Method for estimating seismic data quality factor and seismic wavelet in non-stationary state

Abstract

This application relates to a method for estimating the quality factor and seismic wavelet of unsteady seismic data. It addresses the stringent assumptions required for high-resolution processing of unsteady seismic data by designing a framework to simultaneously estimate the seismic wavelet and quality factor. The estimation problem is transformed into a global intelligent optimization search for the optimal location path through an encoding-decoding approach. The introduction of particle swarm optimization ensures that the final solution does not get trapped in local optima, thus guaranteeing the accuracy of the output results. This application can simultaneously estimate the seismic wavelet and quality factor, utilizing the movement of the root and combining binary-to-decimal conversion for encoding and decoding, thereby achieving stable output results and exhibiting excellent noise resistance.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, specifically to a non-steady-state seismic data quality factor and a seismic wavelet estimation method. Background Technology

[0002] Seismic reflection exploration is a crucial technique for regional geological surveys and oil and gas resource exploration. The maturity and deployment of related methods are irreplaceable by other technologies, particularly in the identification and characterization of deep oil and gas reservoirs. To more precisely describe the depth and spatial variations of subsurface formations, high-resolution seismic data processing techniques require continuous updates and optimizations. The ultimate goal of seismic reflection data processing is to obtain more accurate information on subsurface reflectors. Therefore, to ensure that the phase axes in the seismic data volume better indicate the true lithological interfaces, the high-resolution processing workflow needs to eliminate as much of the influence of seismic wavelets and formation absorption attenuation as possible, i.e., eliminate the filtering effects of seismic wavelets and the quality factor (Q).

[0003] When considering the effects of both factors simultaneously, the assumptions of seismic data based on the traditional convolution model no longer hold. Seismic waves experience frequency reduction, phase distortion, and energy attenuation during propagation, ultimately leading to a decrease in the longitudinal resolution of reflected seismic data. Conventional deconvolution and inverse Q-filtering aim to eliminate the effects of these filters and recover the true reflection coefficients of the subsurface strata. However, using linear time-invariant systems to solve nonlinear problems by designing inverse wavelet and inverse Q-filters is unstable. Furthermore, conventional processing strategies assume that the seismic wavelet and subsurface Q-values ​​are known; even without bias, linear inversion still presents multiple solutions and instability. Therefore, current high-resolution processing techniques for non-steady-state seismic data rely heavily on numerous assumptions. Accurate estimation and capture of the seismic wavelet and Q-model are not easy in actual seismic data processing. Moreover, the elimination of wavelet bandpass filtering and compensation for absorption attenuation are usually performed independently—that is, the formation Q-filtering effect is eliminated before the seismic wavelet filtering effect. Of course, the inconsistencies in the initial processing results will inevitably affect the subsequent processing outcomes. Summary of the Invention

[0004] To overcome at least one deficiency in the prior art, this application provides a non-steady-state seismic data quality factor and a seismic wavelet estimation method.

[0005] Firstly, a method for estimating the quality factor and wavelet of non-steady-state seismic data is provided, including:

[0006] The amplitude spectrum of the initial seismic wavelet was determined based on unsteady seismic data;

[0007] The roots of the initial seismic wavelet are obtained by performing a Z-transform on the amplitude spectrum of the initial seismic wavelet.

[0008] The search space is established based on the number of roots of the initial seismic wavelet and the number of bits of the binary quality factor; the search space is a two-dimensional space, the first dimension of which has a size of 2, and the second dimension has a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits of the binary quality factor.

[0009] The optimal quality factor and seismic wavelet are estimated using a particle swarm optimization algorithm. During the estimation process, the optimal solution is determined based on the positions of the particles in the swarm within the search space.

[0010] In one embodiment, a particle swarm optimization algorithm is used to estimate the optimal quality factor and seismic wavelet, including:

[0011] Step S41: Initialize the position and velocity of each particle in the particle swarm; encode the position of the particles, including encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle, and binary encoding the quality factor.

