Seismic data adaptive denoising method and system based on quantum mechanics

Abstract

The invention discloses a seismic data adaptive denoising method and system based on quantum mechanics, and belongs to the field of seismic data denoising, and the method comprises the following steps: mapping seismic data into a quantum potential energy field based on the predictability of a seismic signal in a frequency-space domain, a self-adaptive quantum basis function is constructed by solving a characteristic value problem of a Schrodinger equation; performing sparse representation on the seismic data by adopting a quantum basis function to obtain an expansion coefficient of the signal on a quantum basis; performing noise suppression on the expansion coefficient by adopting an energy-based threshold processing mechanism to obtain a denoised coefficient; and reconstructing de-noised seismic data through quantum basis function linear combination by using the de-noised coefficient. The method can be seamlessly connected with an existing seismic data processing flow, is convenient for seismic data processing personnel to use, and improves the analysis precision of seismic data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seismic data denoising, and particularly relates to a seismic data adaptive denoising method and system based on quantum mechanics. BACKGROUND

[0002] Seismic data random noise suppression is a key processing step for improving interpretation and inversion accuracy and realizing efficient seismic exploration. Random noise is irregularly distributed on a seismic profile, covering effective signals, and needs to be suppressed by using a denoising algorithm. Random noise suppression algorithms for post-stack seismic data include a structure-oriented algorithm, an fx domain prediction filtering algorithm, a sparse transform domain noise attenuation method based on compressive sensing, a deep learning algorithm, and a non-local similarity algorithm introduced from image processing.

[0003] The above algorithms all have their own application conditions. For example, the structure-oriented algorithm can effectively preserve large dip angle data such as faults, so the algorithm is preferentially used in data processing with rich faults. The fx domain prediction filtering removes high wave number information according to the predictability of the same phase axis, so the fx domain prediction filtering is generally suitable for data with relatively flat strata. The sparse transform domain denoising method has different application data ranges according to the different types of transform domains. The method is based on the characteristics that effective signals are sparsely or densely distributed in the transform domain, while random noise is randomly distributed, and noise suppression is realized through threshold processing. Common transform methods include fk transform, wavelet transform, curvelet transform, and Radon transform. It should be noted that this kind of global transform algorithm may introduce new noise components in the transform domain when applied. The random noise suppression method based on deep learning relies on high-quality training data. Although transfer learning improves the generalization ability of the algorithm, the processing effect is still limited in areas with dramatic changes in structure. The non-local similarity denoising algorithm uses the self-similarity characteristics of seismic data to suppress noise by matching and fusing similar data blocks. However, due to the influence of the fractal characteristics of strata, the method may lose part of the effective signal details in the denoising process, so it is more suitable for strata with strong similarity or stable sedimentary characteristics.

[0004] Existing seismic data denoising techniques generally suffer from key limitations: while structure-guided algorithms are highly effective in protecting steeply dipping faults, they struggle to accurately identify and preserve minute geological structures; fx-domain predictive filtering methods perform well in simple stratigraphic structures but are less adaptable to complex wavefields and high-frequency noise; sparse transform-based techniques (such as wavelet and curvelet transforms) are constrained by the global transform assumption, potentially introducing artifacts and requiring strictly sparsity signals; deep learning methods are highly dependent on the quality of training data and are prone to overfitting or underfitting in areas with complex geological conditions; nonlocal similarity algorithms are ideal in homogeneous stratigraphy but can obscure subtle geological features; and traditional denoising algorithms (such as BM3D) effectively suppress noise but often at the expense of stratigraphic detail.

[0005] In summary, these technical bottlenecks severely restrict the realization of high-precision seismic interpretation, and there is an urgent need to develop innovative methods that can effectively suppress noise while accurately preserving complex small-scale geological features. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes an adaptive denoising method and system for seismic data based on quantum mechanics, thereby resolving the issues present in the prior art.

[0007] To achieve the above objectives, this invention provides an adaptive denoising method for seismic data based on quantum mechanics, comprising: Based on the predictability of seismic signals in the frequency-space domain, seismic data is mapped to a quantum potential field, and an adaptive quantum basis function is constructed by solving the eigenvalue problem of the Schrödinger equation. The quantum basis functions are used to perform sparse representation of seismic data to obtain the expansion coefficients of the signal on the quantum basis. An energy-based thresholding mechanism is used to suppress noise in the expansion coefficients to obtain denoised coefficients. The denoised seismic data is reconstructed by linear combination of quantum basis functions using the denoised coefficients.

