Stable deconvolution method based on hybrid norm constraint and frequency band optimization

Abstract

The present invention belongs to the field of seismic data processing and relates to a stable deconvolution method based on hybrid norm constraint and frequency band optimization, aiming to solve the problems of existing technologies being easily affected by noise interference, insufficient data resolution and dynamic optimization under low signal-to-noise ratio and complex geological environments. The present invention comprises: obtaining a linearized frequency division operator based on the collected seismic data; establishing a stable deconvolution objective function with sparsity constraint and hybrid norm constraint based on the linearized frequency division operator; and ADMM The algorithm decomposes the objective function into multiple subproblems, alternately optimizing and updating each variable to find a near-optimal solution. Iterations terminate when a termination criterion is met, and the last variable obtained is used as the near-optimal solution, outputting the stable deconvolution result. The invention introduces a mixed-norm sparsity constraint and a linearized frequency division operator to effectively maintain data sparsity while precisely controlling the dominant frequency band, ensuring the authenticity and accuracy of the deconvolution results.

Description

Technical Field

[0001] The present invention belongs to the technical field of seismic data processing, and in particular relates to a stable deconvolution method, system and electronic equipment based on hybrid norm constraint and frequency band optimization. Background Art

[0002] In seismic exploration, the resolution of seismic data is a key metric for achieving detailed analysis of underground geological structures. It directly determines the accuracy of precise identification of underground structures and is crucial for oil and gas resource exploration, geological hazard assessment, and other tasks. Traditional deconvolution methods, as a key means of improving seismic data resolution, estimate the autocorrelation function of seismic data to restore the original pulse characteristics of reflected waves, thereby removing multiple reflections and signal distortion, effectively enhancing the resolution of seismic data to a certain extent.

[0003] However, in actual application scenarios, the existing traditional deconvolution methods have exposed many limitations. First, under low signal-to-noise ratio conditions, it is difficult for traditional deconvolution methods to effectively distinguish between noise and signals. Random noise can easily interfere with the deconvolution process, resulting in signal distortion, seriously affecting the accuracy of the deconvolution results, and thus reducing imaging accuracy. Secondly, for key reservoir geological features such as small-scale faults, fractures and cavities that are widely present in complex geological bodies, traditional methods are limited by fixed parameter models and cannot capture the weak phase and amplitude changes in the signal. Especially in low signal-to-noise ratio environments, subtle geological response features are easily submerged, resulting in missing details or blurred morphology in reservoir characterization. Third, the traditional linear deconvolution algorithm uses a static processing process with preset parameters. It lacks the ability to adaptively optimize the reflection coefficient according to the local characteristics of seismic data. It is difficult to adapt to the non-stationary seismic response generated by complex geological interfaces, and there are obvious imaging deviations when characterizing complex structures such as reverse faults and salt dome structures. Finally, the traditional deconvolution method has obvious defects in frequency band control. It only relies on L 1 or L 2. A single norm constraint cannot effectively limit the frequency band range of the deconvolution result, which can easily lead to excessive resolution improvement and infinite frequency band expansion, thereby generating artifacts such as high-frequency noise and false events.

[0004] In summary, current technology's processing bottlenecks in low signal-to-noise ratios and complex geological conditions have become a key factor restricting improvements in seismic exploration accuracy. In particular, significant deficiencies exist in core technical aspects such as recovering small-scale geological features, enhancing resolution, and avoiding artifacts. A new optimization method is urgently needed to overcome these technical challenges and further improve the accuracy and resolution of seismic data. Summary of the Invention

[0005] In order to solve the above-mentioned problems in the prior art, namely, to solve the problems that the prior art is susceptible to noise interference, has insufficient seismic data resolution, and has insufficient dynamic optimization capabilities under low signal-to-noise ratio and complex geological environments, the first aspect of the present invention proposes a stable deconvolution method based on hybrid norm constraint and frequency band optimization, which includes the following steps:

[0006] Acquire and preprocess seismic data, and obtain a linearized frequency division operator based on the preprocessed seismic data;

[0007] With the goal of minimizing the matching error, a stable deconvolution objective function with sparsity constraints and mixed norm constraints is established based on the linearized frequency division operator.