[0012] Step S42: For the current iteration, update the position and velocity of each particle;

[0013] Step S43: Decode the position trajectory of each particle to obtain the seismic wavelet and quality factor corresponding to each particle;

[0014] Step S44: Determine the composite value of the seismic data based on the seismic wavelet and quality factor corresponding to each particle;

[0015] Step S45: Calculate the cross-correlation coefficient between the composite value of the seismic data and the original seismic data corresponding to each particle, and select the particle with the largest cross-correlation coefficient as the optimal particle for the current iteration.

[0016] Step S46: Determine whether the iteration termination condition is met. If it is met, output the seismic wavelet and quality factor corresponding to the best particle in the current iteration as the best quality factor and seismic wavelet. If it is not met, return to step S42.

[0017] In one embodiment, the termination condition is: the current iteration reaches the maximum number of iterations; or, the termination condition is:

[0018] The current iteration has not reached the maximum number of iterations, and max‖xcorr k -xcorr k-1 || <E,xcorr k xcorr is the maximum cross-correlation coefficient corresponding to the current iteration k. k-1 E is the maximum cross-correlation coefficient corresponding to the previous iteration k-1, and E is a set threshold.

[0019] In one embodiment, the synthesized value of the seismic data is determined based on the seismic wavelet and quality factor corresponding to each particle, using the following formula:

[0020]

[0021] Where s is the composite value of the seismic data, M is the number of frequency sampling points, and F -1 Here, is the inverse Fourier transform operator, W is a diagonal matrix composed of seismic wavelets, A is a discrete matrix related to the quality factor, r is the reflection coefficient, and n is noise.

[0022] Secondly, a device for estimating the quality factor and wavelet of unsteady seismic data is provided, including:

[0023] The amplitude spectrum determination module is used to determine the amplitude spectrum of the initial seismic wavelet based on unsteady seismic data;

[0024] The root acquisition module is used to perform a Z-transform on the amplitude spectrum of the initial seismic wavelet to obtain the root of the initial seismic wavelet;

[0025] The search space establishment module is used to establish a search space based on the number of roots of the initial seismic wavelet and the number of bits of the binary representation of the quality factor. The search space is a two-dimensional space, with the first dimension having a size of 2 and the second dimension having a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits of the binary representation of the quality factor.

[0026] The estimation module is used to estimate the optimal quality factor and seismic wavelet using the particle swarm optimization algorithm. During the estimation process using the particle swarm optimization algorithm, the optimal solution is determined based on the positions of the particles in the swarm within the search space.

[0027] In one embodiment, the estimation module is further configured to:

[0028] Step S41: Initialize the position and velocity of each particle in the particle swarm; encode the position of the particles, including encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle, and binary encoding the quality factor.

[0029] Step S42: For the current iteration, update the position and velocity of each particle;

[0030] Step S43: Decode the position trajectory of each particle to obtain the seismic wavelet and quality factor corresponding to each particle;

[0031] Step S44: Determine the composite value of the seismic data based on the seismic wavelet and quality factor corresponding to each particle;

[0032] Step S45: Calculate the cross-correlation coefficient between the composite value of the seismic data and the original seismic data corresponding to each particle, and select the particle with the largest cross-correlation coefficient as the optimal particle for the current iteration.

[0033] Step S46: Determine whether the iteration termination condition is met. If it is met, output the seismic wavelet and quality factor corresponding to the best particle in the current iteration as the best quality factor and seismic wavelet. If it is not met, return to step S42.

[0034] In one embodiment, the termination condition is:

[0035] The current iteration has reached the maximum number of iterations;

[0036] Alternatively, the termination condition is:

[0037] The current iteration has not reached the maximum number of iterations, and max‖xcorr k -xcorr k-1 || <E,xcorr k xcorr is the maximum cross-correlation coefficient corresponding to the current iteration k. k-1 E is the maximum cross-correlation coefficient corresponding to the previous iteration k-1, and E is a set threshold.

[0038] In one embodiment, the synthesized value of the seismic data is determined based on the seismic wavelet and quality factor corresponding to each particle, using the following formula:

[0039]

[0040] Where s is the composite value of the seismic data, m is the number of frequency domain sampling points, and F -1 Here, is the inverse Fourier transform operator, W is a diagonal matrix composed of seismic wavelets, A is a discrete matrix related to the quality factor, r is the reflection coefficient, is a known value, and n is noise.

