A method and device for eliminating noise from seismic coherent attribute images

Abstract

A method and device for eliminating noise from a seismic coherent attribute image, the method comprising: extracting an initial noise image of the seismic coherent attribute image and constructing a noise probability density function of the initial noise image; determining a mathematical expectation and variance corresponding to the noise probability density function; calculating a noise energy threshold using a noise threshold formula based on the mathematical expectation and variance; and obtaining a denoised seismic coherent attribute image based on the initial energy of the seismic coherent attribute image and the noise energy threshold.

Description

Technical Field

[0001] This article relates to the field of seismic exploration technology, and in particular to a method and device for eliminating noise in seismic coherent attribute images. Background Art

[0002] Fault detection helps understand fundamental hydrocarbon accumulation factors such as reservoir formation, hydrocarbon migration, accumulation, and preservation, and is a crucial step in oil and gas exploration. Conventional post-stack discontinuity attribute extraction is the primary technique for fault detection, with the most representative being the eigenvalue coherence technique (Gersztenkorn, 1999; Marfurt, 1999). This family of methods can identify distinct features of fault development, such as event dislocation, shape changes, bifurcations, mergers, and distortions. Consequently, they have gained widespread application and have spawned derivative techniques such as histogram enhancement, directional enhancement, and frequency enhancement. However, the data foundation of these algorithms relies on the amplitude comparison between adjacent seismic traces. Practical experience shows that when seismic imaging quality is high, fault features are clearly discernible in the coherence attributes; otherwise, they are obscured by "fuzzy" noise. This has limited impact on larger major faults, but has a significant impact on more prominent, subtle faults.

[0003] To address this phenomenon, predecessors have mostly started from the source of seismic data, using methods such as structure-guided filtering and edge-preserving filtering to eliminate the random noise of the seismic data itself while enhancing the structural characteristics of the seismic image, ultimately achieving the goal of improving the accuracy of fault detection.

[0004] Therefore, realizing a method to eliminate noise in seismic coherent attribute images is an urgent problem to be solved. Summary of the Invention

[0005] The inventors discovered that data images used in seismic exploration are both related to and distinct from natural images, and that the noise information contained in these images is similar. Starting with Gaussian noise, the most common type of noise found in natural images, and combining it with the response characteristics of coherent image noise, they summarized the distribution patterns of this type of noise, laying a theoretical foundation for subsequent coherent noise elimination.

[0006] The present application provides a method and device for eliminating noise in seismic coherent attribute images. The method calculates a noise energy threshold and uses the initial energy of the seismic coherent attribute image to remove the determined noise energy threshold to obtain a seismic coherent attribute image with noise eliminated.

[0007] In a first aspect, the present application provides a method for eliminating noise in a seismic coherent attribute image, the method comprising:

[0008] Extracting an initial noise image of the seismic coherent attribute image and constructing a noise probability density function of the initial noise image;

[0009] Determining the mathematical expectation and variance corresponding to the noise probability density function; calculating the noise energy threshold using a noise threshold formula based on the mathematical expectation and the variance;

[0010] A denoised seismic coherent attribute image is obtained according to the initial energy of the seismic coherent attribute image and the noise energy threshold.

[0011] In the second aspect, an embodiment of the present invention also provides a device for eliminating seismic coherent attribute image noise, the device comprising: a memory and a processor; the memory is used to store a program for eliminating seismic coherent attribute image noise, and the processor is used to read and execute the program for eliminating seismic coherent attribute image noise, and perform any one of the methods described in the above embodiments.

[0012] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon, and the data processing program is executed by a processor to implement the method for eliminating seismic coherent attribute image noise described in any one of the above embodiments.

[0013] Compared to related technologies, the present application provides a method and apparatus for eliminating noise from seismic coherent attribute images. The method comprises: extracting an initial noise image from the seismic coherent attribute image and constructing a noise probability density function for the initial noise image; determining the mathematical expectation and variance corresponding to the noise probability density function; calculating a noise energy threshold using a noise threshold formula based on the mathematical expectation and variance; and obtaining a denoised seismic coherent attribute image based on the initial energy of the seismic coherent attribute image and the noise energy threshold. The present application calculates the noise energy threshold using a noise threshold formula, and uses the initial energy of the seismic coherent attribute image to remove the determined noise energy threshold to obtain a denoised seismic coherent attribute image.