[0008] Based on the framework of the alternating gradient direction multiplier method, the stable deconvolution objective function is decomposed into several sub-problems, and each variable is updated through alternating optimization to obtain an approximate optimal solution;

[0009] The variables include reflection coefficients, auxiliary variables and linearized frequency division operators;

[0010] When the termination criterion is met, the iteration is terminated, and the variable obtained for the last time is used as the approximate optimal solution of the stable deconvolution objective function and output as a stable deconvolution result.

[0011] In some preferred embodiments, a linearized frequency division operator is obtained based on the seismic data by:

[0012] A 1. Perform Fourier transform on the preprocessed seismic data to obtain frequency domain signals;

[0013] A 2. Based on the center frequency and bandwidth, a zero-phase bandpass filter is defined, and the zero-phase bandpass filter is weighted to obtain a multi-bandpass filter;

[0014] A 3. Filtering the frequency domain signal based on the multi-bandpass filter, then obtaining a time domain signal through inverse Fourier transform and linearizing it to obtain a linearized frequency division operator.

[0015] In some preferred embodiments, with the goal of minimizing the matching error, a stable deconvolution objective function with sparsity constraints and mixed norm constraints is established based on a linearized frequency division operator, and the method is as follows:

[0016] ;

[0017] in, represents the matching error between the reflection coefficient and the observed data; Indicates the use of LThe sparsity of reflection coefficients under the 1 norm; for L 2-norm constraint; is the original post-stack seismic data, is the balance factor, which is used to adjust the weights of different constraints; is the weight of the sparsity constraint, is the auxiliary vector, is the seismic wavelet matrix, is the linearized frequency division operator; For variables r and x The objective function to be jointly optimized.

[0018] In some preferred embodiments, the method for obtaining an approximate optimal solution is as follows:

[0019] B 1. Initialize the parameters, set the initial value of the auxiliary vector to 0, and the initial value of the reflection coefficient vector to , define the regularization parameter and balance factor ;

[0020] B 2. Solve the reflection coefficient:

[0021] ;

[0022] in, k is the iteration index, For the k+ Reflection coefficient for 1 iteration, For the k The auxiliary vector for the iteration, For the k Iterative linearized frequency division operator; Seek to minimize the objective function r The optimal value solving function of ;

[0023] B 3. Based on the reflection coefficient, combined with the soft threshold method, determine the auxiliary vector:

[0024] ;

[0025] in, For the k+ Auxiliary vector for 1 iteration; Seek to minimize the objective function x The optimal value solving function of ;

[0026] B 4. Update the linearized frequency division operator according to the updated auxiliary vector;

[0027] ;

[0028] in, For the k+ 1-iteration linearized frequency division operator;

[0029] B 5. When the algorithm converges, the approximate optimal solution of the stable deconvolution objective function is obtained; otherwise, return to step B 2. Proceed to the next round of iteration.

[0030] A second aspect of the present invention proposes a stable deconvolution system based on hybrid norm constraint and frequency band optimization, the system comprising:

[0031] a data acquisition module configured to acquire seismic data and perform preprocessing;

[0032] a linearized frequency division operator generation module configured to obtain a linearized frequency division operator based on the preprocessed seismic data;

[0033] An objective function generation module is configured to minimize the matching error and establish a stable deconvolution objective function with sparsity constraints and mixed norm constraints based on a linearized frequency division operator;

[0034] ADMM The solution module is configured to decompose the stable deconvolution objective function into a plurality of subproblems based on the framework of the alternating gradient direction multiplier method, and to solve an approximate optimal solution by updating each variable through alternating optimization; the variables include a reflection coefficient, an auxiliary variable, and a linearized frequency division operator; when a termination criterion is met, the iteration is terminated, and the variables obtained for the last time are used as the approximate optimal solution of the stable deconvolution objective function and output as a stable deconvolution result.