[0008] Optionally, the expression for the predictability of seismic signals in the frequency-spatial domain is: ; In the formula, For the first Earthquake data at circular frequency Spectral values ​​at that location For the first Earthquake data at circular frequency Spectral values ​​at that location Represents angular frequency. Indicates the first The contribution weight of each neighboring path to the current path. For the neighboring Linear combination of seismic traces. Indicates the first One earthquake channel, Indicates the first An earthquake channel.

[0009] Alternatively, the expression for mapping seismic data to a quantum potential field is: ; In the formula, m is the mass of the particle. It is the reduced Planck constant. It is the Laplace operator, representing the second derivative in space, where E is the particle's energy. It is the wave function of the particle. For the field of power.

[0010] Optionally, the process of constructing adaptive quantum basis functions includes: Constructing a quantum potential energy field matrix based on seismic data; The quantum potential field matrix is ​​processed using the matrix eigenvalue decomposition method to obtain the corresponding eigenvalues ​​and eigenvectors; The adaptive quantum basis function is obtained based on the eigenvector.

[0011] Optionally, the expression for solving the adaptive quantum basis function is: ; In the formula, For eigenvalues, It is a wave function The vector form of H QAB The Hamitonian operator represents a quantum system.

[0012] Optionally, the denoised coefficients are: ; In the formula, It is a threshold function. The coefficients after thresholding. denoted as the expansion coefficient in the quantum basis.

[0013] Optionally, the reconstructed and denoised seismic data is as follows: ; In the formula, To reconstruct the denoised seismic data, It is the first The wave function of a particle. These are the coefficients after thresholding.

[0014] The application further provides a quantum-mechanical seismic data adaptive denoising system for implementing the method, and the system comprises: a quantum potential field mapping module configured to map the seismic data to a quantum potential field based on a corresponding relationship between the seismic signal amplitude and the quantum potential field; an adaptive quantum basis function construction module configured to process the quantum potential field by using a method for solving an eigenvalue problem of a Schrodinger equation to obtain an adaptive quantum basis function; a sparse representation module configured to perform sparse representation processing on the seismic data based on the adaptive quantum basis function to obtain expansion coefficients of the signal on the quantum basis; a noise suppression module configured to process the expansion coefficients by using an energy-based threshold processing mechanism to obtain denoised coefficients; a denoised data reconstruction module configured to reconstruct the denoised seismic data by using the denoised coefficients through a linear combination method of the quantum basis function to obtain final denoised seismic data.

[0015] The application further provides a computer comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the method when executing the computer program.

[0016] The application further provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the method.

[0017] Compared with the prior art, the application has the following advantages and technical effects: The application uses the adaptive quantum basis function to perform sparse representation on the signal, can accurately capture the complex local features in the seismic data, and especially the small geological structures. The energy-based threshold processing mechanism can intelligently distinguish the effective signal from the random noise, and realizes high-precision noise suppression. Compared with the traditional denoising algorithm, the method can better retain the detail information of the small-scale geological structures such as faults, thin layers, and river channels while effectively removing the random noise, significantly improves the signal-to-noise ratio and structural similarity of the denoised data, and provides a more reliable data basis for high-precision seismic interpretation and inversion. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the application illustrated in the drawings, and their description, are presented to add generic scope to this application. In the drawings: Figure 1 A quantum-mechanical fx-domain seismic data adaptive denoising flowchart of the embodiments of the application; Figure 2 A test signal of the embodiments of the application; Figure 3 adaptive quantum basis function for an embodiment of the present application; Figure 4 adaptive quantum basis function expansion coefficient for an embodiment of the present application; Figure 5 clean data and noisy data for participating in testing for an embodiment of the present application; Figure 6 non-sparse theoretical model test result for an embodiment of the present application; Figure 7 algorithm suppressed noise for an embodiment of the present application; Figure 8 local structure similarity for an embodiment of the present application. DETAILED DESCRIPTION

