Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model ...Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this...Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.展开更多
The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far ...The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber components in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial lowfrequency inversion to ensure the stability of the inversion,and use more high-wavenumber components in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.展开更多
Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuit...Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.展开更多
Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and a...Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and anisotropy parameter models, which are critical for imaging the long-offset and wideazimuth data. We develop an acoustic anisotropic FWI method based on a simplified pure quasi P-wave(qP-wave) equation, which can be solved efficiently and is beneficial for the subsequent inversion.Using the inverse Hessian operator to precondition the functional gradients helps to reduce the parameter tradeoff in the multi-parameter inversion. To balance the accuracy and efficiency, we extend the truncated Gauss-Newton(TGN) method into FWI of pure qP-waves in vertical transverse isotropic(VTI) media. The inversion is performed in a nested way: a linear inner loop and a nonlinear outer loop.We derive the formulation of Hessian-vector products for pure qP-waves in VTI media based on the Lagrange multiplier method and compute the model update by solving a Gauss-Newton linear system via a matrix-free conjugate gradient method. A suitable preconditioner and the Eisenstat and Walker stopping criterion for the inner iterations are used to accelerate the convergence and avoid prohibitive computational cost. We test the proposed FWI method on several synthetic data sets. Inversion results reveal that the pure acoustic VTI FWI exhibits greater accuracy than the conventional pseudoacoustic VTI FWI. Additionally, the TGN method proves effective in mitigating the parameter crosstalk and increasing the accuracy of anisotropy parameters.展开更多
Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networ...Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networks should be able to approximate the complex models, with a high degree of generalization. To achieve generalization, it is necessary to have a diverse range of examples in the training of the neural network, for example in data-driven FWI, training data should cover the expected subsurface models. To meet this requirement, we porposed a method to create geologically meaningful velocity models with complex structures and severe topography. However, it is important to note that generalization comes with its own set of challenges.Because of significant variation in topography of the generated velocity models, we need to include this information as an additional input data in training of the network. Therefore, we have transformed the seismic data to a fixed datum to incorporate geometric information. Additionally, we have enhanced the network's performance by introducing a term in the network loss function. Multiple metrics have been employed to evaluate the performance of the network. The results indicate that by providing the necessary information to the network and employing computational techniques to refine the model's accuracy, deep neural networks are capable of accurately estimating velocity models in complex environments characterized by extreme topography.展开更多
Although full waveform inversion in the frequency domain can overcome the local minima problem in the time direction, such problem still exists in the space direction because of the media subsurface complexity. Based ...Although full waveform inversion in the frequency domain can overcome the local minima problem in the time direction, such problem still exists in the space direction because of the media subsurface complexity. Based on the optimal steep descent methods, we present an algorithm which combines the preconditioned bi-conjugated gradient stable method and the multi-grid method to compute the wave propagation and the gradient space. The multiple scale prosperity of the waveform inversion and the multi-grid method can overcome the inverse problems local minima defect and accelerate convergence. The local inhomogeneous three-hole model simulated results and the Marmousi model certify the algorithm effectiveness.展开更多
In full waveform inversion （FWI）, Hessian information of the misfit function is of vital importance for accelerating the convergence of the inversion; however, it usually is not feasible to directly calculate the He...In full waveform inversion （FWI）, Hessian information of the misfit function is of vital importance for accelerating the convergence of the inversion; however, it usually is not feasible to directly calculate the Hessian matrix and its inverse. Although the limited memory Broyden-Fletcher-Goldfarb-Shanno （L-BFGS） or Hessian-free inexact Newton （HFN） methods are able to use approximate Hessian information, the information they collect is limited. The two methods can be interlaced because they are able to provide Hessian information for each other; however, the performance of the hybrid iterative method is dependent on the effective switch between the two methods. We have designed a new scheme to realize the dynamic switch between the two methods based on the decrease ratio （DR） of the misfit function （objective function）, and we propose a modified hybrid iterative optimization method. In the new scheme, we compare the DR of the two methods for a given computational cost, and choose the method with a faster DR. Using these steps, the modified method always implements the most efficient method. The results of Marmousi and overthrust model testings indicate that the convergence with our modified method is significantly faster than that in the L-BFGS method with no loss of inversion quality. Moreover, our modified outperforms the enriched method by a little speedup of the convergence. It also exhibits better efficiency than the HFN method.展开更多
