Method and device for eliminating seismic coherent attribute image noise

Abstract

The invention discloses a method and device for eliminating noise of a seismic coherent attribute image, and the method comprises the steps: extracting an initial noise image of the seismic coherent attribute image, and constructing a noise probability density function of the initial noise image; determining mathematical expectation and variance corresponding to the noise probability density function; calculating a noise energy threshold by using a noise threshold formula according to the mathematical expectation and the variance; and obtaining a denoised seismic coherence attribute image according to the initial energy of the seismic coherence attribute image and the noise energy threshold.

Description

Technical Field

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

[0002] Fault detection helps to understand the basic oil and gas accumulation elements such as reservoir formation, oil and gas migration and accumulation, and preservation, and is an important part of oil and gas exploration. Conventional post-stack discontinuity attribute extraction is the main technology for fault detection, and the most representative one is the eigenvalue coherence technology (Gersztenkorn, 1999; Marfurt, 1999). This series of methods can indicate the more obvious characteristics of event axis dislocation, shape change, bifurcation, merging, and distortion caused by fault development, so it has been widely used, and in practice has produced a number of derivative technologies such as histogram enhancement, direction enhancement, and frequency enhancement. However, the data basis of this series of algorithms is the comparative relationship between the amplitudes of adjacent seismic traces. Practical experience tells us that when the quality of seismic imaging is high, the fault characteristics are clearly distinguishable in the coherent attributes; otherwise, the fault characteristics will be covered by "mushie" noise. This has limited impact on larger-scale main faults, but has a significant impact on more concerned hidden faults.

[0003] To address this phenomenon, predecessors have started from the source of seismic data and adopted methods such as structure-guided filtering and edge-preserving filtering to eliminate the random noise of the seismic data itself while strengthening 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 found that data images in the field of seismic exploration are both related to and different from natural images, and the same is true for the noise information contained in data images in the field of seismic exploration and the noise information contained in natural images. Starting from the most common Gaussian noise in natural images, combined with the response characteristics of coherent attribute image noise, the distribution law of this type of noise is summarized, laying a theoretical foundation for the subsequent elimination of coherent attribute noise.

[0006] The present application provides a method and device for eliminating noise in a seismic coherent attribute image. 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 a seismic coherent attribute image, and constructing a noise probability density function of the initial noise image;

[0009] Determine the mathematical expectation and variance corresponding to the noise probability density function; calculate 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 a second aspect, an embodiment of the present invention further provides a device for eliminating noise in seismic coherent attribute images, the device comprising: a memory and a processor; the memory is used to store a program for eliminating noise in seismic coherent attribute images, the processor is used to read and execute the program for eliminating noise in seismic coherent attribute images, and execute 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, on which a data processing program is stored, 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 with the related art, the present application provides a method and device for eliminating noise in seismic coherent attribute images, the method comprising: extracting an initial noise image of a 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; and obtaining a denoised seismic coherent attribute image according to 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, thereby obtaining a seismic coherent attribute image with noise eliminated.

[0014] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes 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 1A flow chart of a method for eliminating noise in seismic coherent attribute images according to an embodiment of the present application;

[0017] Figure 2 A schematic diagram of a device for eliminating noise in a seismic coherent attribute image 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 A schematic diagram of a histogram of a multi-scale filtering response of a seismic coherent attribute image and a Rayleigh distribution simulation result in some exemplary embodiments;

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

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

[0023] Figure 8 It is a schematic diagram comparing components of seismic coherent attribute images of scale 1 and direction 4 and denoising results in some exemplary embodiments;

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

[0025] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, 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] The present 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 the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached 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 of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence 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 the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences 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 of seismic coherent attribute images, such as Figure 1 As shown, the method includes steps S100-S130:

[0029] S100: extracting an initial noise image of a 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 coherent attribute image, such as Figure 3As shown; the method for acquiring the seismic coherent attribute image can adopt conventional methods in the art, and there is no specific limitation on 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 ; Wherein, 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 I represents the seismic coherent attribute image component with a scale of 1 and an orientation of 2. 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 orientation respectively; when the scale is 1, the amplitude response of the image component of each orientation is extracted, that is, I 1j Amplitude response.

[0038] Step 4: Determine the initial noise image of the seismic coherent attribute image according to 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 of the initial noise image is established as:

[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 multiple components I with scale 1 at any orientation j 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 variance 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] Among them, 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 to:

[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 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 according to the simplified mathematical expectation and variance;

[0069] Wherein, 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 of the 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 the 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. Calculate 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 comprises:

[0084] Step 1. Subtract the noise energy threshold corresponding to each position from the energy of each position 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 direction.

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

[0088]

[0089] Where E is the denoised seismic coherent 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 azimuth component j.

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

[0091] An embodiment of the present invention further provides a computer-readable storage medium, on which a data processing program is stored. 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.

[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 coherent attribute image, such as Figure 3 shown.

[0095] Step 2: Perform a two-dimensional log-gabor transform on the seismic coherent 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 direction (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 difference in 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 obtain its corresponding mathematical expectation and variance. After simplified analysis, it is believed that the probability density function is a generalized form of the classic formula of Rayleigh distribution and has good generalization characteristics.

[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 coherent attribute image are shown, as well as the comparison of the denoising results of the conventional denoising result and the denoising result of the new method (the method for eliminating the noise of the seismic coherent attribute image in the present 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 of the present application); Fig. 9 The seismic coherent attribute slice image after the implementation of the present invention, as well as the comparison of the conventional denoising results and the denoising results of the new method (the method for eliminating seismic coherent attribute image noise in the present application); Fig. 9 By comparison, the denoising result obtained by the new method (the method of eliminating seismic coherent attribute image noise in this application) can show the fault system more clearly.

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

[0104] First, through research, it is found that data images in the field of seismic exploration are both related and different from natural images. The same is true for the noise information they contain. Starting from the most common Gaussian noise in natural images, combined with the response characteristics of coherent attribute image noise, the distribution law of this type of noise is summarized and the noise probability density function of the initial noise image is constructed.

[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 the 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 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 tapes, 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 of ordinary skill in the art that communication media typically contain 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 a 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; A denoised seismic coherent attribute image is obtained according to the initial energy of the seismic coherent attribute image and the noise energy threshold.

2. The method for eliminating noise of seismic coherent attribute images according to claim 1, characterized in that: The extracting of the initial noise image of the seismic coherent attribute image comprises: Perform a two-dimensional log-gabor transform on the seismic coherent attribute image to obtain a multi-scale, multi-directional image component I ij ; Where i represents the scale, j represents the orientation, i=1…,N1, j=1,…,N2; For each orientation, respectively extract the amplitude response of the minimum scale image component of the orientation; An initial noise image of the seismic coherence attribute image is determined according to the amplitude response.

3. The method for eliminating noise of seismic coherent attribute images according to claim 2, 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.

4. The method for eliminating noise of seismic coherent attribute images according to claim 3, characterized in that: The calculation process of the initial standard deviation σ is: 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 ; 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 variance of the image component at position j, and m is the scaling factor between adjacent scale filters.

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

6. The method for eliminating noise of seismic coherent attribute images according to claim 5, characterized in that: The step of 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; Calculate the noise energy threshold using the noise threshold formula according to the simplified mathematical expectation and variance; Wherein, 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, and k is the scaling factor.

7. The method for eliminating noise of seismic coherent attribute images according to claim 6, 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.

8. The method for eliminating noise of seismic coherent attribute images according to claim 2, characterized in that: The process of determining the initial energy of the seismic coherent 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; The energy of each direction in the coherent attribute image is calculated according to the determined weight.

9. The method for eliminating noise of seismic coherent attribute images according to claim 8, characterized in that: The energy of each position 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.

10. The method for eliminating noise of seismic coherent attribute images according to claim 9, characterized in that: The step of obtaining a denoised seismic coherent attribute image according to the initial energy of the seismic coherent attribute image and the noise energy threshold comprises: The energy of each orientation of the coherent attribute image minus the noise energy threshold corresponding to the orientation to obtain a denoising result of the orientation; The denoising results of all directions are accumulated and summed to obtain the denoised seismic coherent attribute image E; Where E is the denoised seismic coherent 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 azimuth component j.

11. A method and device for eliminating noise of seismic coherent attribute images, characterized in that: The device comprises: a memory and a processor; the memory is used to store a program for eliminating noise of seismic coherent attribute images, and the processor is used to read and execute the program for eliminating noise of seismic coherent attribute images, and execute the method described in claims 1-10.

12. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the method for eliminating noise of seismic coherent attribute images as described in claims 1 to 10.

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