[0041] Thirdly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned non-steady-state seismic data quality factor and seismic wavelet estimation method.

[0042] Fourthly, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the aforementioned non-steady-state seismic data quality factor and seismic wavelet estimation method.

[0043] Compared with the prior art, this application has the following beneficial effects:

[0044] 1. This application considers the problem that the assumptions for high-resolution processing of unsteady seismic data are subject to stringent conditions. It designs a corresponding framework to simultaneously estimate seismic wavelets and quality factors. The estimation problem of both is transformed into a global intelligent optimization search for the best location path through an encoding-decoding method.

[0045] 2. The introduction of the particle swarm optimization algorithm in this application ensures that the final solution will not get trapped in local extrema, thus guaranteeing the accuracy of the output results; it can simultaneously estimate the seismic wavelet and quality factor, and use the movement of the root to encode and decode by combining binary-to-decimal conversion, thereby stabilizing the output results and showing excellent noise resistance.

[0046] 3. This application can provide accurate and reliable parameters for subsequent processing techniques, and performs excellently in theoretically synthesized seismic data and actual data testing. Attached Figure Description

[0047] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings:

[0048] Figure 1 A flowchart illustrating the non-steady-state seismic data quality factor and seismic wavelet estimation method according to an embodiment of this application is shown;

[0049] Figure 2 A schematic diagram of the two-dimensional search space is shown;

[0050] Figure 3 The diagrams show composite seismic data; (a) is a diagram of reflection coefficients, (b) is a diagram of composite seismic data without added noise, (c) is a diagram of composite seismic data with 40% random noise added, and (d) is a diagram of composite seismic data with 40% trap noise added.

[0051] Figure 4 A waveform comparison diagram is shown between the seismic wavelet estimated from noise-free seismic data and the actual seismic wavelet.

[0052] Figure 5 A comparison diagram showing the distribution of the Z-transform roots of the seismic wavelet estimated from noise-free seismic data and the Z-transform roots of the actual seismic wavelet is presented.

[0053] Figure 6 The variation of particle swarm matching values ​​with the number of iterations is shown when using the particle swarm optimization algorithm on noise-free seismic data.

[0054] Figure 7 A waveform comparison diagram is shown between the estimated seismic wavelet and the actual seismic wavelet for seismic data containing random noise.

[0055] Figure 8 A comparison of the distribution of the Z-transform roots of the seismic wavelet estimated from seismic data containing random noise and the Z-transform roots of the actual seismic wavelet is shown.

[0056] Figure 9 The diagram illustrates how the particle swarm optimization algorithm is applied to seismic data containing random noise, and how the particle swarm matching value changes with the number of iterations.

[0057] Figure 10 A waveform comparison diagram is shown between the estimated seismic wavelet and the actual seismic wavelet for seismic data containing trapped noise.

[0058] Figure 11 A comparison diagram showing the distribution of the Z-transform roots of the seismic wavelet estimated from seismic data containing trapped noise is shown.

[0059] Figure 12 The diagram illustrates how the particle swarm optimization algorithm is used to adjust the particle swarm matching value with the number of iterations when applying the particle swarm optimization algorithm to seismic data containing trapped noise.

[0060] Figure 13 This diagram illustrates actual post-stack seismic data for a gas field.

[0061] Figure 14 A comparison diagram is shown between the seismic wavelet obtained from seismic data analysis and the estimated seismic wavelet.