In Surface wave waveform inversion, we want to reconstruct 3D shear wave velocity structure, which calculation beyond the capability of the powerful present day personal computer or even workstation. So we designed a ...In Surface wave waveform inversion, we want to reconstruct 3D shear wave velocity structure, which calculation beyond the capability of the powerful present day personal computer or even workstation. So we designed a high paralleled algorithm and carried out the inversion on Parallel computer based on the partitioned waveform inversion (PWI). It partitions the large scale optimization problem into a number of independent small scale problems and reduces the computational effort by several orders of magnitude. We adopted surface waveform inversion with a equal block(2 o×2 o) discretization.展开更多
As a high quality seismic imaging method, full waveform inversion （FWI） can accurately reconstruct the physical parameter model for the subsurface medium. However, application of the FWI in seismic data processing i...As a high quality seismic imaging method, full waveform inversion （FWI） can accurately reconstruct the physical parameter model for the subsurface medium. However, application of the FWI in seismic data processing is computationally expensive, especially for the three-dimension complex medium inversion. Introducing blended source technology into the frequency-domain FWI can greatly reduce the computational burden and improve the efficiency of the inversion. However, this method has two issues： first, crosstalk noise is caused by interference between the sources involved in the encoding, resulting in an inversion result with some artifacts; second, it is more sensitive to ambient noise compared to conventional FWI, therefore noisy data results in a poor inversion. This paper introduces a frequency-group encoding method to suppress crosstalk noise, and presents a frequency- domain auto-adapting FWI based on source-encoding technology. The conventional FWI method and source-encoding based FWI method are combined using an auto-adapting mechanism. This improvement can both guarantee the quality of the inversion result and maximize the inversion efficiency.展开更多
Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized...Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.展开更多
Reflection-based inversion that aims to reconstruct the low-to-intermediate wavenumbers of the subsurface model, can be a complementary to refraction-data-driven full-waveform inversion(FWI), especially for the deep t...Reflection-based inversion that aims to reconstruct the low-to-intermediate wavenumbers of the subsurface model, can be a complementary to refraction-data-driven full-waveform inversion(FWI), especially for the deep target area where diving waves cannot be acquired at the surface. Nevertheless, as a typical nonlinear inverse problem, reflection waveform inversion may easily suffer from the cycleskipping issue and have a slow convergence rate, if gradient-based first-order optimization methods are used. To improve the accuracy and convergence rate, we introduce the Hessian operator into reflection traveltime inversion(RTI) and reflection waveform inversion(RWI) in the framework of second-order optimization. A practical two-stage workflow is proposed to build the velocity model, in which Gauss-Newton RTI is first applied to mitigate the cycle-skipping problem and then Gauss-Newton RWI is employed to enhance the model resolution. To make the Gauss-Newton iterations more efficiently and robustly for large-scale applications, we introduce proper preconditioning for the Hessian matrix and design appropriate strategies to reduce the computational costs. The example of a real dataset from East China Sea demonstrates that the cascaded Hessian-based RTI and RWI have good potential to improve velocity model building and seismic imaging, especially for the deep targets.展开更多
Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic mi- gration and/or tomographic inv...Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic mi- gration and/or tomographic inversion methods involving illumination compensation. Vertical cable survey is a potential replacement of traditional marine seismic survey for its flexibility and data quality. Conventional vertical cable data processing requires separation of primaries and multiples before migration. We proposed to use multi-scale full waveform inversion （FWI） to improve illumination coverage of vertical cable survey. A deep water velocity model is built to test the capability of multi-scale FWI in detecting low velocity anomalies below seabed. Synthetic results show that multi-scale FWI is an effective model building tool in deep-water exploration. Geometry optimization through target ori- ented illumination analysis and multi-scale FWI may help to mitigate the risks of vertical cable survey. The combination of multi-scale FWI, low-frequency data and multi-vertical-cable acquisition system may provide both high resolution and high fidelity subsurface models.展开更多
Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce th...Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.展开更多
The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the tim...The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.This study applies the NAD method to solving three-dimensional(3D)acoustic wave equations in the frequency-domain.This forward modeling approach is then used as the“engine”for implementing 3D frequency-domain full waveform inversion(FWI).In the numerical modeling experiments,synthetic examples are first given to show the superiority of the NAD method in forward modeling compared with traditional finite difference methods.Synthetic 3D frequency-domain FWI experiments are then carried out to examine the effectiveness of the proposed methods.The inversion results show that the NAD method is more suitable than traditional methods,in terms of computational cost and stability,for 3D frequency-domain FWI,and represents an effective approach for inversion of subsurface model structures.展开更多
Full waveform inversion（FWI） directly minimizes errors between synthetic and observed data.For the surface acquisition geometry,reflections generated from deep reflectors are sensitive to overburden structure,so it ...Full waveform inversion（FWI） directly minimizes errors between synthetic and observed data.For the surface acquisition geometry,reflections generated from deep reflectors are sensitive to overburden structure,so it is reasonable to update the macro velocity model in a top-to-bottom manner.For models dominated by horizontally layered structures,combination of offset/time weighting and constant update depth control（CUDC） is sufficient for layer-stripping FWI.CUDC requires ray tracing to determine reflection traveltimes at a constant depth.As model complexity increases,the multi-path effects will have to be considered.We developed a new layer-stripping FWI method utilizing damped seismic reflection data,which does not need CUDC and ray tracing.Numerical examples show that effective update depth（EUD） can be controlled by damping constants even in complex regions and the inversion result is more accurate than conventional methods.展开更多
A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthet...A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.展开更多
The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In ...The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In the full waveform inversion method,adding a cross-correlation function to the objective function can eff ectively reduce the nonlinearity of the inversion process.In this paper,the nonlinearity of this process is reduced by introducing the correlation objective function into the FAWI and by deriving the corresponding gradient formula.We then combine the first-arrival wave travel-time tomography with the FAWI to form a set of inversion processes.This paper uses the limited memory Broyden-Fletcher-Goldfarb-Shanno(L-BFGS)algorithm to improve the computational effi ciency of inversion and solve the problem of the low effi ciency of the FAWI method.The overthrust model and fi eld data test show that the method used in this paper can eff ectively reduce the nonlinearity of inversion and improve the inversion calculation effi ciency at the same time.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024MF750281)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20230326)+1 种基金the Natural Science Foundation Project of Sichuan Province(Grant No.2025ZNSFSC1170)Sichuan Science and Technology Program(Grant No.2023ZYD0158).
文摘Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.12474461)the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.JCQY202402)the Goal-Oriented Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.MBDX202113).
文摘Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
基金jointly supported by Young Scientists Cultivation Fund Project of Harbin Engineering University(79000013/003)the Mount Taishan Industrial Leading Talent Project+1 种基金the Great and Special Project under Grant KJGG-2022-0104 of CNOOC Limitedthe National Natural Science Foundation of China(42006064,42106070,42074138)。
文摘The low-wavenumber components in the gradient of full waveform inversion(FWI)play a vital role in the stability of the inversion.However,when FWI is implemented in some high frequencies and current models are not far away from the real velocity model,an excessive number of low-wavenumber components in the gradient will also reduce the convergence rate and inversion accuracy.To solve this problem,this paper firstly derives a formula of scattering angle weighted gradient in FWI,then proposes a hybrid gradient.The hybrid gradient combines the conventional gradient of FWI with the scattering angle weighted gradient in each inversion frequency band based on an empirical formula derived herein.Using weighted hybrid mode,we can retain some low-wavenumber components in the initial lowfrequency inversion to ensure the stability of the inversion,and use more high-wavenumber components in the high-frequency inversion to improve the convergence rate.The results of synthetic data experiment demonstrate that compared to the conventional FWI,the FWI based on the proposed hybrid gradient can effectively reduce the low-wavenumber components in the gradient under the premise of ensuring inversion stability.It also greatly enhances the convergence rate and inversion accuracy,especially in the deep part of the model.And the field marine seismic data experiment also illustrates that the FWI based on hybrid gradient(HGFWI)has good stability and adaptability.