[0019] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0020] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0021] The present application aims to solve the problem that the conventional seismic data random noise suppression algorithm faces when processing seismic data containing small structures. The existing method often has difficulty in effectively preserving small-scale structural details in seismic data while removing noise, resulting in signal distortion or reduced resolution. For example, structure-oriented algorithms can protect large dip data, but they are not good at protecting small structural details; fx domain predictive filtering is suitable for flat layers, but it is not suitable for complex structures; deep learning methods rely on training data and are limited in areas with large structural differences; and non-local similarity algorithms may blur signal details due to excessive smoothing. Therefore, an fx domain seismic data random noise suppression algorithm based on the principles of quantum mechanics is proposed, which combines seismic signal potential energy distribution and quantum state representation by constructing an adaptive quantum basis function, achieving high-precision separation of noise and signal in the energy domain. This technology not only effectively suppresses random noise, but also completely preserves the details of complex small-scale structures, significantly improving the interpretation accuracy and reliability of seismic data, and is particularly suitable for exploration areas with complex geological structures.

[0022] The main work of the technology is as follows: 1. The amplitude of the seismic signal is mapped to the quantum potential field, and the adaptive quantum basis function is constructed by solving the eigenvalue problem of the Schrödinger equation; 2. A new fx-domain quantum mechanics denoising algorithm (fx-QABD) is developed, which uses quantum basis function to sparsely represent seismic data, and then uses an energy-based threshold processing mechanism to suppress random noise in the expansion coefficient on the quantum basis; 3. The superiority of the algorithm in processing seismic data containing complex small structures is verified through model testing, especially the ability to remove random noise while preserving small-scale structural details; 4. The universality and effectiveness of the algorithm are further tested using actual seismic data, proving its applicability and superiority under different geological conditions. The technology has good integration in the algorithm, can improve the analysis accuracy of seismic data, is convenient for seismic data processing personnel to use, and has high popularization value.

[0023] As shown in Figure 1 The embodiment provides a quantum mechanics-based adaptive seismic data denoising method, which includes the following steps: based on the predictability of seismic signals in the frequency-space domain, the seismic data is mapped to a quantum potential field, and an adaptive quantum basis function is constructed by solving the eigenvalue problem of the Schrödinger equation; the seismic data is sparsely represented using the quantum basis function to obtain the expansion coefficient of the signal on the quantum basis; an energy-based threshold processing mechanism is used to suppress noise in the expansion coefficient to obtain the denoised coefficient; and the denoised seismic data is reconstructed by linear combination of the denoised coefficient through the quantum basis function.

[0024] The adaptive fx-domain seismic data denoising flowchart of quantum mechanics is as shown in Figure 1 The patent includes four modules: the first is to map the amplitude of the seismic signal to the quantum potential field, and to construct the adaptive quantum basis function by solving the eigenvalue problem of the Schrödinger equation; the second is to develop a new fx-domain quantum mechanics denoising algorithm (fx-QABD), which uses quantum basis function to sparsely represent seismic data, and then uses an energy-based threshold processing mechanism to suppress random noise in the expansion coefficient on the quantum basis; the third is to verify the superiority of the algorithm in processing seismic data containing complex small structures, especially the ability to remove random noise while preserving small-scale structural details; and the fourth is to further test the universality and effectiveness of the algorithm using actual seismic data, proving its applicability and superiority under different geological conditions.

[0025] The predictability of seismic data in the fx domain indicates that the fx-domain prediction filter random noise suppression method is based on the predictability of linear events in the frequency domain, and all dip angle events are predicted simultaneously. Assuming that the seismic data s is a plane wave function about time t and spatial position x, the seismic data s(t,x) can be represented as N slants with a slope pi The linear superposition of tilted in-phase axes, i.e.: (1) in, Let be the Dirac function, representing the th A reflective interface in time An ideal pulse reflection (without width or distortion) is generated at the point. Represents two-dimensional linear convolution. It is the seismic wavelet associated with the i-th reflection phase axis, and located at time The Dirac function is convolved, and , It is a certain in-phase axis Intercept time at position, For the first Given the slope of the phase axis, performing a Fourier transform on equation (1) yields: (2) Represents angular frequency. The imaginary unit, It is a wavelet The Fourier transform, according to the auto-regressive model, is the fourth transform in the frequency domain. Earthquake Channel This can be represented as seismic traces at different spatial locations within a spatial window. Superposition: (3) In the formula, For the first Earthquake data at circular frequency Spectral values ​​at that location For the first Earthquake data at circular frequency Spectral values ​​at that location Represents angular frequency. Indicates the first The contribution weight of each neighboring path to the current path. For the neighboring Linear combination of seismic traces. Indicates the first One earthquake channel, Indicates the first An earthquake channel.