[0014] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0016] Figure 1This is a flow chart of a method for eliminating noise in seismic coherent attribute images according to an embodiment of the present application;

[0017] Figure 2 Schematic diagram of a device for eliminating noise in seismic coherent attribute images according to an embodiment of the present application;

[0018] Figure 3 is an original seismic coherence attribute image in some exemplary embodiments;

[0019] Figure 4 is a multi-scale filter response of a seismic coherence attribute image in some exemplary embodiments;

[0020] Figure 5 Schematic diagram of a histogram of multi-scale filtering responses and Rayleigh distribution simulation results of a seismic coherence attribute image in some exemplary embodiments;

[0021] Figure 6 Schematic diagram of fitting results of a histogram of a multi-scale filtering response and a probability density distribution function of a seismic coherent attribute image in some exemplary embodiments;

[0022] Figure 7 Schematic diagram showing comparison of components and denoising results of a seismic coherent attribute image at scale 1 and direction 1 in some exemplary embodiments;

[0023] Figure 8 Schematic diagram showing comparison of components and denoising results of seismic coherent attribute images at scale 1 and direction 4 in some exemplary embodiments;

[0024] Figure 9 Schematic diagram of comparison between seismic coherent attribute images and denoising results in some exemplary embodiments. DETAILED DESCRIPTION

[0025] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0026] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0027] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0028] The embodiment of the present invention provides a method for eliminating noise in seismic coherent attribute images, such as Figure 1 As shown, the method includes steps S100-S130:

[0029] S100: extracting an initial noise image of the seismic coherent attribute image and constructing a noise probability density function of the initial noise image;

[0030] S110: Determine the mathematical expectation and variance corresponding to the noise probability density function;

[0031] S120: Calculating a noise energy threshold using a noise threshold formula according to the mathematical expectation and the variance;

[0032] S130: Obtaining a denoised seismic coherent attribute image according to the initial energy of the seismic coherent attribute image and the noise energy threshold.

[0033] In an exemplary embodiment, the initial noise image for extracting the seismic coherent attribute image may be:

[0034] The first step is to obtain the original seismic coherence attribute image, such as Figure 3As shown; the method for acquiring the seismic coherent attribute image can adopt conventional methods in the field, and is not specifically limited to this.

[0035] The second step is to perform a two-dimensional log-gabor transform on the image to obtain multi-scale and multi-directional image components I ij ; Where i represents the scale, j represents the orientation, i=1…,N1, j=1,…,N2.

[0036] The specific process is: using a two-dimensional log-gabor filter to extract the multi-scale (N1) and multi-directional (N2) components of the seismic coherent attribute image I; 12 Represents the seismic coherent attribute image component with a scale of 1 and an orientation of 2, I 35 Represents the seismic coherent attribute image component with a scale of 3 and an orientation of 5.

[0037] The third step is to extract the amplitude response of the minimum scale image component of each direction respectively; when the scale is 1, the amplitude response of the image component of each direction is extracted, i.e., I 1j Amplitude response.

[0038] The fourth step is to determine the initial noise image of the seismic coherence attribute image based on the amplitude response. Figure 4 As shown, the noise response characteristics in multiple directions are displayed.

[0039] In an exemplary embodiment, the noise probability density function for establishing the initial noise image is:

[0040]

[0041] In the above function, f(x) is the probability density, x is the amplitude response of the minimum-scale image component, a is the longitudinal scaling factor of the probability density curve, b is the horizontal scaling factor of the variable x, c is the steepness of the probability density curve, σ is the initial standard deviation, and a, b, c, and σ are all real numbers greater than 0.

[0042] In an exemplary embodiment, the calculation process of the initial standard deviation σ is:

[0043] Step 1. For any orientation j, multiple components I with scale 1 1j , determine the mean v of the region to which the component with the highest frequency belongs j ;

[0044] Step 2. According to the determined mean v j , determine the initial standard deviation σ of the image component at position j according to the standard variance formula;

[0045] Wherein, the standard deviation formula is:

[0046]

[0047] In the above variance formula, σ is the initial standard deviation of the image component at position j, and m is the scaling factor between adjacent scale filters.

[0048] In an exemplary embodiment, the mathematical expectation and variance corresponding to the noise probability density function are determined:

[0049] The mathematical expectation is:

[0050]

[0051] The variance is:

[0052]

[0053] Where EX is the mathematical expectation, DX is the variance, Γ(x) is the gamma function, and sign(x) is the sign function.

[0054] Since c is a real number greater than 0, sign(c) = 1. Therefore, (2) and (3) can be simplified as follows:

[0055] The simplified mathematical expectation is:

[0056]

[0057] Simplified variance:

[0058]

[0059] Among them, EX′ is the simplified mathematical expectation, and DX′ is the simplified variance.

[0060] When c = 2, we have If it holds, the corresponding mathematical expectation (4) and variance (5) become as follows:

[0061]

[0062]

[0063] Furthermore, when a=1, b=1, c=2,

[0064] have If it holds, the corresponding mathematical expectation (4) and variance (5) will become the Rayleigh distribution, that is:

[0065]

[0066]

[0067] It can be seen from this that the noise distribution of the coherent attribute slice image is related to the response law of conventional Gaussian noise, namely the Rayleigh distribution.

[0068] In an exemplary embodiment, the noise energy threshold is calculated using a noise threshold formula based on the simplified mathematical expectation and variance;

[0069] The noise threshold formula is:

[0070]

[0071] In the above formula, T is the noise energy threshold, and k is the scaling factor, which can be set according to the maximum possible amplitude of the compensation noise.

[0072] In an exemplary embodiment, the process of determining the initial energy of the seismic coherence attribute image is as follows:

[0073] Step 1. Extract the real and imaginary parts of all scale image components at each orientation, and sum them up to obtain the real energy and imaginary energy at that orientation;

[0074]

[0075] EI j is the real energy, OI j is the imaginary energy, “real” is the operator for extracting the real part, and “imag” is the operator for extracting the imaginary part.

[0076] Step 2. Determine corresponding specific gravity according to the real energy and the imaginary energy respectively;

[0077] PEI j =EI j / (EI j 2 +OI j 2 ) 1 / 2 POI j =OI j / (EI j 2 +OI j 2 ) 1 / 2

[0078] PEI j is the real energy EI j The proportion of POI j is the imaginary energy OI j proportion.

[0079] Step 3. Calculating the energy of each direction in the coherent attribute image according to the determined weight;

[0080] The energy of each direction in the coherent attribute image is:

[0081]

[0082] Among them, “||” takes the absolute value operator, E j Represents the energy of the coherent attribute image component at position j.

[0083] In an exemplary embodiment, obtaining a denoised seismic coherent attribute image according to the initial energy of the seismic coherent attribute image and the noise energy threshold includes:

[0084] Step 1. Subtract the noise energy threshold corresponding to each position from the energy of the coherent attribute image to obtain the denoising result of the position;

[0085] E′ j =E j -T j

[0086] E′ j is the denoising result of the azimuth component j, T j is the noise energy threshold corresponding to the j-position.

[0087] Step 2. Accumulate and sum the denoising results of all directions to obtain the denoised seismic coherence attribute image E;

[0088]

[0089] Where E is the denoised seismic coherence attribute image, j is the orientation of the image component, N2 is the total number of orientations, and E′ j is the denoising result of the orientation component j.

[0090] An embodiment of the present invention also provides a device for eliminating seismic coherent attribute image noise, the device comprising: a memory 200 and a processor 210; the memory is used to store a program for eliminating seismic coherent attribute image noise, and the processor is used to read and execute the program for eliminating seismic coherent attribute image noise, and perform any one of the methods described in the above embodiments.

[0091] An embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon. The data processing program is used by a processor to execute the method for eliminating seismic coherent attribute image noise according to any one of the above embodiments.