文摘Seismic migration and inversion are closely related techniques to portray subsurface images and identify hydrocarbon reservoirs.Seismic migration aims at obtaining structural images of subsurface geologic discontinuities.More specifically,seismic migration estimates the reflectivity function(stacked average reflectivity or pre-stack angle-dependent reflectivity)from seismic reflection data.On the other hand,seismic inversion quantitatively estimates the intrinsic rock properties of subsurface formulations.Such seismic inversion methods are applicable to detect hydrocarbon reservoirs that may exhibit lateral variations in the inverted parameters.Although there exist many differences,pre-stack seismic migration is similar with the first iteration of the general linearized seismic inversion.Usually,seismic migration and inversion techniques assume an acoustic or isotropic elastic medium.Unconventional reservoirs such as shale and tight sand formation have notable anisotropic property.We present a linearized waveform inversion(LWI)scheme for weakly anisotropic elastic media with vertical transversely isotropic(VTI)symmetry.It is based on two-way anisotropic elastic wave equation and simultaneously inverts for the localized perturbations(ΔVp_(0)/Vp_(0)/Vs_(0)/Vs_(0)/,Δ∈,Δδ)from the long-wavelength reference model.Our proposed VTI-elastic LWI is an iterative method that requires a forward and an adjoint operator acting on vectors in each iteration.We derive the forward Born approximation operator by perturbation theory and adjoint operator via adjoint-state method.The inversion has improved the quality of the images and reduces the multi-parameter crosstalk comparing with the adjoint-based images.We have observed that the multi-parameter crosstalk problem is more prominent in the inversion images for Thomsen anisotropy parameters.Especially,the Thomsen parameter is the most difficult to resolve.We also analyze the multi-parameter crosstalk using scattering radiation patterns.The linearized waveform inversion for VTI-elastic media presented in this article provides quantitative information of the rock properties that has the potential to help identify hydrocarbon reservoirs.
基金supported by the National Natural Science Foundation of China (grant No. 42174156)the Young Science and Technology Star Project of Shaanxi Province (grant No. 2023KJXX-021)the Fundamental Research Funds for the Central Universities, CHD (grant Nos. 300102263401 and 300102264204)。
文摘Full-waveform inversion(FWI) uses the full information of seismic data to obtain a quantitative estimation of subsurface physical parameters. Anisotropic FWI has the potential to recover high-resolution velocity and anisotropy parameter models, which are critical for imaging the long-offset and wideazimuth data. We develop an acoustic anisotropic FWI method based on a simplified pure quasi P-wave(qP-wave) equation, which can be solved efficiently and is beneficial for the subsequent inversion.Using the inverse Hessian operator to precondition the functional gradients helps to reduce the parameter tradeoff in the multi-parameter inversion. To balance the accuracy and efficiency, we extend the truncated Gauss-Newton(TGN) method into FWI of pure qP-waves in vertical transverse isotropic(VTI) media. The inversion is performed in a nested way: a linear inner loop and a nonlinear outer loop.We derive the formulation of Hessian-vector products for pure qP-waves in VTI media based on the Lagrange multiplier method and compute the model update by solving a Gauss-Newton linear system via a matrix-free conjugate gradient method. A suitable preconditioner and the Eisenstat and Walker stopping criterion for the inner iterations are used to accelerate the convergence and avoid prohibitive computational cost. We test the proposed FWI method on several synthetic data sets. Inversion results reveal that the pure acoustic VTI FWI exhibits greater accuracy than the conventional pseudoacoustic VTI FWI. Additionally, the TGN method proves effective in mitigating the parameter crosstalk and increasing the accuracy of anisotropy parameters.
文摘Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networks should be able to approximate the complex models, with a high degree of generalization. To achieve generalization, it is necessary to have a diverse range of examples in the training of the neural network, for example in data-driven FWI, training data should cover the expected subsurface models. To meet this requirement, we porposed a method to create geologically meaningful velocity models with complex structures and severe topography. However, it is important to note that generalization comes with its own set of challenges.Because of significant variation in topography of the generated velocity models, we need to include this information as an additional input data in training of the network. Therefore, we have transformed the seismic data to a fixed datum to incorporate geometric information. Additionally, we have enhanced the network's performance by introducing a term in the network loss function. Multiple metrics have been employed to evaluate the performance of the network. The results indicate that by providing the necessary information to the network and employing computational techniques to refine the model's accuracy, deep neural networks are capable of accurately estimating velocity models in complex environments characterized by extreme topography.
