confidence coefficient parameters including the linear confidence measure M are obtainedlineAnd area confidence measure Mplane(formula (4)), which is used to detect the relationship between the image structure of the seismic data and the structure tensor eigenvalue:
Figure FDA0002698744930000031
Mplaneand MlineWhen the value range is between 0 and 1, the two respectively represent the similarity degree of the neighborhood homofacial structure and the linear structure of a certain point, and M is addedlineAnd MplaneTaken together, a confidence measure M for detecting discontinuity boundary information including faultsfault：
Figure FDA0002698744930000032
6. The method of claim 5, wherein M is Mline、MplaneAnd MfaultThe relationship to the formation structure is shown in the following table:
Figure FDA0002698744930000034
7. the method of claim 6, wherein the step 5 is to redesign the eigenvalue of diffusion tensor according to the fault confidence parameters obtained in step 4, and the eigenvector of diffusion tensor D should be matched with the structure tensor S in order to ensure that the diffusion direction is along the construction directionρThe agreement gives:
Figure FDA0002698744930000033
wherein gamma is1、γ2And gamma3For the three eigenvalues of the diffusion tensor D, the corresponding eigenvector is ζ1、ζ2And ζ3。
8. The method of claim 7, wherein in the three-dimensional anisotropic diffusion filtering, the magnitude of the diffusion filtering strength depends on the eigenvalue γ of the diffusion tensor D1、γ2And gamma3The closer the eigenvalue is to 0, the smaller the intensity of diffusion filtering; the eigenvalue approaches 1 and the diffusion filter strength is greater.
9. The method of claim 7, wherein M is the sum of M and Mline、MplaneAnd fault confidence measure MfaultIntroduction ofIn the design of the eigenvalues of the diffusion tensor D, the following form is obtained:
Figure FDA0002698744930000041
wherein a is a positive number close to 0,
Figure FDA0002698744930000042
for Sigmoid function, s and τ represent threshold and slope, respectively, function hτ(x) The intensity of the diffusion filtering between the two eigenvectors can be effectively ensured.
10. The edge preserving diffusion filtering method based on fault confidence coefficient parameter control according to claim 7, wherein after the diffusion tensor D is calculated in the step 6, the three-dimensional anisotropic diffusion equation is solved through an iterative method, and finally, diffusion-filtered seismic data are obtained:
Figure FDA0002698744930000043
Ukand Uk+1Respectively representing data obtained by the k and k +1 iteration calculation, and delta t is an iteration step length.
11. The edge-preserving diffusion filtering method based on fault confidence parameter control according to claim 9, wherein the iteration step Δ t should be small in value.
CN202011014935.7A 2020-09-24 2020-09-24 Edge-preserving diffusion filtering method based on fault confidence parameter control Active CN114428295B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011014935.7A CN114428295B (en) 2020-09-24 2020-09-24 Edge-preserving diffusion filtering method based on fault confidence parameter control

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011014935.7A CN114428295B (en) 2020-09-24 2020-09-24 Edge-preserving diffusion filtering method based on fault confidence parameter control

Publications (2)

Publication Number Publication Date
CN114428295A true CN114428295A (en) 2022-05-03
CN114428295B CN114428295B (en) 2024-03-29

Family

ID=81310396

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011014935.7A Active CN114428295B (en) 2020-09-24 2020-09-24 Edge-preserving diffusion filtering method based on fault confidence parameter control

Country Status (1)

Country Link
CN (1) CN114428295B (en)

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070110294A1 (en) * 2005-11-17 2007-05-17 Michiel Schaap Image enhancement using anisotropic noise filtering
WO2011149609A1 (en) * 2010-05-28 2011-12-01 Exxonmobil Upstream Research Company Method for seismic hydrocarbon system analysis
US20120253680A1 (en) * 2011-03-30 2012-10-04 Hunt Energy Enterprises, Llc Method and System for Passive Electroseismic Surveying
CN103926616A (en) * 2014-04-11 2014-07-16 中国海洋石油总公司 Multi-scale anisotropic diffusion filtering method based on pre-stack CRP trace sets
CN104020492A (en) * 2013-07-01 2014-09-03 西安交通大学 Edge-preserving filtering method of three-dimensional earthquake data
US20150117143A1 (en) * 2013-10-29 2015-04-30 Mark W. Dobin Method for post-stack noise mitigation
CN106772604A (en) * 2016-12-28 2017-05-31 中国石油化工股份有限公司 Prestack seismic inversion method based on the fluid volume compressed coefficient
CN108022221A (en) * 2017-12-06 2018-05-11 河北工业大学 A kind of three-dimensional dispersion point cloud smoothing denoising method of Anisotropic diffusion filtering
CN109669213A (en) * 2019-02-25 2019-04-23 中国石油化工股份有限公司 Frequency dividing diffusing filter tomography intensifying method based on optimization Morlet small echo
CN110579804A (en) * 2019-10-10 2019-12-17 中国石油化工股份有限公司 Diffusion filtering method under structure tensor trace constraint based on absolute square gradient
CN110596751A (en) * 2019-07-19 2019-12-20 中国石油化工股份有限公司 Complex field multilayer anisotropic fault boundary extraction method