[0035] A third aspect of the present invention provides an electronic device, comprising:

[0036] at least one processor; and

[0037] a memory communicatively connected to at least one of the processors; wherein,

[0038] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned stable deconvolution method based on mixed norm constraint and frequency band optimization.

[0039] Beneficial effects of the present invention:

[0040] Based on the mixed norm sparsity constraint and the linearized frequency division operator, combined with an iterative optimization mechanism, the deconvolution result output by the present invention not only retains the dominant frequency band characteristics of the seismic data, but also can finely restore the details of the geological structure, such as small and medium-scale fractures, cracks and holes, and other geological features, effectively improving the imaging accuracy and making the deconvolution result clearer and more accurate. It avoids the problems of traditional deconvolution methods such as the inability to accurately restore complex geological structures and inaccurate frequency band control. It is suitable for the fine identification of details such as small and medium-scale fractures in complex geological environments, and can effectively improve the accuracy of fracture identification and the reliability of reservoir prediction.

[0041] Based on the frequency band optimization operator, a weight formula is used to dynamically select high signal-to-noise ratio (SNR) frequency bands, suppressing the contribution of low SNR frequency bands. Furthermore, by combining the hybrid norm sparsity constraint with the linearized frequency division operator, the output deconvolution result can maintain the sparsity of the reflection coefficient while effectively controlling the frequency band range, avoiding the problem of excessive enhancement or distortion of frequency components. This ensures that the dominant frequency band range in the deconvolution process is consistent with the actual seismic data, avoids the structural and lithologic artifacts caused by excessive resolution improvement in traditional deconvolution methods, and ensures the authenticity and accuracy of the deconvolution results.

[0042] The linearized frequency division operator is integrated into the ADMM iterative framework as an optimization variable to achieve its dynamic update during the reflection coefficient estimation process, overcoming the limitations of traditional methods using fixed operators and making the optimization process more robust and adaptive. By optimizing the reflection coefficient and precisely controlling the frequency band range, the continuity and identifiability of the fault can be effectively enhanced, especially in low signal-to-noise ratio environments. An adaptive parameter adjustment mechanism is provided to dynamically adjust the regularization parameters according to the data characteristics of the work area, ensuring that ideal deconvolution effects can be achieved under different signal-to-noise ratio conditions, taking into account the needs of both high and low signal-to-noise ratio scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0044] Figure 1 It is a flow chart of the stable deconvolution method based on hybrid norm constraint and frequency band optimization of the present invention;

[0045] Figure 2 This is the original post-stack data map of the shallow layer in the work area in the embodiment of the present invention;

[0046] Figure 3 This is a graph of the shallow stable deconvolution processing data of the working area in an embodiment of the present invention;

[0047] Figure 4 This is the original post-stack data map of any line in the deep layer of the work area in the embodiment of the present invention;

[0048] Figure 5 This is a graph of stable deconvolution processing data of arbitrary lines in the deep layer of the working area in an embodiment of the present invention;

[0049] Figure 6 It is the original position of a layer in the working area in the embodiment of the present invention. RTM Data coherence body attribute graph;

[0050] Figure 7 It is a coherence volume attribute map of the stable deconvolution data of a certain layer in the work area in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0052] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0053] The present invention constructs an iterative objective function containing a mixed norm constraint, combines a linearized frequency division operator to dynamically balance the target dominant frequency band, solves the high-resolution reflection coefficient, and ultimately outputs seismic data with excellent amplitude preservation and a reasonable frequency band. The linearized frequency division operator is used to perform frequency division on the seismic data to accurately extract the target dominant frequency component. The mixed norm constraint is combined to optimize the sparsity of the reflection coefficient, so that the deconvolution result can maintain the sparsity and accurately match the dominant frequency band in the seismic data, thereby preventing the distortion or excessive enhancement of the frequency components during the deconvolution process.