[0062] Figure 15 This shows how the particle swarm matching value changes with the number of iterations when using the particle swarm optimization algorithm;

[0063] Figure 16 A structural block diagram of an unsteady seismic data quality factor and seismic wavelet estimation device according to an embodiment of this application is shown. Detailed Implementation

[0064] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0065] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution according to this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0066] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0067] To eliminate the unreasonable assumptions made in traditional high-resolution processing of unsteady seismic data regarding the known seismic wavelet and quality factor, and to achieve minimal assumptions for practical processing, this application provides a method for estimating the quality factor and seismic wavelet of unsteady seismic data, avoiding the influence of artificially given parameter models on the processing results in processing unsteady reflection seismic records. Figure 1 A flowchart illustrating the non-steady-state seismic data quality factor and seismic wavelet estimation method according to an embodiment of this application is shown. See also... Figure 1 The method includes the following steps:

[0068] Step S1: Determine the amplitude spectrum of the initial seismic wavelet based on the unsteady-state seismic data. Here, the unsteady-state seismic data is known data, i.e., the original seismic data. The amplitude spectrum of the well-side seismic trace can be obtained from the unsteady-state seismic data. The amplitude spectrum is then smoothed to obtain the amplitude spectrum of the initial seismic wavelet.

[0069] Step S2: Perform a Z-transform on the amplitude spectrum of the initial seismic wavelet to obtain the root of the initial seismic wavelet.

[0070] Step S3: Establish a search space based on the number of roots of the initial seismic wavelet and the number of bits in the binary representation of the quality factor; the search space is a two-dimensional space, with the first dimension having a size of 2 and the second dimension having a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits in the binary representation of the quality factor.

[0071] Here, if the number of roots is N r +N c N r Let N be the number of real roots. c If we are the number of complex roots, then we can obtain 2. Nr+Nc A seismic wavelet; the binary number of the quality factor can be set to 10, that is, the Q value range is 1 to 1023, that is, the search space is (2, N). r +N c +10).

[0072] Step S4 involves using a particle swarm optimization (PSO) algorithm to estimate the optimal quality factor and seismic wavelet. During this estimation process, the optimal solution is determined based on the positions the particle swarm traverses within the search space; this optimal solution represents the best quality factor and seismic wavelet. Using PSO to estimate the optimal quality factor and seismic wavelet significantly reduces computational complexity and improves computational efficiency.

[0073] Here, when using the particle swarm optimization algorithm for estimation, the length of the seismic wavelet to be estimated and the relevant parameters of the particle swarm optimization algorithm can be preset, such as the number of particles in the particle swarm, the maximum number of iterations, the learning factor, and the inertia weight.

[0074] In this embodiment, the mathematical representation framework of seismic wavelet filtering effect and formation absorption attenuation effect is used. Through a global optimization algorithm strategy, the quality factor and seismic wavelet are adaptively estimated, which can provide the basic parameters required for subsequent high-resolution data processing. It has high computational efficiency, accurate output, and convenient deployment.

[0075] In one embodiment, step S4, estimating the optimal quality factor and seismic wavelet using a particle swarm optimization algorithm, may include:

[0076] Step S41: Initialize the position and velocity of each particle in the particle swarm; encode the particle position, including encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle, and binary encoding the quality factor; here, the first part of the encoding is encoding the seismic wavelet, that is, encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle. The particle can only choose between 0 and 1. If it is 0, it means that the root does not undergo a symmetric transformation about the unit circle. If it is 1, it means that the root undergoes a symmetric transformation about the unit circle; the second part of the encoding is the binary encoding of the quality factor Q. Since the Q value is positive, a simple decimal integer to binary encoding strategy is adopted; the Q value of a general stratum is within a certain range, and when the Q value is large, it can be approximated as no attenuation. In this embodiment, the number of bits to be searched by the particle swarm algorithm is 10, that is, the Q value range is 1 to 1023.

[0077] Step S42: For the current iteration, update the position and velocity of each particle.

[0078] Here, the speed update can be achieved using the following formula:

[0079]

[0080] in, Let ω be the velocity of particle i in the (k+1)th iteration in the d-th dimension, and ω be the inertia weight. Let be the velocity of particle i in the d-th dimension during the k-th iteration, c1 and c2 be learning factors, and r1 and r2 be random numbers between 0 and 1 to increase the randomness of the search. The optimal matching value is obtained by decoding the position trajectory of particle i in the d-th dimension during the k-th iteration. The optimal matching value is obtained by decoding and calculating the position trajectory of the particle swarm in the d-th dimension during the k-th iteration. Let be the position of particle i in the d-th dimension during the k-th iteration.