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070110294A1 (en) * 2005-11-17 2007-05-17 Michiel Schaap Image enhancement using anisotropic noise filtering
WO2011149609A1 (en) * 2010-05-28 2011-12-01 Exxonmobil Upstream Research Company Method for seismic hydrocarbon system analysis
US20120253680A1 (en) * 2011-03-30 2012-10-04 Hunt Energy Enterprises, Llc Method and System for Passive Electroseismic Surveying
CN104020492A (en) * 2013-07-01 2014-09-03 西安交通大学 Edge-preserving filtering method of three-dimensional earthquake data
US20150117143A1 (en) * 2013-10-29 2015-04-30 Mark W. Dobin Method for post-stack noise mitigation
CN103926616A (en) * 2014-04-11 2014-07-16 中国海洋石油总公司 Multi-scale anisotropic diffusion filtering method based on pre-stack CRP trace sets
CN106772604A (en) * 2016-12-28 2017-05-31 中国石油化工股份有限公司 Prestack seismic inversion method based on the fluid volume compressed coefficient
CN108022221A (en) * 2017-12-06 2018-05-11 河北工业大学 A kind of three-dimensional dispersion point cloud smoothing denoising method of Anisotropic diffusion filtering
CN109669213A (en) * 2019-02-25 2019-04-23 中国石油化工股份有限公司 Frequency dividing diffusing filter tomography intensifying method based on optimization Morlet small echo
CN110596751A (en) * 2019-07-19 2019-12-20 中国石油化工股份有限公司 Complex field multilayer anisotropic fault boundary extraction method
CN110579804A (en) * 2019-10-10 2019-12-17 中国石油化工股份有限公司 Diffusion filtering method under structure tensor trace constraint based on absolute square gradient

Also Published As

Publication number Publication date
CN114428295B (en) 2024-03-29

Similar Documents

Publication Publication Date Title
US20090122061A1 (en) Seismic data processing
Liu et al. A 1D time-varying median filter for seismic random, spike-like noise elimination
Liu et al. Stacking seismic data using local correlation
Riji et al. Iterative bilateral filter for Rician noise reduction in MR images
US10281604B2 (en) Method of removing noise in seismic reverse-time migration
AlBinHassan et al. 3D edge-preserving smoothing and applications
Wang et al. Seismic data denoising for complex structure using BM3D and local similarity
Al-Dossary et al. Lineament-preserving filtering
CN110579804B (en) Diffusion filtering method under structure tensor trace constraint based on absolute square gradient
Wang et al. Structure-oriented DTGV regularization for random noise attenuation in seismic data
Wang et al. Robust singular value decomposition filtering for low signal-to-noise ratio seismic data
Boustani et al. Mapping channel edges in seismic data using curvelet transform and morphological filter
CN114428295A (en) Edge preserving diffusion filtering method based on fault confidence coefficient parameter control
Qi et al. Seismic fault enhancement and skeletonization
Sanchis et al. Sparse code shrinkage for signal enhancement of seismic data
Jun et al. Loss functions in machine learning for seismic random noise attenuation
Wang et al. A joint framework for seismic signal denoising using total generalized variation and shearlet transform
Bianchin et al. Acoustic impedance estimation from combined harmonic reconstruction and interval velocity
Ma Improved local wavenumber methods in the interpretation of potential field data
Bo et al. 3-D structure-oriented adaptive Gaussian pyramid for seismic multiscale fracture detection
Cai et al. An adaptive noise attenuation method for edge and amplitude preservation
Liu et al. Sparse-layer inversion with SPGL1 method
CN110542921B (en) Amplitude transverse change depicting method based on principal component analysis
Ni et al. Thin-bed detection using amplitude slicing at minimum interference frequency: Method and application
Wang et al. Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant