Seismic data interpolation and denoising integrated method and system

Abstract

The application discloses a seismic data interpolation denoising integrated method and system, and relates to the technical field of seismic data processing.The method comprises the following steps: firstly, obtaining noisy missing seismic data, which can be obtained by simulating a field acquisition scene or directly using actual field acquisition data; based on the compressed sensing theory, an interpolation denoising objective function is established, which comprises a data error term and a sparse promotion constraint term; based on the idea of shape regularization, an iterative solution framework is constructed, and a local signal-noise orthogonalization process is integrated, through which the useful signals leaked in the preliminary denoising can be reconstructed; after the iterative solution of the objective function, high-fidelity reconstructed seismic data are output.The application combines the local signal-noise orthogonalization and the shape regularization, effectively restores the missing seismic traces and suppresses the noise, realizes the protection and compensation of the useful signals, and significantly improves the signal-to-noise ratio and fidelity of the reconstructed data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, and particularly relates to a seismic data interpolation and denoising integrated method and system. BACKGROUND

[0002] Seismic exploration is a geophysical exploration method for exploring underground geological structure and resources by artificially exciting seismic waves and collecting reflected signals, and is widely used in oil and gas exploration, solid mineral exploration and engineering geological exploration. Seismic data processing and interpretation are based on seismic data acquisition, so the quality of the acquired data seriously affects the reliability and accuracy of seismic data processing and interpretation. However, due to factors such as terrain, acquisition bad channels, and acquisition cost, the seismic data collected in the field often faces the problem of irregular missing. In order to provide complete seismic data for subsequent processing and interpretation, it is necessary to reconstruct the missing data, which is called seismic data interpolation. In addition, the actually collected data is often contaminated by noise, which seriously affects the data quality. Therefore, it is necessary to interpolate and denoise the noisy missing data.

[0003] The prior art usually applies sparse constraints to seismic data in the transform domain (Fourier domain, curvelet domain, wavelet domain, etc.), and reconstructs the missing seismic traces by iterative threshold processing while suppressing noise. However, due to the inevitable factors such as poor parameter selection and insufficient denoising assumptions, the existing interpolation and denoising technology is prone to damage useful signals, resulting in reduced resolution and fidelity of seismic data, and then affecting the accuracy of seismic data processing and interpretation.

[0004] In view of the above problems, it is urgent to invent a seismic data interpolation and denoising integrated method that can effectively protect useful signals, so as to realize high-precision reconstruction of noisy missing seismic data and lay a good data foundation for subsequent seismic data processing and interpretation. SUMMARY

[0005] The purpose of the present application is to provide a seismic data interpolation and denoising integrated method to solve the problem that the prior art cannot fully protect useful signals, resulting in poor data reconstruction results, so as to provide high-quality acquisition data for seismic data processing and interpretation.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme:

[0007] In one aspect of the present application, a seismic data interpolation and denoising integrated method is provided, comprising the following steps:

[0008] S1, obtaining noisy missing seismic data, the noisy missing seismic data is obtained by simulating noise pollution and irregular missing processing on artificially synthesized seismic data, or is directly the seismic data collected in the field which contains noise and has missing seismic traces;

[0009] S2 Based on the compressed sensing theory, a seismic data interpolation denoising objective function under sparse promotion constraint is established using the noisy missing seismic data;

[0010] S3 Based on the idea of shape regularization, an iterative solution framework of the seismic data interpolation denoising objective function is constructed, and a local signal-noise orthogonalization process is integrated into the iterative solution framework;

[0011] S4 Based on the shape regularization framework containing the local signal-noise orthogonalization process, the seismic data interpolation denoising objective function is iteratively solved;

[0012] S5 The interpolated and denoised seismic data is output.

[0013] Further, the S2 based on the compressed sensing theory, using the noisy missing seismic data to establish a seismic data interpolation denoising objective function under sparse promotion constraint is:

[0014]

[0015] Wherein, b obs represents the collected noisy missing seismic data, represents the sampling matrix, b represents the noise-free complete seismic data expected to be reconstructed, represents the mathematical transformation of the tight frame, represents the weight parameter of the sparse promotion constraint term, represents the quadratic L 2 norm, represents the linear L 1 norm.

[0016] Further, the mathematical transformation of the tight frame in step S2 is curvelet transform, and the interpolation denoising objective function includes two terms, the first term is a data error term, and the second term is a sparse promotion constraint term.

