A method and system for sparse inversion ghost wavefield suppression based on encoding sources

Abstract

The present application relates to a kind of based on the sparse inversion ghost wave suppression method and system of coding source, comprising the following steps: the original seismic data containing ghost wave is encoded into hybrid phase wavelet, obtain encoding matrix;Utilize encoding matrix to establish target functional, for solving the inverse problem after joining sparse constraint regularization term, obtain the seismic data without ghost wave.The present application first encodes the original source wavelet into hybrid phase wavelet, then forms encoding matrix and joins Kjartansson model under constant Q to describe absorption attenuation, realizes from the perspective of coding to consider the relationship between ghost wave and primary wave, constructs more accurate ghost wave prediction, then in view of the sparsity of reflection coefficient, sparse constraint regularization term to model is added in solving inverse problem, to obtain more meaningful solution, improve the precision of solution.The present application can be widely applied in geophysical exploration technology field.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical exploration, in particular to a sparse inversion ghost wave suppression method and system based on a coded source. BACKGROUND

[0002] Multiple wave problem has been one of the most important problems in marine seismic exploration. In the process of marine seismic data acquisition, in order to avoid the influence of complex sea surface factors, the source and receiver need to be placed below the sea surface. Because the contact surface of sea water and air is a good reflection interface, three types of ghost wave fields will be formed, that is, source-end ghost wave, detection-end ghost wave and source-detection ghost wave field. Due to the abnormal development of free surface related multiple waves, the authenticity and reliability of seismic imaging will be seriously affected, thereby interfering with the interpretation of seismic data. Therefore, a reasonable ghost wave suppression method is needed to broaden the frequency band of seismic data and improve the resolution of seismic data.

[0003] At present, the mainstream method of ghost wave suppression is reflected in the acquisition and processing stages. In the acquisition stage, the distribution of trap wave points is controlled by designing the observation mode. Ozdemir et al. (2008) explored and developed the idea of suppressing ghost waves by up and down tow acquisition; Barr and Sanders (1989), Starr (1998), Carlson et al. (2007) and others proposed to place water detectors and land detectors on the streamer at the same time. In the processing stage, a better ghost wave prediction operator is proposed, and under the appropriate inversion framework, the primary wave field is estimated to realize the separation of ghost wave and primary wave. Such methods are mainly aimed at the data body of conventional horizontal cable acquisition, for example, Fokkema and Van den Berg (1993) first gave the frequency-wavenumber domain wave field extrapolation method to suppress ghost waves; Weglein et al. (1997) theoretically deduced the inverse scattering series method to suppress ghost waves in the full wave field domain; Wang and Peng (2012) further developed the Bootstrap method to optimize the water detector depth on the basis of the frequency-wavenumber domain wave field extrapolation, and Wang et al. (2013) extended it to the frequency-ray parameter domain. These ghost wave suppression methods for horizontal single cable acquisition can achieve certain effect in actual data processing.

[0004] Because the conventional horizontal single cable acquisition is widely used, the subsequent processing flow is mature, and because its acquisition cost is lower, in the foreseeable future, the conventional horizontal cable acquisition will still be important. This determines that the research on the ghost wave suppression method for conventional horizontal cable is still worthy of enough attention. Therefore, the research on the ghost wave suppression algorithm for the data acquired by the conventional horizontal cable still needs to be focused on. SUMMARY

[0005] In view of the above problems, the present application aims to provide a sparse inversion ghost wave suppression method and system based on an encoding source, which is based on an encoding and decoding theory framework, uses encoding to establish a source ghost wave prediction model, establishes decoding estimation of a primary reflected wave under a Bayes inversion framework, and adds a suitable regularization constraint, thereby solving the notch effect caused by the trailing effect of the source end ghost wave caused by the sea level in a high-resolution exploration problem and the problem of reduced imaging resolution caused by a fat seismic wavelet.

[0006] To achieve the above object, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a sparse inversion ghost wave suppression method based on an encoding source, comprising the following steps:

[0008] Encoding the original seismic data containing a ghost wave into a mixed phase wavelet to obtain an encoding matrix;

[0009] Using the encoding matrix to establish a target functional for solving the inverse problem after adding a sparse constraint regularization term to obtain ghost wave-free seismic data.