[0026] in, , This reflects the frequency ratio of wavelet amplitudes in two seismic data streams. Due to channel spacing, the effective signal wavenumber is low, and the noise wavenumber is high. Within the spatial window, when When the data is small, it is relevant; when When the noise level is high, any single data point cannot be represented by other data points, and is therefore considered to be high wavenumber noise or random noise.

[0027] Therefore, in the fx domain, noise attenuation can be achieved by reducing high wavenumber energy. Similarly, adaptive signal decomposition in the fx domain can also achieve noise attenuation.

[0028] Quantum mechanics and the construction of quantum basis functions: This algorithm utilizes the predictability of the region to separate signal from noise by constructing quantum basis functions. Its core idea is to adaptively decompose the signal. In the coefficient domain of the quantum basis function expansion, the effective signal exhibits sparse characteristics, while noise shows a non-sparse distribution. Random noise suppression is achieved based on this difference.

[0029] The process of constructing adaptive quantum basis functions includes: constructing a quantum potential energy field matrix based on seismic data; processing the quantum potential energy field matrix using the matrix eigenvalue decomposition method to obtain the corresponding eigenvalues ​​and eigenvectors; and obtaining the adaptive quantum basis functions based on the eigenvectors.

[0030] Quantum theory gives the probability of finding a quantum particle at a certain point. If a quantum particle with energy E probes this surface, then the probability of this quantum particle existing at a certain position on the surface will be given by the wave function. The dynamic behavior of particles in a potential field is determined. This can be described by the Schrödinger equation: (4) in, It is the mass of the particle. It is the reduced Planck constant. It is the Laplace operator, representing the second derivative in space. It is the energy of the particles. It is the wave function of the particle, the square of its absolute value. Indicates the position of the particle The probability density distribution. Equation (4) can be transformed into solving the eigenvalue problem of the matrix: (5) The Hamiltonian operator of a quantum system can be represented as ,in, For eigenvalues, It is a wave function The eigenfunctions of this operator have two important properties: first, they form an orthogonal set, i.e., the wave functions corresponding to different energy levels are orthogonal to each other; second, they form a complete set, such that any quantum state can be represented as a linear superposition of these eigenstates. The solution set gives a set of stationary solutions related to the eigenvalues (energies) .

[0031] (6) where represents the th component of the operator , and denote the matrix row and column indices, and denotes the potential value at the th grid point.

[0032] By solving the Schrödinger equation (4) or the eigenvalues of the matrix (5), a set of orthogonal wave functions can be obtained, which form an adaptive quantum basis, denoted as Quantum Adaptive Basis (QAB) in this algorithm. Each wave function is associated with a specific energy and has a different oscillation frequency, which depends on the local potential value of the signal or image, i.e., (7) where is the energy of the th eigenstate, then the signal can be represented as a linear combination of these basis functions: (8) where are the coefficients, and is the wave function of the th particle, which can be calculated by the inner product: (9) where denotes the th quantum basis function. In the spatial direction, noise mainly affects the high wave number components of the signal, and can be removed by thresholding the coefficients . The purpose of thresholding is to retain the expansion coefficients of the basis functions related to low energies , which correspond to the low wave number components of the signal, while removing the noise components related to high energies .

[0033] Thresholded coefficients may be expressed as (10) where is a threshold function, which determines whether to keep a coefficient or not according to the energy and hyper-parameters and , where denotes a smoothing parameter to control the slope of the threshold function, is a threshold parameter to determine how many largest coefficients to keep. The processed coefficients are used to reconstruct the denoised signal or image : (11) The spatial direction of Fourier transform of seismic data is similar to irregular harmonics, while random noise is still randomly distributed in the whole signal. In order to simulate this phenomenon, the embodiment constructs a similar signal model as shown in Figure 2 Based on equation (5), the quantum basis functions shown in Figure 3 are obtained, which can adaptively detect low potential energy regions at higher frequencies and high potential energy regions at lower frequencies. By applying these quantum basis functions to decompose the signal, the embodiment obtains the projection coefficients of the signal on the quantum adaptive basis as shown in Figure 4 In the projection, the effective signal shows the characteristics of sparse distribution, while the noise exists in the form of non-sparse. Based on the soft threshold, the random noise can be effectively suppressed to obtain the denoised signal. The soft threshold processing realizes the effective separation of signal and noise by selectively reducing the non-sparse noise component and keeping the sparse signal component.

