Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With ...Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With the development of seismic exploration into deep and ultradeep events,especially those from complex targets in the western region of China,the internal multiple eliminations become increasingly challenging.Currently,three-dimensional(3D)seismic data are primarily used for oil and gas target recognition and drilling.Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling.In this study,we propose an internal multiple prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory.This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps.Firstly,simulating direct waves with a 3D macroscopic velocity model.Secondly,using direct waves and 3D full seismic acquisition records to obtain the upgoing and down-going Green's functions between the virtual source point and surface.Thirdly,constructing internal multiples of the relevant layers by upgoing and downgoing Green's functions.Finally,utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples.Compared with the two-dimensional(2D)Marchenko algo-rithm,the performance of the 3D Marchenko algorithm for internal multiple prediction has been significantly enhanced,resulting in higher computational accuracy.Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data,thereby exhibiting important theoretical and industrial application value.展开更多
Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to inter...Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to internal multiples.The Marchenko imaging method is a target-oriented technique;thus,it can image a user specified area.In the traditional Marchenko method,an accurate velocity model is critical for estimating direct waves from imaging points to the surface.An error in the velocity model results in the inaccurate estimation of direct waves.In turn,this leads to errors in computation of one-way Green’s functions,which then affects the final Marchenko images.To solve this problem,in this paper,we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method.The proposed method calculates the time shift of direct waves caused by the error in the velocity model,and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions.The proposed method improves the results of imaging using an inaccurate velocity model.By comparing the results from traditional Marchenko and the new method using synthetic data experiments,we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions.展开更多
By means of some algebraic techniques,especially the Binet-Cauchy formula,an explicit multi-soliton solution of the derivative nonlinear Schrdinger equation with vanishing boundary condition is attained based on a pur...By means of some algebraic techniques,especially the Binet-Cauchy formula,an explicit multi-soliton solution of the derivative nonlinear Schrdinger equation with vanishing boundary condition is attained based on a pure Marchenko formalism without needing the usual scattering data except for given N simple poles. The one-and two-soliton solutions are given as two special examples in illustration of the general formula of multi-soliton solution. Their effectiveness and equivalence to other approaches are also demonstrated. Meanwhile,the asymptotic behavior of the multi-soliton solution is discussed in detail. It is shown that the N-soliton solution can be viewed as a summation of N one-soliton solutions with a definite displacement and phase shift of each soliton in the whole process（from t →∞ to t → ＋∞ ） of the elastic collisions.展开更多
Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries,...Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries, the introduction of noise, and the size of the matrix, the resulting eigenvalue and eigenvector distributions remain relatively unchanged. However, when there are certain anomalous eigenvalues and their corresponding eigenvectors that do not follow the predicted distributions, it could indicate that there’s an underlying non-random signal inside the data. As data and matrices become more important in the sciences and computing, so too will the importance of processing them with the principles of random matrix theory.展开更多
文摘Internal multiples are commonly present in seismic data due to variations in velocity or density of subsurface media.They can reduce the signal-to-noise ratio of seismic data and degrade the quality of the image.With the development of seismic exploration into deep and ultradeep events,especially those from complex targets in the western region of China,the internal multiple eliminations become increasingly challenging.Currently,three-dimensional(3D)seismic data are primarily used for oil and gas target recognition and drilling.Effectively eliminating internal multiples in 3D seismic data of complex structures and mitigating their adverse effects is crucial for enhancing the success rate of drilling.In this study,we propose an internal multiple prediction algorithm for 3D seismic data in complex structures using the Marchenko autofocusing theory.This method can predict the accurate internal multiples of time difference without an accurate velocity model and the implementation process mainly consists of several steps.Firstly,simulating direct waves with a 3D macroscopic velocity model.Secondly,using direct waves and 3D full seismic acquisition records to obtain the upgoing and down-going Green's functions between the virtual source point and surface.Thirdly,constructing internal multiples of the relevant layers by upgoing and downgoing Green's functions.Finally,utilizing the adaptive matching subtraction method to remove predicted internal multiples from the original data to obtain seismic records without multiples.Compared with the two-dimensional(2D)Marchenko algo-rithm,the performance of the 3D Marchenko algorithm for internal multiple prediction has been significantly enhanced,resulting in higher computational accuracy.Numerical simulation test results indicate that our proposed method can effectively eliminate internal multiples in 3D seismic data,thereby exhibiting important theoretical and industrial application value.
基金supported by the National Natural Science Foundation of China(No.41874167)the National Science and Technology Major Project of China(No.2017YFB0202904)the National Natural Science Foundation of China(No.41904130)。
文摘Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to internal multiples.The Marchenko imaging method is a target-oriented technique;thus,it can image a user specified area.In the traditional Marchenko method,an accurate velocity model is critical for estimating direct waves from imaging points to the surface.An error in the velocity model results in the inaccurate estimation of direct waves.In turn,this leads to errors in computation of one-way Green’s functions,which then affects the final Marchenko images.To solve this problem,in this paper,we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method.The proposed method calculates the time shift of direct waves caused by the error in the velocity model,and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions.The proposed method improves the results of imaging using an inaccurate velocity model.By comparing the results from traditional Marchenko and the new method using synthetic data experiments,we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions.
基金Supported by the National Natural Science Foundation of China (10775105)
文摘By means of some algebraic techniques,especially the Binet-Cauchy formula,an explicit multi-soliton solution of the derivative nonlinear Schrdinger equation with vanishing boundary condition is attained based on a pure Marchenko formalism without needing the usual scattering data except for given N simple poles. The one-and two-soliton solutions are given as two special examples in illustration of the general formula of multi-soliton solution. Their effectiveness and equivalence to other approaches are also demonstrated. Meanwhile,the asymptotic behavior of the multi-soliton solution is discussed in detail. It is shown that the N-soliton solution can be viewed as a summation of N one-soliton solutions with a definite displacement and phase shift of each soliton in the whole process（from t →∞ to t → ＋∞ ） of the elastic collisions.
文摘Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability distribution of the entries, the introduction of noise, and the size of the matrix, the resulting eigenvalue and eigenvector distributions remain relatively unchanged. However, when there are certain anomalous eigenvalues and their corresponding eigenvectors that do not follow the predicted distributions, it could indicate that there’s an underlying non-random signal inside the data. As data and matrices become more important in the sciences and computing, so too will the importance of processing them with the principles of random matrix theory.