[0017] Further, the iterative solution framework of the seismic data interpolation denoising objective function based on the idea of shape regularization is:

[0018]

[0019] Wherein, represents the curvelet transform, is the inverse curvelet transform, represents the soft threshold operator containing the threshold parameter , represents the sampling matrix, represents the sampling matrix, represents the reconstructed seismic data obtained in the th iteration.

[0020] Further, the local signal-noise orthogonalization process is integrated into an iterative solution framework of the seismic data interpolation denoising objective function based on the reshaping regularization idea:

[0021]

[0022]

[0023] wherein, represents the seismic data obtained after preliminary interpolation denoising by a traditional method, represents the Hadamard product of the matrix, W n represents the local signal-noise orthogonalization weight of the i-th iteration. n

[0024] Another aspect of the present application protects a seismic data interpolation denoising integrated system, which comprises:

[0025] a seismic data acquisition module, configured to acquire noisy missing seismic data, wherein the noisy missing seismic data is obtained by simulating noise pollution and irregular missing on artificially synthesized seismic data, or is directly the noisy seismic data with missing seismic traces acquired in the field;

[0026] a seismic data interpolation denoising objective function establishment module, configured to establish a seismic data interpolation denoising objective function under a sparse promotion constraint based on the compressed sensing theory and using the noisy missing seismic data;

[0027] an iterative solution framework construction module, configured to construct an iterative solution framework of the seismic data interpolation denoising objective function based on the reshaping regularization idea, and integrate the local signal-noise orthogonalization process into the iterative solution framework;

[0028] a solution module, configured to iteratively solve the seismic data interpolation denoising objective function based on the reshaping regularization framework containing the local signal-noise orthogonalization process;

[0029] an output module, configured to output the seismic data after interpolation denoising.

[0030] Another aspect of the present application protects a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method described above.

[0031] Technical effects of the present application

[0032] Compared with the prior art, the present application has the following advantages:

[0033] The present application has the following beneficial effects:

[0034] ​The application embeds a local signal-noise orthogonalization process into an iterative solving framework of a seismic data interpolation denoising objective function based on shape regularization, and proposes an integrated seismic data interpolation denoising method, which overcomes the defect that the existing interpolation denoising technology easily damages useful signals, can compensate for the damaged useful signals while reconstructing data and suppressing noise, so as to obtain complete seismic data with amplitude preservation and fidelity, and helps the subsequent seismic data processing and interpretation work.

[0035] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application.

[0036] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0038] Figure 1 A flow chart of an integrated seismic data interpolation denoising method according to an embodiment of the present application;

[0039] Fig. 2(a) is a schematic diagram of complete seismic data without noise according to an embodiment of the present application;

[0040] Fig. 2(b) is a schematic diagram of missing seismic data with noise according to an embodiment of the present application;

[0041] Fig. 3(a) is a schematic diagram of reconstructed seismic data by applying a traditional method according to an embodiment of the present application;

[0042] Fig. 3(b) is a schematic diagram of noise-removed data by applying a traditional method according to an embodiment of the present application;

[0043] Fig. 4(a) is a schematic diagram of reconstructed seismic data by applying an integrated interpolation denoising method according to an embodiment of the present application;

[0044] Fig. 4(b) is a schematic diagram of noise-removed data by applying an integrated interpolation denoising method according to an embodiment of the present application;

[0045] Figure 5 A comparison diagram of the signal-to-noise ratio of reconstructed seismic data changing with the number of iterations by applying two methods according to an embodiment of the present application;

[0046] Figure 6 A block diagram of an integrated seismic data interpolation denoising system according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0048] The above purposes, features and advantages of the present application will be more apparent and understandable, and the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0049] Embodiment one:

[0050] The technical key point of the present application is an integrated method for seismic data interpolation and denoising, and the main feature is to integrate the local signal-noise orthogonalization process into the iterative solution framework of the seismic data interpolation and denoising objective function based on the shaping regularization, so as to maximize the protection of useful signals from being damaged, and then realize high-precision and high-fidelity reconstruction of noisy missing seismic data.

[0051] Figure 1 For the integrated method for seismic data interpolation and denoising of the embodiments of the present application, the flowchart is shown in FIG. 2, and the specific steps are as follows for testing the interpolation and denoising effect of synthetic seismic data:

[0052] S1, data preparation: FIG. 2(a) is artificially synthesized noise-free complete seismic data, which has a total of 256 seismic traces, and each seismic trace contains 400 time sampling points with a sampling interval of 4 ms. It can be found by observation that the data is composed of many curved events with amplitude varying with space and time. In order to simulate the noise pollution and irregular missing phenomenon in the actual field acquisition data, Gaussian random noise is added to the noise-free complete seismic data and 50% of the seismic traces are randomly removed. The noisy missing seismic data with a signal-to-noise ratio of 2.45 shown in FIG. 2(b) can be obtained. It can be seen that the data is severely polluted by noise and the event missing phenomenon is prominent, which leads to insufficient resolution and spatial continuity, and it is necessary to attenuate the noise and reconstruct the interpolation to improve the data quality.