[0081] Position updates can be performed using the following formula:

[0082]

[0083] in, Let be the position of particle i in the d-th dimension during the (k+1)-th iteration.

[0084] Step S43: Decode the position trajectory of each particle to obtain the seismic wavelet and quality factor corresponding to each particle. Figure 2 A schematic diagram of the two-dimensional search space is shown; see [link / reference]. Figure 2 Each particle obtains a position trajectory (red solid line) in each iteration, and the method of decoding the position trajectory corresponds to the encoding method.

[0085] Step S44: Determine the composite value of the seismic data based on the seismic wavelet and quality factor corresponding to each particle.

[0086] Specifically, the composite value of seismic data can be obtained using the following formula:

[0087]

[0088] Where s is the composite value of the seismic data, m is the number of frequency domain sampling points, and F -1 Here, is the inverse Fourier transform operator, W is a diagonal matrix composed of seismic wavelets, A is a discrete matrix related to the quality factor, r is the reflection coefficient, is a known value, and n is noise.

[0089] F -1 For the inverse Fourier transform operator, that is:

[0090]

[0091] Where ω0,…ω m-1 Let m be the discrete sampling points of angular frequency ω, and m be the number of sampling points in the frequency domain, t0,…t n-1 Let be the sampling points at time t, and n be the number of sampling points in the actual earthquake record.

[0092] A is a discrete matrix related to the quality factor, i.e.:

[0093]

[0094] Where Q is the quality factor, ω c The reference angular frequency is γ, which is a quality factor related variable, and γ = -1 / (πQ).

[0095] Figure 3The diagrams show composite seismic data, where (a) is a diagram of reflection coefficients, (b) is a diagram of composite seismic data without added noise, (c) is a diagram of composite seismic data with 40% random noise added, and (d) is a diagram of composite seismic data with 40% trap noise added.

[0096] Step S45: Calculate the cross-correlation coefficient between the composite value of the seismic data and the original seismic data corresponding to each particle, and select the particle corresponding to the largest cross-correlation coefficient as the optimal particle for the current iteration.

[0097] The cross-correlation coefficient xcorr is calculated using the following formula:

[0098]

[0099] Where s is the composite value of the seismic data, s true This is the raw earthquake data.

[0100] Step S46: Determine whether the iteration termination condition is met. If it is met, output the seismic wavelet and quality factor corresponding to the best particle in the current iteration as the best quality factor and seismic wavelet. If it is not met, return to step S42.

[0101] Specifically, the termination condition can be that the current iteration reaches the maximum number of iterations. In other embodiments, the termination condition can also be that the current iteration has not reached the maximum number of iterations, and max‖xcorr k -xcorr k-1 || <E,xcorr k xcorr is the maximum cross-correlation coefficient corresponding to the current iteration k. k-1 E is the maximum cross-correlation coefficient corresponding to the previous iteration k-1, and E is a set threshold.

[0102] To further verify the effectiveness of the non-steady-state seismic data quality factor and seismic wavelet estimation method of this application, the quality factor and seismic wavelet are estimated using the method of this application. Figure 4 The diagram shows a waveform comparison between the estimated seismic wavelet and the actual seismic wavelet for noise-free seismic data. The estimated seismic wavelet is represented by an orange dashed line, while the actual seismic wavelet is represented by a blue solid line.

[0103] Figure 5 The diagram shows a comparison of the distribution of the Z-transform roots of the seismic wavelet estimated from noise-free seismic data with that of the actual seismic wavelet. The blue circles represent the Z-transform roots of the actual seismic wavelet, the orange stars represent the Z-transform roots of the estimated seismic wavelet, and the pink line is the unit circle.

[0104] Figure 6The diagram illustrates how the particle swarm optimization (PSO) matching value changes with the number of iterations when applied to noise-free seismic data. The orange line represents the change in the best matching value among the 50 particles at a given iteration number, while the blue line represents the convergence of the average matching value among the 50 particles.

[0105] according to Figures 4-6 As can be seen, the method of this application can accurately output the seismic wavelet and quality factor in a noise-free environment. The output quality factor Q value is 50, which is exactly the same as the true Q value, further demonstrating the accuracy of the estimation by the method of this application; the optimal matching value can converge precisely to 1 after multiple iterations, further demonstrating the feasibility of the method of this application.

[0106] Figure 7 The diagram shows a waveform comparison between the estimated seismic wavelet and the actual seismic wavelet for seismic data containing random noise. The estimated seismic wavelet is represented by an orange dashed line, while the actual seismic wavelet is represented by a blue solid line.

[0107] Figure 8 The diagram shows a comparison of the distribution of the Z-transform roots of the seismic wavelet estimated from seismic data containing random noise with that of the actual seismic wavelet. In the diagram, the blue circles represent the Z-transform roots of the actual seismic wavelet, the orange stars represent the Z-transform roots of the estimated seismic wavelet, and the pink line is the unit circle.

[0108] Figure 9 This diagram illustrates how the particle swarm optimization (PSO) matching value changes with the number of iterations when applied to seismic data containing random noise. The orange line represents the change in the best matching value among the 50 particles at a given iteration number, while the blue line represents the convergence of the average matching value among the 50 particles.

[0109] according to Figures 7-9 It can be seen that the present application can output seismic wavelet and quality factor relatively accurately under random noise environment. The output quality factor Q value is 48, which has a slight difference from the true Q value. The best matching value converges to 0.84, indicating that the method of the present application can overcome strong random interference and achieve accurate estimation of relevant parameters.

[0110] Figure 10 The diagram shows a waveform comparison between the estimated seismic wavelet and the actual seismic wavelet for seismic data containing trapped noise. The estimated seismic wavelet is represented by an orange dashed line, and the actual seismic wavelet is represented by a blue solid line.

[0111] Figure 11The diagram shows a comparison of the distribution of the Z-transform roots of the estimated seismic wavelet with the Z-transform roots of the actual seismic wavelet for seismic data containing trapped noise. In the diagram, the blue circles represent the Z-transform roots of the actual seismic wavelet, the orange stars represent the Z-transform roots of the estimated seismic wavelet, and the pink line is the unit circle.

[0112] Figure 12 This diagram illustrates how the particle swarm optimization (PSO) matching value changes with the number of iterations when applied to seismic data containing trapped noise. The orange line represents the change in the best matching value among the 50 particles at a given iteration number, while the blue line represents the convergence of the average matching value among the 50 particles.

[0113] according to Figures 10-12 It can be seen that this application can output seismic wavelet and quality factor relatively accurately even in relatively difficult banded noise environments. The output Q value is 44.68, which differs slightly from the true Q value. The optimal matching value converges to 0.82, indicating that the method of this application can overcome strong interference. The noise is distributed within a certain frequency range, but the method of this application can overcome this interference and obtain relatively accurate results, further demonstrating the advantages of the method of this application.

[0114] Figure 13 The diagram shows a schematic of actual back-stack seismic data for a gas field. The profile indicates a relatively simple geological structure with clear reflection phase axes and no significant tectonic undulations. A reflection coefficient curve (solid black line) can be obtained from well logging data at CDP623.

[0115] Figure 14 The diagram shows a comparison between the seismic wavelet obtained from seismic data analysis and the estimated seismic wavelet. The seismic wavelet obtained from seismic data analysis is represented by an orange dashed line, while the estimated seismic wavelet is represented by a blue solid line.

[0116] Figure 15 This diagram illustrates how the particle swarm optimization (PSO) algorithm's matching value changes with the number of iterations. The orange line represents the change in the best matching value among the 50 particles at the corresponding iteration number, while the blue line represents the convergence of the average matching value among the 50 particles.

[0117] according to Figures 13-15 It can be seen that the seismic wavelet and quality factor obtained by the method proposed in this application are reasonable and can be used for further high-resolution processing and inversion procedures. The final estimated Q value is 217.125. The optimal matching value obtained by the final particle swarm optimization algorithm is 0.6022, which meets the basic conditions for well-seismic matching.