[0010] Further, the encoding of the original seismic data containing a ghost wave into a mixed phase wavelet to obtain an encoding matrix comprises:

[0011] Obtaining the original seismic data containing a ghost wave and extracting a seismic wavelet;

[0012] Processing the extracted seismic wavelet to form a phase-shifted primary wave and a source end ghost wave;

[0013] Encoding the primary wave and the source end ghost wave into a mixed phase wavelet to obtain an encoding matrix.

[0014] Further, when the primary wave and the source end ghost wave are encoded into a mixed phase wavelet, the size of the encoding matrix is set to a square matrix according to the length of one trace of the input seismic data.

[0015] Further, the encoding matrix is represented as:

[0016]

[0017] wherein w(t) is an extracted / simulated seismic wavelet, k z is a vertical plane wave number for phase shift, z s is a source depth, R s is a sea level reflection coefficient, w g (t) is a mixed phase wavelet.

[0018] Further, the encoding matrix further comprises an absorption attenuation matrix, and the absorption attenuation matrix adopts a Kjartansson matrix under a constant Q.

[0019] Further, the target functional is:

[0020] J(R) = ||w g (t) * R(t) - d(t) ||w + λ1||R(t)||1+ λ2||R(t)||2

[0021] Wherein, J(R) is an error functional defined for the reflection coefficient; R(t) is the underground reflection coefficient; d(t) is the input original seismic data volume; λ1, λ2 are weights for measuring data residual and model residual; w g (t) is the mixed phase wavelet after absorption attenuation.

[0022] In a second aspect, the present application provides a sparse inversion ghost wave suppression system based on an encoding source, comprising:

[0023] An encoding module, configured to encode original seismic data containing ghost waves into mixed phase wavelets to obtain an encoding matrix;

[0024] A ghost wave suppression module, configured to establish a target functional by using the encoding matrix, and to solve the inverse problem after adding a sparse constraint regularization term to obtain ghost wave-free seismic data.

[0025] Further, the encoding module comprises:

[0026] A seismic wavelet extraction module, configured to obtain original seismic data containing ghost waves and extract seismic wavelets;

[0027] A seismic wavelet processing module, configured to process the extracted seismic wavelets to form a first wave and a source-end ghost wave after phase shift;

[0028] A mixed phase wavelet encoding module, configured to encode the first wave and the source-end ghost wave into mixed phase wavelets to obtain the encoding matrix.

[0029] In a third aspect, the present application provides a computer readable storage medium storing one or more programs, the one or more programs including instructions which, when executed by a computing device, cause the computing device to perform any of the methods.

[0030] In a fourth aspect, the present application provides a computing device, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods.

[0031] The present application has the following advantages due to the above technical solutions:

[0032] 1、The present application considers the relationship between the ghost wave and the primary wave from the coding angle, and constructs a more accurate ghost wave prediction model, wherein the influence of absorption attenuation is added. The method for removing the ghost wave constructed under the Bayes framework can conveniently add the regularization method of the sparse constraint inversion type.

[0033] 2、The present application is based on the coding and decoding theory framework, and uses coding to establish a ghost wave prediction model, and the accuracy of the positive problem determines the effect of the inversion. Meanwhile, due to the application of a suitable regularization constraint, the accuracy of the inversion solution is improved, and the ghost wave suppression based on the model and the corresponding inversion theory is more thorough.

[0034] Therefore, the present application can be widely applied to the field of geophysical exploration technology. BRIEF DESCRIPTION OF DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the present application. Throughout the drawings, the same reference designations are used to represent the same elements. In the drawings:

[0036] Figure 1 A flowchart of the sparse inversion ghost wave suppression method based on the coding source in the embodiment of the present application is shown in the figure.

[0037] Figure 2 A schematic diagram of the coding mixed wavelet in the embodiment of the present application is shown in the figure.

[0038] Figures 3a-3d A schematic diagram of the synthetic data and the spectrum comparison before and after the suppression of the present application is shown in the figure, wherein, Figure 3a is the de-ghosted data; Figure 3b is the two-dimensional synthetic data; Figure 3c is the reflection coefficient inversion result; Figure 3d is the spectrum comparison before and after the de-ghosting;

[0039] Figures 4a-4d A schematic diagram of the two-dimensional ghost wave seismic data before and after the suppression of the present application is shown in the figure, wherein, Figure 4a is the actual two-dimensional pstm data; Figure 4b is the L2+L2 constraint de-ghosting result; Figure 4c is the L2+L2+L1 constraint de-ghosting result; Figure 4d is the spectrum analysis of Figure 4c . DETAILED DESCRIPTION

[0040] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the drawings of the embodiments of the present application to make a clear and complete description of the technical solutions of the embodiments of the present application. Obviously, the described embodiments are a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0041] It should be noted that the terms used herein are only intended to describe specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a presence of the features, steps, operations, devices, components and / or combinations thereof.

[0042] Some embodiments of the present application provide a sparse inversion ghost wave suppression method based on a coded source, which is mainly used for suppressing the ghost wave at the source end of the marine exploration seismic streamer data source. First, the original source wavelet is coded into a mixed phase wavelet, then a coding matrix is formed and a Kjartansson model under constant Q is added to describe the absorption attenuation, the relationship between the ghost wave and the primary wave is considered from the coding point of view, and a more accurate ghost wave prediction model is constructed. Then, aiming at the sparsity of the reflection coefficient, a sparse constraint regularization term of the model is added in the solution of the inverse problem, so that a more meaningful solution is obtained, and the accuracy of the solution is improved.

[0043] Correspondingly, in some other embodiments of the present application, a sparse inversion ghost wave suppression system, device and storage medium based on a coded source are provided.

[0044] Embodiment 1

[0045] As shown in Figure 1 The present embodiment provides a sparse inversion ghost wave suppression method based on a coded source, comprising the following steps:

[0046] Step 1: coding the original seismic data containing ghost waves into a mixed phase wavelet to obtain a coding matrix;

[0047] Step 2: using the coding matrix to solve the inverse problem after adding a sparse constraint regularization term to obtain ghost wave-free seismic data.

[0048] Preferably, in the above step 1, the following steps are included:

[0049] Step 1.1: obtaining the original seismic data containing ghost waves and extracting the seismic wavelet;

[0050] Step 1.2: Process the extracted seismic wavelet to form the phase-shifted primary wave and the source ghost wave;

[0051] Step 1.3: Encode the primary wave and the source ghost wave into a mixed phase wavelet to obtain the encoding matrix.

[0052] Furthermore, the method for extracting seismic wavelets from the original seismic data containing ghost waves in step 1.1 above is existing technology and will not be elaborated upon in this invention. In this embodiment, the default premise is that a relatively accurate seismic wavelet can be obtained, or a simulated far-field wavelet. If an accurate wavelet cannot be obtained, a Ricker wavelet can be used instead.

[0053] Furthermore, in step 1.2 above, for zero-offset or near-offset data, the ghost wave at the source travels twice the source depth compared to the primary wave because the source is below sea level, thus causing a time difference. Therefore, the time shift caused by the ghost wave at the source can be eliminated through encoding. Since marine data can provide relatively accurate seabed models and original excitation wavelets, the above scheme can be implemented. Moreover, the minimum offset of most marine towed cable data can reach within 100 meters, and in some high-precision exploration areas it can even reach tens of meters. Therefore, when using small-offset data instead of zero-offset data, the experimental error is only a few milliseconds, which does not affect the effectiveness of the algorithm.

[0054] Furthermore, in step 1.3 above, when encoding the primary wave and the source ghost wave into a mixed phase wavelet, the size of the encoding matrix should be set as a square matrix according to the length of one channel of the input seismic data.

[0055] Specifically, the encoding process for the hybrid phase wavelet can be described as follows:

[0056]

[0057] Where w(t) is the extracted / simulated seismic wavelet, k z The vertical plane wavenumber is used for phase shift, z s R is the focal depth. s The sea level reflection coefficient, w, can be used to describe higher-order source-end ghost waves according to the formula. g (t) represents the mixed-phase wavelet.

[0058] Furthermore, in step 3 above, an absorption attenuation matrix can be added to the encoding matrix. The absorption attenuation model applied in this invention is the Kjartansson model under constant Q, thereby achieving more accurate prediction of the mixed phase wavelet.