基金supported by the China State Key Science and Technology Project on Marine Carbonate Reservoir Characterization (No. 2011ZX05004-003)the Basic Research Programs of CNPC during the 12th Five-Year Plan Period (NO.2011A-3603)+1 种基金the Natural Science Foundation of China (No.41104066)the RIPED Young Professional Innovation Fund (NO.2010-13-16-02, 2010-A-26-02)
文摘Although full waveform inversion in the frequency domain can overcome the local minima problem in the time direction, such problem still exists in the space direction because of the media subsurface complexity. Based on the optimal steep descent methods, we present an algorithm which combines the preconditioned bi-conjugated gradient stable method and the multi-grid method to compute the wave propagation and the gradient space. The multiple scale prosperity of the waveform inversion and the multi-grid method can overcome the inverse problems local minima defect and accelerate convergence. The local inhomogeneous three-hole model simulated results and the Marmousi model certify the algorithm effectiveness.
基金financially supported by the National Important and Special Project on Science and Technology(2011ZX05005-005-007HZ)the National Natural Science Foundation of China(No.41274116)
文摘In full waveform inversion （FWI）, Hessian information of the misfit function is of vital importance for accelerating the convergence of the inversion; however, it usually is not feasible to directly calculate the Hessian matrix and its inverse. Although the limited memory Broyden-Fletcher-Goldfarb-Shanno （L-BFGS） or Hessian-free inexact Newton （HFN） methods are able to use approximate Hessian information, the information they collect is limited. The two methods can be interlaced because they are able to provide Hessian information for each other; however, the performance of the hybrid iterative method is dependent on the effective switch between the two methods. We have designed a new scheme to realize the dynamic switch between the two methods based on the decrease ratio （DR） of the misfit function （objective function）, and we propose a modified hybrid iterative optimization method. In the new scheme, we compare the DR of the two methods for a given computational cost, and choose the method with a faster DR. Using these steps, the modified method always implements the most efficient method. The results of Marmousi and overthrust model testings indicate that the convergence with our modified method is significantly faster than that in the L-BFGS method with no loss of inversion quality. Moreover, our modified outperforms the enriched method by a little speedup of the convergence. It also exhibits better efficiency than the HFN method.
基金Sponsored by NSFC(4973415 0 ) and National Defense Prediction F und
文摘In Surface wave waveform inversion, we want to reconstruct 3D shear wave velocity structure, which calculation beyond the capability of the powerful present day personal computer or even workstation. So we designed a high paralleled algorithm and carried out the inversion on Parallel computer based on the partitioned waveform inversion (PWI). It partitions the large scale optimization problem into a number of independent small scale problems and reduces the computational effort by several orders of magnitude. We adopted surface waveform inversion with a equal block(2 o×2 o) discretization.
基金financially supported by the National Natural Science Foundation of China(No.41074075/D0409)the National Science and Technology Major Project(No.2011ZX05025-001-04)
文摘As a high quality seismic imaging method, full waveform inversion （FWI） can accurately reconstruct the physical parameter model for the subsurface medium. However, application of the FWI in seismic data processing is computationally expensive, especially for the three-dimension complex medium inversion. Introducing blended source technology into the frequency-domain FWI can greatly reduce the computational burden and improve the efficiency of the inversion. However, this method has two issues： first, crosstalk noise is caused by interference between the sources involved in the encoding, resulting in an inversion result with some artifacts; second, it is more sensitive to ambient noise compared to conventional FWI, therefore noisy data results in a poor inversion. This paper introduces a frequency-group encoding method to suppress crosstalk noise, and presents a frequency- domain auto-adapting FWI based on source-encoding technology. The conventional FWI method and source-encoding based FWI method are combined using an auto-adapting mechanism. This improvement can both guarantee the quality of the inversion result and maximize the inversion efficiency.
基金supported by the National High-Tech Research and Development Program of China(863 Program)(No.2008AA093001)
文摘Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.