[0118] Based on the same inventive concept as the non-steady-state seismic data quality factor and seismic wavelet estimation method, this embodiment also provides a corresponding non-steady-state seismic data quality factor and seismic wavelet estimation device. Figure 16 A structural block diagram of an apparatus for estimating the quality factor of unsteady seismic data and a seismic wavelet according to an embodiment of this application is shown, including:

[0119] Amplitude spectrum determination module 161 is used to determine the amplitude spectrum of the initial seismic wavelet based on unsteady seismic data;

[0120] The root acquisition module 162 is used to perform Z-transform on the amplitude spectrum of the initial seismic wavelet to obtain the root of the initial seismic wavelet;

[0121] The search space establishment module 163 is used to establish a search space based on the number of roots of the initial seismic wavelet and the number of bits of the binary quality factor; the search space is a two-dimensional space, the first dimension of which has a size of 2, and the second dimension has a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits of the binary quality factor.

[0122] The estimation module 164 is used to estimate the optimal quality factor and seismic wavelet using the particle swarm optimization algorithm. During the estimation process using the particle swarm optimization algorithm, the optimal solution is determined based on the positions of the particles in the particle swarm within the search space.

[0123] The non-steady-state seismic data quality factor and seismic wavelet estimation device of this embodiment has the same inventive concept as the non-steady-state seismic data quality factor and seismic wavelet estimation method described above. Therefore, the specific implementation of this device can be found in the embodiment section of the non-steady-state seismic data quality factor and seismic wavelet estimation method described above, and its technical effects correspond to the technical effects of the above method, so it will not be repeated here.

[0124] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-described non-steady-state seismic data quality factor and seismic wavelet estimation method.

[0125] This application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the aforementioned non-steady-state seismic data quality factor and seismic wavelet estimation method.

[0126] In summary, this application has the following technical effects:

[0127] 1. This application considers the problem that the assumptions for high-resolution processing of unsteady seismic data are subject to stringent conditions. It designs a corresponding framework to simultaneously estimate seismic wavelets and quality factors. The estimation problem of both is transformed into a global intelligent optimization search for the best location path through an encoding-decoding method.

[0128] 2. The introduction of the particle swarm optimization algorithm in this application ensures that the final solution will not get trapped in local extrema, thus guaranteeing the accuracy of the output results; it can simultaneously estimate the seismic wavelet and quality factor, and use the movement of the root to encode and decode by combining binary-to-decimal conversion, thereby stabilizing the output results and showing excellent noise resistance.

[0129] 3. This application can provide accurate and reliable parameters for subsequent processing techniques, and performs excellently in theoretically synthesized seismic data and actual data testing.

[0130] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for estimating the quality factor and seismic wavelet of non-steady-state seismic data, characterized in that, include: The amplitude spectrum of the initial seismic wavelet was determined based on unsteady seismic data; The roots of the initial seismic wavelet are obtained by performing a Z-transform on the amplitude spectrum of the initial seismic wavelet. A search space is established based on the number of roots of the initial seismic wavelet and the number of bits in the binary representation of the quality factor; the search space is a two-dimensional space, the first dimension of which has a size of 2, and the second dimension has a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits in the binary representation of the quality factor. The optimal quality factor and seismic wavelet are estimated using a particle swarm optimization algorithm. During the estimation process using the particle swarm optimization algorithm, the optimal solution is determined based on the positions traversed by the particles in the search space. The process of estimating the optimal quality factor and seismic wavelet using the particle swarm optimization algorithm includes: Step S41: Initialize the position and velocity of each particle in the particle swarm; encode the position of the particle, the encoding including encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle, and binary encoding the quality factor; Step S42: For the current iteration, update the position and velocity of each particle; Step S43: Decode the position trajectory of each particle to obtain the seismic wavelet and quality factor corresponding to each particle; Step S44: Determine the composite value of the seismic data based on the seismic wavelet corresponding to each particle and the quality factor; Step S45: Calculate the cross-correlation coefficient between the synthesized value of the seismic data corresponding to each particle and the original seismic data, and select the particle corresponding to the largest cross-correlation coefficient as the optimal particle for the current iteration. Step S46: Determine whether the iteration termination condition is met. If it is met, output the seismic wavelet and quality factor corresponding to the best particle in the current iteration as the best quality factor and seismic wavelet. If it is not met, return to step S42.