[0054] A stable deconvolution method based on hybrid norm constraint and frequency band optimization of the present invention comprises the following steps:

[0055] S 1. Acquire seismic data and preprocess them, and obtain a linearized frequency division operator based on the preprocessed seismic data;

[0056] S 2. With the goal of minimizing the matching error, a stable deconvolution objective function with sparsity constraints and mixed norm constraints is established based on the linearized frequency division operator;

[0057] S 3. Based on the framework of the alternating gradient direction multiplier method, the stable deconvolution objective function is decomposed into several sub-problems, and each variable is updated by alternating optimization to obtain an approximate optimal solution;

[0058] The variables include reflection coefficients, auxiliary variables and linearized frequency division operators;

[0059] S 4. When the termination criterion is met, the iteration is terminated, and the variable obtained for the last time is used as the approximate optimal solution of the stable deconvolution objective function and output as the stable deconvolution result.

[0060] In order to more clearly illustrate the stable deconvolution method based on hybrid norm constraint and frequency band optimization of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0061] The stable deconvolution method based on hybrid norm constraint and frequency band optimization of the first embodiment of the present invention comprises the following steps: S 1~Step S 4. Each step is described in detail as follows:

[0062] S 1. Obtain seismic data and perform preprocessing, obtain original post-stack seismic data based on the original seismic data, and obtain a linearized frequency division operator based on the original post-stack seismic data, the method is as follows:

[0063] A 1. Performing Fourier transform on the original post-stack seismic data to obtain a frequency domain signal;

[0064] A 2. Based on the center frequency and bandwidth, a zero-phase bandpass filter is defined, and the zero-phase bandpass filter is weighted to obtain a multi-bandpass filter;

[0065] A 3. Filtering the frequency domain signal based on the multi-bandpass filter, then obtaining a time domain signal through inverse Fourier transform and linearizing it to obtain a linearized frequency division operator.

[0066] Preferably, a multi-bandpass filter is obtained by:

[0067] Define a zero-phase bandpass filter :

[0068] ;

[0069] in, is the center frequency, is the bandwidth, is the frequency variable of the frequency band; extract the dominant frequency band from the original data and adjust the center frequency according to the spectrum characteristics of the actual data and bandwidth ;

[0070] According to the conformity of different frequency bands to geological structures, the dominant frequency bands are selected and weighted superposition is performed:

[0071] ;

[0072] in, For each frequency band, the corresponding zero-phase bandpass filter is is the number of frequency bands; The weight corresponding to each frequency band.

[0073] More preferably, the weight Dynamic adjustment is made based on the frequency band's conformity with the target geological structure. The method is as follows:

[0074] ;

[0075] in, For the Adaptive weights for each frequency band, It is The signal-to-noise ratio of the frequency band, It is The correlation coefficient between the frequency band and the target geological structure, For the corresponding .

[0076] Further preferably, the linearized frequency division operator is specifically:

[0077] ;

[0078] in, for Tukey The representation of the windowing function in the frequency domain reduces the spectrum leakage and oscillation caused by truncation of the frequency domain signal, namely the Gibbs effect; is the Fourier transform of the original seismic data, is the inverse Fourier transform.

[0079] In this application, the linearized frequency division operator preserves the linear characteristics of the target frequency in the seismic recording, avoiding phase distortion. By optimizing data from different frequency bands and analyzing their conformity with the target geological structure, the filter operator is ultimately determined to obtain the dominant frequency component. By introducing the linearized frequency division operator, not only is the frequency range of the deconvolution process controlled, but the reflection coefficient is also ensured to be sparse within the target frequency band. In this way, during the deconvolution process, artifacts caused by excessive enhancement of frequency components are avoided, and the deconvolution results are ensured to not deviate from the actual seismic data frequency band.

[0080] S2. With the goal of minimizing the matching error, a stable deconvolution objective function with sparsity constraints and mixed norm constraints is established based on the linearized frequency division operator:

[0081] ;

[0082] in, represents the matching error between the reflection coefficient and the observed data; Indicates the use of L The sparsity of reflection coefficients under the 1 norm; for L 2-norm constraint; is the original post-stack seismic data, is the balance factor, which is used to adjust the weights of different constraints; is the weight of the sparsity constraint, is the auxiliary vector, is the seismic wavelet matrix, is the linearized frequency division operator; is the objective function for joint optimization of variables r and x.