[0092] Example 1

[0093] This example implements a method to remove noise from seismic coherent attribute images. The implementation process is as follows:

[0094] Step 1: Extract the original seismic coherence attribute image, such as Figure 3 shown.

[0095] Step 2: Perform a two-dimensional log-gabor transform on the seismic coherence attribute image to obtain multi-scale (number is N1) and multi-orientation (number is N2) image results.

[0096] Step 3: For the result image of step 2, extract the amplitude response of the minimum scale transformation result according to the orientation (from 1 to N2), which is the noise image; Figure 4 The scale shown is 1, and the noise response characteristic diagrams at different directions.

[0097] Step 4: Use Rayleigh distribution to fit the noise image amplitude response in step 3. It is found that the shape of the noise image amplitude distribution is similar to Rayleigh distribution, but there are obvious differences, such as Figure 5 shown.

[0098] Step 5: Based on the fitting results of step 4, observe the differences in the actual distribution and construct a probability density distribution function.

[0099] Step 6: Perform a fitting experiment on the probability density function constructed in step 5. The experimental results show that the new function has a very high fitting accuracy for the noise distribution; Figure 6 shown.

[0100] Step 7: Integrate the probability density function constructed in step 5 and calculate its corresponding mathematical expectation and variance. After simplified analysis, it is believed that this probability density function is a generalized form of the classic formula of Rayleigh distribution and has good generalization properties.

[0101] Step 8: Construct a noise energy threshold function based on the mathematical expectation and variance derived in step 7.

[0102] Step 9: Use the seismic coherent image energy to subtract the noise energy automatic threshold to obtain the denoised fault feature image. Figure 7 The components of scale 1 and direction 1 of the seismic coherence attribute image are shown, as well as a comparison of the denoising results of the conventional method and the new method (the method for eliminating noise in seismic coherence attribute images in this application); Figure 8 The components of scale 1 and direction 4 of the seismic coherent attribute image after the implementation of the present invention, as well as the comparison of the denoising results of the conventional method and the new method (the method for eliminating noise in seismic coherent attribute images in this application); Figure 9 The seismic coherent attribute slice image after the implementation of the present invention, as well as the comparison of the denoising results of the conventional method and the new method (the method for eliminating seismic coherent attribute image noise in this application); Figure 9 By comparison, the denoising results obtained by the new method (the method of eliminating seismic coherent attribute image noise in this application) can more clearly display the fault system.

[0103] The method for eliminating noise in seismic coherent attribute images of the present application has the following technical effects:

[0104] First, research has revealed that data images used in seismic exploration are both related to and distinct from natural images. The same is true for the noise information they contain. Starting with Gaussian noise, the most common type found in natural images, and combining it with the response characteristics of coherent image noise, we summarize the distribution patterns of this type of noise and construct a noise probability density function for this initial noise image.

[0105] Second, based on the constructed noise probability density function, the relevant steps for denoising the seismic coherent attributes are determined to solve the blind spots in the understanding of the noise distribution law and elimination methods in seismic coherent attributes.

[0106] Third, by using the method for eliminating noise in seismic coherent attribute images in this embodiment, a better denoising imaging effect can be achieved.

[0107] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for eliminating noise in seismic coherent attribute images, characterized in that: The method comprises: Extracting an initial noise image of the seismic coherent attribute image and constructing a noise probability density function of the initial noise image; Determining the mathematical expectation and variance corresponding to the noise probability density function; Calculating a noise energy threshold using a noise threshold formula according to the mathematical expectation and the variance; Obtaining a denoised seismic coherent attribute image according to the initial energy of the seismic coherent attribute image and the noise energy threshold; in, The extracting of the initial noise image of the seismic coherent attribute image includes: Perform two-dimensional log-gabor transformation on the seismic coherence attribute image to obtain multi-scale and multi-directional image components I ij ; Where i represents scale, j represents orientation, i=1…,N1, j=1,…,N2; N1 is the total number of scales, N2 is the total number of orientations; For each orientation, the amplitude response of the minimum-scale image component of the orientation is extracted respectively; determining an initial noise image of the seismic coherence attribute image according to the amplitude response; Calculating the noise energy threshold using a noise threshold formula according to the mathematical expectation and the variance includes: Simplifying the mathematical expectation and the variance to obtain simplified mathematical expectation and variance; Calculating the noise energy threshold using a noise threshold formula according to the simplified mathematical expectation and variance; The noise threshold formula is: In the above formula, T is the noise energy threshold, EX′ is the simplified mathematical expectation, DX′ is the simplified variance, k is the scaling factor, a is the longitudinal scaling coefficient of the probability density curve, b is the horizontal scaling coefficient of the variable x, x is the amplitude response of the minimum scale image component; c is the steepness of the probability density curve, σ is the initial standard deviation, and Γ() is the gamma function.