[0053] S2, establishing an objective function: based on the compressed sensing theory, the seismic data interpolation and denoising objective function under the sparse promotion constraint is established using the acquired incomplete seismic data:

[0054]

[0055] Wherein, b obs represents the acquired noisy missing seismic data, represents a sampling matrix, b represents the noise-free complete seismic data expected to be reconstructed, represents a mathematical transformation of a certain tight frame, in the present embodiment for curvelet transform, denotes the weight parameter of the sparsity-promoting constraint term, denotes the quadratic L norm, denotes the linear L norm, it is noted that the interpolation denoising objective function includes two terms, the first term is the data error term, and the second term is the sparsity-promoting constraint term;

[0056] S3, constructing an iterative solution framework: based on the idea of total variation regularization, an iterative solution framework of the seismic data interpolation denoising objective function is constructed:

[0057]

[0058] wherein, is the inverse curvelet transform, denotes a soft threshold operator containing a threshold parameter , which functions to gradually suppress noise while reconstructing data, denotes the reconstructed seismic data obtained in the th iteration, Fig. 3(a) is the reconstructed seismic data after the 50th iteration of directly applying the iterative solution framework, and the signal-to-noise ratio is 14.98, and Fig. 3(b) is the noise profile removed by the method, it can be found by observation that although this method can restore missing seismic traces and suppress noise to a certain extent (Fig. 3(a)), there is obvious useful signal leakage in the removed noise profile, as indicated by the arrows in Fig. 3(b), indicating that the conventional method is prone to damage useful signals in the interpolation denoising process, and cannot obtain high-fidelity reconstructed data.

[0059] The local signal-noise orthogonalization process is integrated into the iterative solution framework of the seismic data interpolation denoising objective function based on the idea of total variation regularization:

[0060]

[0061]

[0062] wherein, denotes the seismic data obtained after preliminary interpolation denoising by the conventional method, denotes the Hadamard product of matrices, W n denotes the local signal-noise orthogonalization weight of the n th iteration, which is applied to to reconstruct the useful signals leaked in the preliminary denoising profile, so that is composed of the data after preliminary interpolation denoising and the useful signals leaked in the preliminary denoising profile;

[0063] S4, iteratively solving: based on the shaping regularization framework containing the local signal-noise orthogonalization process, iteratively solving the seismic data interpolation denoising objective function, that is, firstly applying the traditional method to the noisy missing seismic data for interpolation reconstruction, then applying the local signal-noise orthogonalization weight to the seismic data preliminarily interpolated and denoised by the traditional method to obtain the useful signal leaked in the preliminary denoising profile, finally adding the reconstructed useful signal to the preliminary interpolation denoising profile to be regarded as the end of the first iteration, and repeating the above process until the preset maximum iteration number is reached, and the entire iterative solving process is completed;

[0064] S5, result output and analysis: output the interpolated and denoised seismic data, Fig. 4(a) is the reconstructed seismic data after the 50th iteration of the shaping regularization framework containing the local signal-noise orthogonalization process, and the signal-to-noise ratio is 17.60, it can be found that the reconstructed seismic data of the method is good in consistency with the noise-free complete seismic data (Fig. 2(a)), and no obvious signal amplitude leakage is found at the position indicated by the arrow in the noise profile (Fig. 4(b)), which shows that the seismic data interpolation denoising integrated method designed in the application can not only restore the missing seismic trace well, but also effectively compensate for the damage of the useful signal caused by noise suppression, thereby improving the fidelity of the reconstructed data.

[0065] Figure 5 The signal-to-noise ratio of the reconstructed seismic data of the interpolation denoising integrated method and the traditional method is shown in the variation curve comparison, and it can be seen from the comparison that under the same iteration number, the interpolation denoising integrated method containing the local signal-noise orthogonalization process can always produce reconstructed data with higher signal-to-noise ratio, which shows that the application has better data reconstruction and denoising ability than the traditional method.