[0083] The optimization objective of this objective function consists of three parts: the first Represents the matching error between the reflection coefficient and the observed data. By minimizing this term, the characteristics of the real seismic data can be retained to the maximum extent. The second term use L 1 norm to maintain the sparsity of the reflection coefficient, ensuring that only the components that contribute significantly to seismic reflection are retained during the optimization process, while eliminating noise and non-dominant frequency band interference; the third L 2-norm constraint The relationship between the auxiliary vector and the linearized frequency division operator is constrained by the balance term so that the reflection coefficient and the divided signal match within the dominant frequency band, thereby maintaining the stability and consistency of the solution during the iterative process.

[0084] By introducing the reflection coefficient into the deconvolution process L 1 and L 2 mixed norm constraints, using L The 1-norm maintains the sparsity of the deconvolution results, thereby reducing random errors and improving resolution. Combined with actual data conditions, the dominant frequency band in the seismic data is optimized, and the L2-norm is used to improve the consistency between the deconvolution results and the dominant frequency band data to ensure fidelity. Through these two constraints, the resolution of seismic data can be improved while avoiding structural and lithologic artifacts caused by excessive resolution improvement, thereby ensuring the resolution and accuracy of the deconvolution results.

[0085] S3. Based on the framework of the alternating gradient direction multiplier method, the stable deconvolution objective function is decomposed into several sub-problems, and each variable is updated by alternating optimization to obtain an approximate optimal solution;

[0086] The variables include reflection coefficients, auxiliary variables and linearized frequency division operators.

[0087] Preferably, the method for finding the approximate optimal solution is:

[0088] B 1. Initialize the parameters and set the initial value of the auxiliary vector , initial value of reflection coefficient vector , define the regularization parameter and balance factor ;

[0089] B 2. Solve the reflection coefficient , update the reflection coefficient to :

[0090] ;

[0091] in, k is the iteration index, For the k+ Reflection coefficient for 1 iteration, For the k The auxiliary vector for the iteration, For the k Iterative linearized frequency division operator; Seek to minimize the objective function r The optimal value solving function of ;

[0092] B 3. Based on the reflection coefficient, combined with the soft threshold method, the auxiliary vector is updated as :

[0093] Fixed reflection coefficient vector , using the soft threshold method to solve L 1-norm problem:

[0094] ;

[0095] in, For the k+ Auxiliary vector for 1 iteration; Seek to minimize the objective function x The optimal value solving function of ;

[0096] B 4. Update the linearized frequency division operator according to the updated auxiliary vector;

[0097] The linearized frequency division operator is explicitly updated as To balance the constraint deviation:

[0098] ;

[0099] in, For the k+ 1 iteration of the linearized frequency division operator to adjust the balance of constraints to further optimize the estimation of the auxiliary vector;

[0100] B 5. When the algorithm converges, the approximate optimal solution of the stable deconvolution objective function is obtained; otherwise, return to step B 2. Proceed to the next round of iteration.

[0101] Preferably, in this embodiment, when solving the linear optimization problem of the reflection coefficient, the conjugate gradient method is used to accelerate the solution process.

[0102] Preferably, in this embodiment, using L 1-norm on auxiliary vector Thinning is performed to ensure that only important geological features are retained. In this process, Controls sparsity, with a typical value of 0.1~1.0, and the soft threshold is , the value is set to 1:3~1:5, which controls the sparsity of the reflection coefficient. The higher the threshold, the fewer reflection coefficients are retained and the stronger the sparsity.

[0103] The hybrid norm of the present invention avoids overfitting or band distortion caused by a single constraint by means of collaborative sparsity and band matching constraints; while the linearized frequency division operator accurately extracts the dominant frequency components that match the target geological structure by dynamically optimizing multi-band weights and windowing filtering, and constrains the deconvolution process to perform sparseness only within the effective frequency band. The combination of the two enables the present invention to suppress noise interference while avoiding the high-frequency oscillation or phase distortion problems of traditional methods, thereby achieving fault boundary sharpening and thin-layer resolution improvement under low signal-to-noise ratio conditions.