2. The method for eliminating noise in seismic coherent attribute images according to claim 1, characterized in that: The noise probability density function of the initial noise image is: In the above function, f(x) is the probability density, x is the amplitude response of the minimum-scale image component, a is the longitudinal scaling factor of the probability density curve, b is the horizontal scaling factor of the variable x, c is the steepness of the probability density curve, σ is the initial standard deviation, and a, b, c, and σ are all real numbers greater than 0.

3. The method for eliminating noise in seismic coherent attribute images according to claim 2, characterized in that: The calculation process of the initial standard deviation σ is: For multiple components I with scale 1 and arbitrary orientation j 1j , determine the mean v of the region to which the component with the highest frequency belongs j ; According to the determined mean v j , determine the initial standard deviation σ of the image component at position j according to the standard variance formula; Wherein, the standard deviation formula is: In the above variance formula, σ is the initial standard deviation of the image component at position j, and m is the scaling factor between adjacent scale filters.

4. The method for eliminating noise in seismic coherent attribute images according to claim 3, characterized in that: The mathematical expectation is: The variance is: Among them, EX is the mathematical expectation, DX is the variance, Γ() is the gamma function, and sign() is the sign function.

5. The method for eliminating noise in seismic coherent attribute images according to claim 1, characterized in that: The simplified mathematical expectation is: The simplified variance is: Among them, EX′ is the simplified mathematical expectation, and DX′ is the simplified variance.

6. The method for eliminating noise in seismic coherent attribute images according to claim 1, characterized in that: The process of determining the initial energy of the seismic coherence attribute image is as follows: Extract the real and imaginary parts of all scale image components at each orientation, and sum them up to obtain the real energy and imaginary energy at that orientation; Determine corresponding specific gravity according to the real energy and the imaginary energy respectively; Calculating the energy of each direction in the coherent attribute image according to the determined weight; The energy of each direction in the coherent attribute image is: Among them, "||" takes the absolute value operator, PEI j =EI j / (EI j 2 +OI j 2 ) 1 / 2 POI j =OI j / (EI j 2 +OI j 2 ) 1 / 2 ; EI j is the real energy, OI j is the imaginary energy, "real" is the operator for extracting the real part, "imag" is the operator for extracting the imaginary part, PEI j is the real energy EI j The proportion of POI j is the imaginary energy OI j proportion.

7. The method for eliminating noise in seismic coherent attribute images according to claim 6, characterized in that: The step of obtaining a denoised seismic coherent attribute image based on the initial energy of the seismic coherent attribute image and the noise energy threshold comprises: The energy of each direction of the coherent attribute image minus the noise energy threshold corresponding to the direction to obtain a denoising result of the direction; The denoising results of all directions are accumulated and summed to obtain the denoised seismic coherence attribute image E; Where E is the denoised seismic coherence attribute image, j is the orientation of the image component, N2 is the total number of orientations, and E′ j is the denoising result of the image component at orientation j.

8. A method and apparatus for eliminating noise in seismic coherent attribute images, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for eliminating seismic coherent attribute image noise, and the processor is used to read and execute the program for eliminating seismic coherent attribute image noise, and execute the method described in any one of claims 1-7.

9. A computer-readable storage medium having a data processing program stored thereon, wherein a processor executes the method for eliminating noise in seismic coherent attribute images according to any one of claims 1 to 7.

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