[0066] Example two:

[0067] Figure 6 The seismic data interpolation denoising integrated system of the application is shown, which comprises:

[0068] The seismic data acquisition module is used for acquiring noisy missing seismic data, and the noisy missing seismic data is obtained by simulating noise pollution and irregular missing on artificially synthesized seismic data, or is directly the noisy seismic data with missing seismic traces collected in the field;

[0069] The seismic data interpolation denoising objective function establishment module is used for establishing the seismic data interpolation denoising objective function under the sparse promotion constraint based on the compressed sensing theory and using the noisy missing seismic data;

[0070] An iterative solution framework construction module is configured to construct an iterative solution framework of a seismic data interpolation denoising objective function based on a shaping regularization idea, and to integrate a local signal-noise orthogonalization process into the iterative solution framework.

[0071] A solution module is configured to iteratively solve a seismic data interpolation denoising objective function based on a shaping regularization framework containing a local signal-noise orthogonalization process.

[0072] An output module is configured to output the seismic data after interpolation denoising.

[0073] In another aspect of the present application, a computer readable storage medium is provided, which stores a computer program. The computer program, when executed by a processor, implements any of the above-described methods.

[0074] The present application is described in reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The devices that implement the functions specified in one block or multiple blocks.

[0075] The above-described specific embodiments further explain the purposes, technical solutions, and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely examples of the present application, and are not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A seismic data interpolation and denoising integrated method, characterized in that, Includes the following steps: S1 acquires noisy and missing seismic data; S2 Based on compressed sensing theory, a seismic data interpolation denoising objective function under sparsity promotion constraints is established using the aforementioned noisy and missing seismic data: ； in, This indicates the acquisition of noisy and missing seismic data. Represents the sampling matrix, This indicates the expectation of reconstructing noise-free, complete seismic data. Mathematical transformations representing tight frames, The weight parameters represent the sparsity promotion constraint terms. Indicates the second degree L 2-norm, Indicates one time L 1-norm; mathematical transformation of the tight frame For curvelet transform, the interpolation denoising objective function contains two terms: the first term is the data error term, and the second term is the sparsity promotion constraint term. S3. Based on the concept of shape regularization, an iterative solution framework for the objective function of seismic data interpolation and denoising is constructed, and the local signal-noise orthogonalization process is incorporated into the iterative solution framework. The iterative solution framework for the objective function of seismic data interpolation and denoising based on the concept of shape regularization is as follows: ； in, Represents the curvelet transform. For inverse curve transform, Indicates the inclusion of threshold parameters The soft threshold operator, Represents the sampling matrix, Indicates the first The reconstructed seismic data obtained from the next iteration; The framework for iteratively solving the objective function of seismic data interpolation and denoising, which incorporates the local signal-noise orthogonalization process, based on the idea of ​​shaping regularization, is as follows: ; ;in, This represents the seismic data obtained after preliminary interpolation and noise reduction using traditional methods. Denotes the Hadamard product of matrices. Indicates the first Local signal-noise orthogonalization weights in the next iteration; S4 is based on a shaping regularization framework that includes a local signal-noise orthogonalization process, and iteratively solves the objective function for seismic data interpolation and denoising. S5 outputs the interpolated and denoised seismic data.

2. The integrated seismic data interpolation and denoising method according to claim 1, characterized in that, The noisy and missing seismic data is obtained by simulating noise pollution and irregular missing data in artificially synthesized seismic data, or directly by noisy seismic data collected in the field with missing seismic traces.

3. The integrated seismic data interpolation and denoising method according to claim 1, characterized in that, S4 specifically includes: First, the noisy and missing seismic data is reconstructed by interpolation. Then, the local signal-noise orthogonalization weight is applied to the seismic data after preliminary interpolation and denoising using traditional methods to obtain the useful signal leaked in the preliminary denoised profile. Finally, the reconstructed useful signal is added to the preliminary interpolated and denoised profile, which is considered the end of the first iteration. The above process is repeated until the preset maximum number of iterations is reached to complete the entire iterative solution process.

4. A seismic data interpolation and denoising integrated system, characterized in that, The system is applied to the method according to any one of claims 1-3, comprising: The earthquake data acquisition module is used to acquire noisy and missing earthquake data; The seismic data interpolation and denoising objective function establishment module is used to establish a seismic data interpolation and denoising objective function under sparsity promotion constraints based on compressed sensing theory and the noisy and missing seismic data. An iterative solution framework construction module is used to construct an iterative solution framework for the objective function of seismic data interpolation and denoising based on the idea of ​​integer regularization, and to integrate the local signal-noise orthogonalization process into the iterative solution framework. The solver module is used to iteratively solve the seismic data interpolation and denoising objective function based on a shaping regularization framework that includes a local signal-noise orthogonalization process. The output module is used to output the interpolated and denoised seismic data.

5. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in claims 1-3.

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