[0104] S 4. When the termination criterion is met, the iteration is terminated, and the variable obtained for the last time is used as the approximate optimal solution of the stable deconvolution objective function and output as the stable deconvolution result.

[0105] Preferably, when the algorithm converges, the termination criterion is satisfied:

[0106] ;

[0107] in, k is the iteration index, For the k+Reflection coefficient of 1 iteration, For the k The reflection coefficient of the iteration, tol is the preset threshold.

[0108] Preferably, in this embodiment, the preset threshold tol Take 10 -5 , low signal-to-noise ratio data can be relaxed to 10 -4 To speed up convergence.

[0109] By continuously iteratively optimizing the reflection coefficient and linearizing the frequency division operator, not only the frequency range in the deconvolution process is controlled, but also the reflection coefficient is ensured to be sparse within the target dominant frequency range; multiple reflections and distortion components are gradually removed, making the seismic data clearer after each round of iteration. In the deconvolution process, it not only avoids structural and lithologic artifacts caused by excessive enhancement of frequency components, but also ensures that the deconvolution results do not deviate from the actual seismic data frequency band; especially in a low signal-to-noise ratio environment, the present invention can better restore the details of small-scale faults and complex geological bodies, effectively reduce noise interference, and improve imaging accuracy. In general, the present invention improves the quality and availability of seismic data by optimizing the sparsity of the reflection coefficient, especially under low signal-to-noise ratio conditions, showing its unique advantages and providing a more reliable technical guarantee for seismic exploration and underground structure identification.

[0110] Preferably, in this embodiment, a stable deconvolution method based on hybrid norm constraint and frequency band optimization is used to RTM The effectiveness of the present invention is tested using post-stack data.

[0111] like Figure 2 As shown in the figure, a post-stack profile of a line in the shallow area of ​​the work area is shown. The data contains 331 seismic traces, 1200 time sampling points, and a sampling interval of 1 millisecond. The parameters are set as follows: μ = 0.3 、 λ = 1.2, and the soft threshold is 0.25. In terms of frequency band selection, four main frequency bands are selected based on the actual spectrum characteristics analysis, and adaptively weighted according to their contribution to the seismic data: low frequency band (0-10 Hz ) retains macroscopic geological information; medium and low frequency bands (11-30 Hz ) reflects the response of mesoscale geological bodies, balancing resolution and stability; medium and high frequency bands (31-45 Hz ) usually contains small-scale fracture details, but may be accompanied by higher noise, so the weighting algorithm dynamically adjusts its weight according to the contribution of the frequency band and the noise level to retain the effective signal and suppress the noise; the high frequency band (46-60 Hz ) is mainly high-frequency noise, so the algorithm will give it a low weight to improve the signal-to-noise ratio. After processing by this method, Figure 3As shown, the fault features (indicated by arrows) become clearer, and the continuity and recognition of the phase axis are better than the original RTM The post-stack data has been significantly improved.

[0112] like Figure 4 As shown in the figure, a stable deconvolution process is performed on the post-stack profile of an arbitrary line in the deep layer of the work area. The data contains 291 seismic traces, 1200 time sampling points, and the sampling interval is 1 millisecond. The processing results are shown in the figure. Figure 5 As shown, the fault features (indicated by arrows) are easier to distinguish, and the continuity and structural details of the phase axis are better than those of the original RTM The data has been improved. This improvement not only increases the resolution of seismic imaging sections, but also further verifies the effectiveness of the algorithm in improving data quality, especially in enhancing fault resolution and detail presentation, providing a more reliable basis for geological analysis.

[0113] Perform seismic attribute analysis on the data before and after processing. Figure 6 As shown, this is the original RTM The coherence properties of the data, Figure 7 The data shows the properties of the coherence volume processed by the present method. A comparison clearly shows that the processed data makes the overall structure of the fault zone clearer, the continuity and distribution of the faults more prominent, and the representation of small-scale faults and local structures is significantly enhanced. Compared with unprocessed data, the processed data has a higher resolution, which enables more accurate identification and analysis of complex geological volumes, providing more effective support for geological exploration.