2. The method as described in claim 1, characterized in that, The termination condition is: the current iteration reaches the maximum number of iterations; or, the termination condition is: The current iteration has not reached the maximum number of iterations, and, , The maximum cross-correlation number corresponding to the current iteration k. The maximum cross-correlation number corresponding to the previous iteration k-1. To set a threshold.

3. The method as described in claim 1, characterized in that, in, The composite value of the seismic data is determined based on the seismic wavelet corresponding to each particle and the quality factor, using the following formula: Where s is the composite value of the seismic data, M F is the number of frequency sampling points. -1 Here, is the inverse Fourier transform operator, W is a diagonal matrix composed of seismic wavelets, A is a discrete matrix related to the quality factor, r is the reflection coefficient, and n is noise.

4. A device for estimating the quality factor and wavelet of unsteady seismic data, characterized in that, include: The amplitude spectrum determination module is used to determine the amplitude spectrum of the initial seismic wavelet based on unsteady seismic data; The root acquisition module is used to perform a Z-transform on the amplitude spectrum of the initial seismic wavelet to obtain the root of the initial seismic wavelet; The search space establishment module is used to establish a search space based on the number of roots of the initial seismic wavelet and the number of bits of the binary representation of the quality factor; the search space is a two-dimensional space, the first dimension of which has a size of 2, and the second dimension has a size equal to the sum of the number of roots of the Z-transform polynomial and the number of bits of the binary representation of the quality factor. The estimation module is used to estimate the optimal quality factor and seismic wavelet using a particle swarm optimization algorithm; during the estimation process using the particle swarm optimization algorithm, the optimal solution is determined based on the positions traversed by the particles in the particle swarm within the search space. The estimation module is further used for: Step S41: Initialize the position and velocity of each particle in the particle swarm; encode the position of the particle, the encoding including encoding whether the root corresponding to the initial seismic wavelet has moved about the unit circle, and binary encoding the quality factor; Step S42: For the current iteration, update the position and velocity of each particle; Step S43: Decode the position trajectory of each particle to obtain the seismic wavelet and quality factor corresponding to each particle; Step S44: Determine the composite value of the seismic data based on the seismic wavelet corresponding to each particle and the quality factor; Step S45: Calculate the cross-correlation coefficient between the synthesized value of the seismic data corresponding to each particle and the original seismic data, and select the particle corresponding to the largest cross-correlation coefficient as the optimal particle for the current iteration. Step S46: Determine whether the iteration termination condition is met. If it is met, output the seismic wavelet and quality factor corresponding to the best particle in the current iteration as the best quality factor and seismic wavelet. If it is not met, return to step S42.

5. The apparatus as described in claim 4, characterized in that, The termination condition is: The current iteration has reached the maximum number of iterations; Alternatively, the termination condition is: The current iteration has not reached the maximum number of iterations, and, , The maximum cross-correlation number corresponding to the current iteration k. The maximum cross-correlation number corresponding to the previous iteration k-1. To set a threshold.

6. The apparatus as claimed in claim 4, characterized in that, in, The composite value of the seismic data is determined based on the seismic wavelet corresponding to each particle and the quality factor, using the following formula: Where s is the composite value of the seismic data, m F is the number of sampling points in the frequency domain. -1 Here, is the inverse Fourier transform operator, W is a diagonal matrix composed of seismic wavelets, A is a discrete matrix related to the quality factor, r is the reflection coefficient, is a known value, and n is noise.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the non-steady-state seismic data quality factor and seismic wavelet estimation method according to any one of claims 1-3.

8. A computer program product, characterized in that, It includes a computer program / instruction, which, when executed by a processor, implements the non-steady-state seismic data quality factor and seismic wavelet estimation method according to any one of claims 1-3.

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