[0034] As mentioned above, the characteristic vector (called wave vector in quantum physics) is an oscillation function, and the oscillation frequency of the function is usually proportional to the local value of The QAB has adaptivity, and the difference between it and the Fourier basis and the wavelet basis is that the basis vector detects the low potential energy region at a higher frequency and adaptively detects the high potential energy region at a lower frequency, and the dependence of the local frequency on the data value can be adjusted by the hyper-parameter . This means that compared with the fixed basis function, the quantum basis function has higher degrees of freedom, that is, higher adaptivity.

[0035] Meanwhile, the adaptive quantum basis functions are constructed based on the characteristics of the signal itself, which means that the basis functions can adapt to the local features of the signal, similar to dictionary learning, resulting in wave functions that are dynamically generated based on the specific content of the signal or image. This adaptability allows the basis functions to more accurately capture the characteristics of the signal while ignoring irrelevant noise components. The wave functions in quantum mechanics have localized characteristics, and their oscillation frequencies depend on the local potential energy of the signal. Therefore, these basis functions can use different frequency components in different regions of the signal, better representing the local features of the signal. Moreover, the high wave number components of the fxdomain signal (corresponding to high-energy quantum states) are often associated with noise, while the low wave number components are associated with the main information of the signal. By thresholding the coefficients in the energy domain, the high-frequency components corresponding to noise can be removed, while the low-frequency components of the signal are preserved.

[0036] In addition to the above discussion, the quantum localization phenomenon refers to the localization of wave functions in disordered potential energy, which can lead to excessive concentration of quantum basis functions in noisy signals or images. To solve this problem, adaptive basis functions can be obtained after pre-noise reduction to reduce the localization of basis functions.

[0037] Model test: To test the denoising effect of the algorithm, this embodiment synthesizes a model, which is a profile of the Stanfold geological model. The model data shows delta facies and river facies deposition from shallow to deep, and the profile contains many small-scale structures such as branch channels, as shown in Figure 5 The original data presents the characteristics of the anticline structure. This embodiment flattens the data based on the provided horizon information to facilitate testing the denoising ability of the algorithm. On this basis, this embodiment adds random noise to simulate the noise in real geological data. The signal-to-noise ratio of the noisy data is shown in Table 1. The denoising results of different algorithms are compared visually, as shown in Figure 6 and Figure 7As shown in the test results. Considering that the denoising effect of the deep learning method depends largely on the quality of the training data set, in order to ensure the fairness and objectivity of the comparison, the deep learning-based method is not included in the comparison range. Among the traditional denoising algorithms compared, the frequency-space domain smoothing filter (fx-smooth) suppresses the residual noise, the frequency-space domain prediction filter (fx-decon) loses part of the signal, and the three-dimensional block matching denoising algorithm (BM3D algorithm) has an advantage in maintaining the continuity of the same phase axis and spatial consistency, but its smoothing mechanism can have a significant impact on subtle geological structures. This method can suppress noise while reducing the clarity of small-scale geological features such as micro-faults and thin layers, resulting in a loss of detail resolution in the lateral direction. This characteristic makes this algorithm more suitable for geological scenes dominated by large structures and with low requirements for subtle structures. In contrast, the proposed frequency-space domain quantum adaptive basis denoising algorithm (fx-QABD algorithm) basically retains the response of small-scale structures such as branch channels during the denoising process, while effectively suppressing noise, and the numerical value of the local structure similarity is the largest, indicating that the similarity to the real clean data is better, as Figure 8 shown. This shows that the fx-QABD algorithm can provide good denoising effect while preserving geological details.

[0038] Table 1

[0039] Note: SNR: signal-to-noise ratio, the larger the value, the less the noise; PSNR: peak signal-to-noise ratio, the larger the value, the less the noise; SSIM: structural similarity, the larger the value, the closer to the clean signal.

[0040] The present application first applies the principles of quantum mechanics to the noise suppression of seismic data, uses quantum basis functions to sparsely represent signals, which is a completely new method; the present application constructs an adaptive quantum basis function that can automatically adjust according to the local characteristics of the seismic signal, which enables the algorithm to more accurately capture signal characteristics and suppress noise; the present application introduces an adaptive threshold mechanism to suppress non-sparse noise components, which can dynamically adjust according to the local potential energy value of the signal, improving the accuracy of denoising; the algorithm of the present application has good integration and can be seamlessly integrated with the existing seismic data processing flow, making it easy for seismic data processing personnel to use and improving the accuracy of seismic data analysis.