[0114] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0115] A second embodiment of the present invention provides a stable deconvolution system based on hybrid norm constraint and frequency band optimization, the system comprising:

[0116] a data acquisition module configured to acquire seismic data and perform preprocessing;

[0117] a linearized frequency division operator generation module configured to obtain a linearized frequency division operator based on the preprocessed seismic data;

[0118] An objective function generation module is configured to minimize the matching error and establish a stable deconvolution objective function with sparsity constraints and mixed norm constraints based on a linearized frequency division operator;

[0119] ADMM The solution module is configured to decompose the stable deconvolution objective function into a plurality of subproblems based on the framework of the alternating gradient direction multiplier method, and to solve an approximate optimal solution by updating each variable through alternating optimization; the variables include a reflection coefficient, an auxiliary variable, and a linearized frequency division operator; when a termination criterion is met, the iteration is terminated, and the variables obtained for the last time are used as the approximate optimal solution of the stable deconvolution objective function and output as a stable deconvolution result.

[0120] It should be noted that the stable deconvolution system based on hybrid norm constraint and frequency band optimization provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps and are not regarded as improper limitations of the present invention.

[0121] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0122] An electronic device according to a third embodiment of the present invention includes:

[0123] at least one processor; and

[0124] a memory communicatively connected to at least one of the processors; wherein,

[0125] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned stable deconvolution method based on mixed norm constraint and frequency band optimization.

[0126] A fourth embodiment of the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to be executed by a computer to implement the above-mentioned stable deconvolution method based on mixed norm constraint and frequency band optimization.

[0127] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes and related instructions of the electronic device and computer-readable storage medium described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0128] Those skilled in the art should be aware that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM). RAM ), memory, read-only memory ( ROM ), electrically programmable ROM , electrically erasable and programmable ROM , registers, hard disks, removable disks, CD - ROM , or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the above description has generally described the components and steps of each example according to function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0129] Computer program code for carrying out operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java 、 Smalltalk 、 C ++, but also conventional procedural programming languages—such as C " language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the server via any type of network, including a local area network (LAN). LAN ) or WAN ( WAN )—connects to a user's computer, or, alternatively, can connect to an external computer (e.g., via the Internet using an Internet service provider).

[0130] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0131] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or indicate a particular order or sequence.

[0132] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0133] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A stable deconvolution method based on hybrid norm constraint and frequency band optimization, characterized in that: The method comprises the following steps: Acquire and preprocess seismic data, and obtain a linearized frequency division operator based on the preprocessed seismic data; With the goal of minimizing the matching error, a stable deconvolution objective function with sparsity constraints and mixed norm constraints is established based on the linearized frequency division operator: ； in, represents the matching error between the reflection coefficient and the observed data; represents the sparsity constraint of the reflection coefficient under the L1 norm; is the L2 norm constraint; is the original post-stack seismic data, is the balance factor, which is used to adjust the weights of different constraints; is the regularization parameter of the sparsity constraint, is the auxiliary vector, is the seismic wavelet matrix, is the linearized frequency division operator; is the objective function for the joint optimization of variables r and x, where r is the reflection coefficient to be calculated; Based on the framework of the alternating gradient direction multiplier method, the stable deconvolution objective function is decomposed into several sub-problems, and each variable is updated by alternating optimization to obtain an approximate optimal solution; the variables include reflection coefficients, auxiliary variables and linearized frequency division operators; When the termination criterion is met, the iteration is terminated, and the variable obtained for the last time is used as the approximate optimal solution of the stable deconvolution objective function and output as a stable deconvolution result.

2. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 1, characterized in that: A linearized frequency division operator is obtained based on the seismic data, and the method is as follows: A1. Perform Fourier transform on the pre-processed seismic data to obtain frequency domain signals; A2. defining a zero-phase bandpass filter based on the center frequency and the bandwidth, and weighting the zero-phase bandpass filter to obtain a multi-bandpass filter; A3. Filter the frequency domain signal based on the multi-bandpass filter, then obtain a time domain signal through inverse Fourier transform and linearize it to obtain a linearized frequency division operator.

3. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 2, characterized in that: Get a multi-bandpass filter as follows: Define a zero-phase bandpass filter : ； in, is the center frequency, is the bandwidth, is the frequency variable of the frequency band; According to the conformity of different frequency bands to geological structures, the dominant frequency bands are selected and weighted superposition is performed: ； in, For each frequency band, the corresponding zero-phase bandpass filter is is the number of frequency bands; The weight corresponding to each frequency band.

4. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 3, characterized in that: The weight Dynamic adjustment is made based on the frequency band's conformity with the target geological structure. The method is as follows: ； in, It is The signal-to-noise ratio of the zero-phase bandpass filter corresponding to the frequency band is is the signal-to-noise ratio of the zero-phase bandpass filter corresponding to the j-th frequency band, It is The correlation coefficient between the frequency band and the target geological structure, is the correlation coefficient between the jth frequency band and the target geological structure, For the The zero-phase bandpass filter corresponding to the frequency band frequency components.

5. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 3, characterized in that: The linearized frequency division operator is specifically: ； in, is the representation of Tukey window function in the frequency domain, is the Fourier transform of the original seismic data, is the inverse Fourier transform.

6. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 1, characterized in that: The method to find the approximate optimal solution is: B1. Initialize the parameters, set the initial value of the auxiliary vector to 0, and initialize the reflection coefficient to the original post-stack seismic data. , defines the regularization parameter of the sparsity constraint and balance factor ; B2. Solve the reflection coefficient: ； Among them, k is the iteration number index, is the reflection coefficient of the k+1th iteration, is the auxiliary vector for the kth iteration, is the linearized frequency division operator of the kth iteration; Find the optimal value of r to minimize the objective function; B3. Based on the reflection coefficient and in combination with the soft threshold method, determine the auxiliary vector: ； in, is the auxiliary vector for the k+1th iteration; Find the optimal value of x that minimizes the objective function; B4. Update the linearized frequency division operator according to the updated auxiliary vector; ； in, is the linearized frequency division operator of the k+1th iteration; B5. When the algorithm converges, the approximate optimal solution of the stable deconvolution objective function is obtained; otherwise, return to step B2 and perform the next round of iteration.

7. The stable deconvolution method based on hybrid norm constraint and frequency band optimization according to claim 6, characterized in that: When the algorithm converges, the termination criterion is met: ； Among them, k is the iteration number index, is the reflection coefficient of the k+1th iteration, is the reflection coefficient of the kth iteration, and tol is the preset threshold.

8. A stable deconvolution system based on hybrid norm constraint and frequency band optimization, characterized in that: The system comprises: a data acquisition module configured to acquire seismic data and perform preprocessing; a linearized frequency division operator generation module configured to obtain a linearized frequency division operator based on the preprocessed seismic data; The objective function generation module is configured to minimize the matching error and establish a stable deconvolution objective function with sparsity constraints and mixed norm constraints based on the linearized frequency division operator: ； in, represents the matching error between the reflection coefficient and the observed data; represents the sparsity constraint of the reflection coefficient under the L1 norm; is the L2 norm constraint; is the original post-stack seismic data, is the balance factor, which is used to adjust the weights of different constraints; is the regularization parameter of the sparsity constraint, is the auxiliary vector, is the seismic wavelet matrix, is the linearized frequency division operator; is the objective function for the joint optimization of variables r and x, where r is the reflection coefficient to be calculated; The ADMM solution module is configured to decompose the stable deconvolution objective function into several sub-problems based on the framework of the alternating gradient direction multiplier method, and to solve an approximate optimal solution by updating each variable through alternating optimization; the variables include reflection coefficients, auxiliary variables, and linearized frequency division operators; when the termination criterion is met, the iteration is terminated, and the variables obtained for the last time are used as the approximate optimal solution of the stable deconvolution objective function and output as the stable deconvolution result.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the stable deconvolution method based on mixed norm constraint and frequency band optimization according to any one of claims 1 to 7.

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