[0059] Furthermore, in step 4 above, the decoding problem can generally be defined as a parameter estimation problem within the Bayes framework. The introduction of the Bayes framework clearly explains the essence of the inverse problem, especially highlighting the importance of regularization ideas and methods in improving inversion accuracy. Here, we will not elaborate on Bayes estimation theory, but instead directly address the functional optimization problem. Specifically, we establish the following objective functional within the Bayes framework to solve the inverse problem with sparse constraints:

[0060] J(R) = ||w g (t)*R(t)-d(t)|| 2 +λ1||R(t)||1+λ2||R(t)||2 (2)

[0061] Where J(R) is the error functional defined for the reflection coefficient; R(t) is the subsurface reflection coefficient; d(t) is the input raw seismic data volume; λ1 and λ2 are the weights for measuring the data residuals and model residuals, and the norm applied to the model (i.e., the reflection coefficient) can be either L1 or L2, where L1 and L2 refer to the definition methods of the error functional, namely, sparsity constraint and energy minimum constraint, respectively; w g (t) represents the mixed phase wavelet after absorption and attenuation.

[0062] In solving the formula in the spatiotemporal domain, the wave field after ghost wave removal should exhibit sparsity, making sparse constraint inversion (L2+L1) of the model a suitable choice. At this point, formula (2) is an optimization problem of "L2 data matching term + L2 regularization term + L1 regularization term". The results of each iteration can be thresholded based on iterative weighting. The specific solution method is a well-known technique to those skilled in the art, and this invention will not elaborate on it.

[0063] Example 2

[0064] This embodiment uses two-dimensional cable-supported source-containing ghost wave seismic data from the South China Sea as an example to further describe the sparse inversion ghost wave suppression method based on encoded sources provided in Embodiment 1.

[0065] like Figures 3a-3d As shown, two-dimensional towed seismic data containing source ghost waves from the South China Sea were used as input, with a shot interval of 75m, a water detection interval of 12.5m, a single-channel recording length of 12.288s, and a sampling interval of 4ms. The towed cable was preset to a depth of 10m, and the shot depth was 7m. Here, only the suppression of source ghost waves is considered; detection ghost waves are not considered for the time being. In typical marine exploration, obtaining the source wavelet is relatively convenient, so subsequent processing of the source wavelet is feasible. The experimental results shown in this invention are all processed results using a 30Hz Ricker wavelet instead of the actual source wavelet.

[0066] Figure 2The text describes a source-end coding method. For zero-offset or near-offset earthquakes, the ghost wave travels twice the source depth compared to the primary wave because the source is below sea level, causing a time difference. Therefore, coding can eliminate the time shift caused by the ghost wave. Alternatively, the source can be shifted to sea level, and the paths of the primary wave and ghost wave can be increased and decreased respectively, thus creating a new hybrid excitation source.

[0067] Based on the designed hybrid wavelet, a coding matrix corresponding to the single-channel seismic data is constructed, which is a square matrix in this case. Since the water layer accounts for a large proportion of the test data, setting the Kjartansson model parameter q to 2000 under constant Q is reasonable, indicating that the absorption attenuation effect is not very significant.

[0068] like Figures 4a-4d As shown, the image is a comparison of the before and after effects and spectrum of two-dimensional seismic data containing ghost waves suppressed using the method of the present invention. It can be seen that the suppression effect of the present invention is better and has a certain improvement on low-frequency information.

[0069] Example 3

[0070] The above-described embodiment 1 provides a sparse inversion ghost wave suppression method based on a coding source. Correspondingly, this embodiment provides a sparse inversion ghost wave suppression system based on a coding source. The system provided in this embodiment can implement the sparse inversion ghost wave suppression method based on a coding source of embodiment 1. This system can be implemented through software, hardware, or a combination of both. For example, the system may include integrated or separate functional modules or units to execute the corresponding steps in the methods of embodiment 1. Since the system in this embodiment is basically similar to the method embodiment, the description process in this embodiment is relatively simple. Relevant details can be found in the description of embodiment 1. The system embodiment provided in this embodiment is merely illustrative.

[0071] The sparse inversion ghost wave suppression system based on coding source provided in this embodiment includes:

[0072] The encoding module is used to encode the raw seismic data containing ghost waves into a mixed phase wavelet to obtain an encoding matrix;

[0073] The ghost wave suppression module is used to establish an objective functional using the encoding matrix, and is used to solve the inverse problem after adding a sparse constraint regularization term to obtain seismic data without ghost waves.