[0041] The application further provides a quantum-mechanics-based seismic data adaptive denoising system for implementing the method, and the system comprises a quantum potential field mapping module, a self-adaptive quantum basis function construction module, a sparse representation module, a noise suppression module and a denoised data reconstruction module.

[0042] The application further provides a computer comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method when executing the computer program.

[0043] The application further provides a storage medium having a computer program stored thereon, and the program is executable on a processor to implement the method.

[0044] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited to this, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the application, which should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. An adaptive denoising method for seismic data based on quantum mechanics, characterized in that, Includes the following steps: Based on the predictability of seismic signals in the frequency-space domain, seismic data is mapped to a quantum potential field, and an adaptive quantum basis function is constructed by solving the eigenvalue problem of the Schrödinger equation. The quantum basis functions are used to perform sparse representation of seismic data to obtain the expansion coefficients of the signal on the quantum basis. An energy-based thresholding mechanism is used to suppress noise in the expansion coefficients to obtain denoised coefficients. The denoised seismic data is reconstructed by linear combination of quantum basis functions using the denoised coefficients.

2. The adaptive denoising method for seismic data based on quantum mechanics according to claim 1, characterized in that, The expression for the predictability of seismic signals in the frequency-spatial domain is as follows: ； In the formula, For the first Earthquake data at circular frequency Spectral values ​​at that location For the first Earthquake data at circular frequency Spectral values ​​at that location Represents angular frequency. Indicates the first The contribution weight of each neighboring path to the current path. For the neighboring Linear combination of seismic traces. Indicates the first One earthquake channel, Indicates the first An earthquake channel.

3. The adaptive denoising method for seismic data based on quantum mechanics according to claim 1, characterized in that, The expression for mapping seismic data to a quantum potential field is: ； In the formula, m is the mass of the particle. It is the reduced Planck constant. It is the Laplace operator, representing the second derivative in space, where E is the particle's energy. It is the wave function of the particle. For the field of power.

4. The adaptive denoising method for seismic data based on quantum mechanics according to claim 1, characterized in that, The process of constructing adaptive quantum basis functions includes: Constructing a quantum potential energy field matrix based on seismic data; The quantum potential field matrix is ​​processed using the matrix eigenvalue decomposition method to obtain the corresponding eigenvalues ​​and eigenvectors; The adaptive quantum basis function is obtained based on the eigenvector.

5. The quantum mechanics-based adaptive denoising method for seismic data according to claim 4, characterized in that, The expression for solving the adaptive quantum basis function is: ； In the formula, For eigenvalues, It is a wave function The vector form of H QAB The Hamitonian operator represents a quantum system.

6. The adaptive denoising method for seismic data based on quantum mechanics according to claim 1, characterized in that, The denoised coefficients are: ； In the formula, It is a threshold function. These are the coefficients after thresholding. denoted as the expansion coefficient in the quantum basis.

7. The quantum mechanics-based adaptive denoising method for seismic data according to claim 6, characterized in that, The reconstructed and denoised seismic data are as follows: ； In the formula, To reconstruct the denoised seismic data, It is the first The wave function of a particle. These are the coefficients after thresholding.

8. A quantum mechanics-based adaptive denoising system for seismic data, characterized in that, The system for implementing the method as described in any one of claims 1-7 comprises: The quantum potential energy field mapping module is used to map seismic data into a quantum potential energy field based on the correspondence between seismic signal amplitude and quantum potential energy field. The adaptive quantum basis function construction module is used to process the quantum potential field by solving the eigenvalue problem of the Schrödinger equation to obtain adaptive quantum basis functions; The sparse representation module is used to perform sparse representation processing on seismic data based on adaptive quantum basis functions to obtain the expansion coefficients of the signal on the quantum basis. The noise suppression module is used to process the expansion coefficients using an energy-based thresholding mechanism to obtain the denoised coefficients. The denoised data reconstruction module is used to reconstruct the denoised seismic data using the denoised coefficients through a linear combination of quantum basis functions, resulting in the final denoised seismic data.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.

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