[0074] Preferably, the encoding module includes:

[0075] The seismic wavelet extraction module is used to acquire raw seismic data containing ghost waves and extract seismic wavelets.

[0076] The seismic wavelet processing module is used to process the extracted seismic wavelets to form a phase-shifted primary wave and a source ghost wave.

[0077] The hybrid phase wavelet coding module is used to encode the primary wave and the source ghost wave into a hybrid phase wavelet to obtain the coding matrix.

[0078] Example 4

[0079] This embodiment provides a processing device corresponding to the sparse inversion ghost wave suppression method based on coding source provided in Embodiment 1. The processing device can be a client-side processing device, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Embodiment 1.

[0080] The processing device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the sparse inversion ghost wave suppression method based on the coded source provided in Embodiment 1.

[0081] In some embodiments, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0082] In other embodiments, the processor can be a general-purpose processor of various types, such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0083] Example 5

[0084] The sparse inversion ghost wave suppression method based on the coding source in Embodiment 1 can be specifically implemented as a computer program product. The computer program product may include a computer-readable storage medium on which computer-readable program instructions for executing the sparse inversion ghost wave suppression method based on the coding source described in Embodiment 1 are loaded.

[0085] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A sparse inversion ghost wave suppression method based on coding sources, characterized in that... Includes the following steps: The raw seismic data containing ghost waves is encoded into a mixed phase wavelet to obtain the encoding matrix; An objective functional is established using the encoding matrix to solve the inverse problem after adding a sparse constraint regularization term, thus obtaining seismic data without ghost waves. The process of encoding the raw seismic data containing ghost waves into a mixed phase wavelet to obtain an encoding matrix includes: Acquire raw seismic data containing ghost waves and extract seismic wavelets; The extracted seismic wavelets are processed to form a phase-shifted primary wave and a source ghost wave; The primary wave and the source ghost wave are encoded into a mixed phase wavelet to obtain the encoding matrix; The encoding matrix also includes an absorption attenuation matrix, and the absorption attenuation matrix adopts the Kjartansson matrix under constant Q.

2. The sparse inversion ghost wave suppression method based on coding source as described in claim 1, characterized in that, When encoding the primary wave and the source ghost wave into a mixed phase wavelet, the size of the encoding matrix should be set as a square matrix according to the length of one channel of the input seismic data.

3. The sparse inversion ghost wave suppression method based on coding source as described in claim 1, characterized in that, The encoding matrix is ​​represented as follows: in, To extract / simulate the seismic wavelet, The vertical plane wavenumber is used for phase shifting. The depth of the hypocenter. The sea level reflectance, The ellipsis represents the mixed phase wavelet after absorption and attenuation; the ellipsis indicates a higher-order source ghost wave.

4. The sparse inversion ghost wave suppression method based on coding source as described in claim 1, characterized in that, The objective functional is: in, The error functional is defined for the reflection coefficient; This refers to the underground reflectance coefficient. This is the input raw seismic data volume; , To measure the weights of data residuals and model residuals; It is a mixed phase wavelet after absorption and attenuation.

5. A sparse inversion ghost wave suppression system based on a coding source, characterized in that, include: The encoding module is used to encode the raw seismic data containing ghost waves into a mixed phase wavelet to obtain an encoding matrix; The ghost wave suppression module is used to establish the target functional using the encoding matrix, and is used to solve the inverse problem after adding the sparse constraint regularization term to obtain seismic data without ghost waves. The encoding module includes: The seismic wavelet extraction module is used to acquire raw seismic data containing ghost waves and extract seismic wavelets. The seismic wavelet processing module is used to process the extracted seismic wavelets to form a phase-shifted primary wave and a source ghost wave. The hybrid phase wavelet coding module is used to encode the primary wave and the source ghost wave into a hybrid phase wavelet to obtain the coding matrix; The encoding matrix also includes an absorption attenuation matrix, and the absorption attenuation matrix adopts the Kjartansson matrix under constant Q.

6. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described in claims 1 to 4.

7. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described in claims 1 to 4.

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