基金supported by National Natural Science Foundation of China (42074157)the National Key Research and Development Program of China (2018YFC0310104)the Strategic Priority Research Program of the Chinese Academy of Science(XDA14010203)。
文摘Reflection-based inversion that aims to reconstruct the low-to-intermediate wavenumbers of the subsurface model, can be a complementary to refraction-data-driven full-waveform inversion(FWI), especially for the deep target area where diving waves cannot be acquired at the surface. Nevertheless, as a typical nonlinear inverse problem, reflection waveform inversion may easily suffer from the cycleskipping issue and have a slow convergence rate, if gradient-based first-order optimization methods are used. To improve the accuracy and convergence rate, we introduce the Hessian operator into reflection traveltime inversion(RTI) and reflection waveform inversion(RWI) in the framework of second-order optimization. A practical two-stage workflow is proposed to build the velocity model, in which Gauss-Newton RTI is first applied to mitigate the cycle-skipping problem and then Gauss-Newton RWI is employed to enhance the model resolution. To make the Gauss-Newton iterations more efficiently and robustly for large-scale applications, we introduce proper preconditioning for the Hessian matrix and design appropriate strategies to reduce the computational costs. The example of a real dataset from East China Sea demonstrates that the cascaded Hessian-based RTI and RWI have good potential to improve velocity model building and seismic imaging, especially for the deep targets.
基金the financial support by the National Natural Science Foundation of China (Nos.41304109 and 41230318)the Fundamental Research Funds for the Central Universities,China University of Geosciences (Wuhan) (Nos.CUG130103 and CUG110803)
文摘Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic mi- gration and/or tomographic inversion methods involving illumination compensation. Vertical cable survey is a potential replacement of traditional marine seismic survey for its flexibility and data quality. Conventional vertical cable data processing requires separation of primaries and multiples before migration. We proposed to use multi-scale full waveform inversion （FWI） to improve illumination coverage of vertical cable survey. A deep water velocity model is built to test the capability of multi-scale FWI in detecting low velocity anomalies below seabed. Synthetic results show that multi-scale FWI is an effective model building tool in deep-water exploration. Geometry optimization through target ori- ented illumination analysis and multi-scale FWI may help to mitigate the risks of vertical cable survey. The combination of multi-scale FWI, low-frequency data and multi-vertical-cable acquisition system may provide both high resolution and high fidelity subsurface models.
基金jointly supported by the National Science and Technology Major Project(Nos.2016ZX05002-005-07HZ,2016ZX05014-001-008HZ,and 2016ZX05026-002-002HZ)National Natural Science Foundation of China(Nos.41720104006 and 41274124)+2 种基金Chinese Academy of Sciences Strategic Pilot Technology Special Project(A)(No.XDA14010303)Shandong Province Innovation Project(No.2017CXGC1602)Independent Innovation(No.17CX05011)。
文摘Full waveform inversion(FWI)is an extremely important velocity-model-building method.However,it involves a large amount of calculation,which hindsers its practical application.The multi-source technology can reduce the number of forward modeling shots during the inversion process,thereby improving the efficiency.However,it introduces crossnoise problems.In this paper,we propose a sparse constrained encoding multi-source FWI method based on K-SVD dictionary learning.The phase encoding technology is introduced to reduce crosstalk noise,whereas the K-SVD dictionary learning method is used to obtain the basis of the transformation according to the characteristics of the inversion results.The multiscale inversion method is adopted to further enhance the stability of FWI.Finally,the synthetic subsag model and the Marmousi model are set to test the effectiveness of the newly proposed method.Analysis of the results suggest the following:(1)The new method can effectively reduce the computational complexity of FWI while ensuring inversion accuracy and stability;(2)The proposed method can be combined with the time-domain multi-scale FWI strategy flexibly to further avoid the local minimum and to improve the stability of inversion,which is of significant importance for the inversion of the complex model.
基金supported by the Joint Fund of Seismological Science(Grant No.U1839206)the National R&D Program on Monitoring,Early Warning and Prevention of Major Natural Disaster(Grant No.2017YFC1500301)+2 种基金supported by IGGCAS Research Start-up Funds(Grant No.E0515402)National Natural Science Foundation of China(Grant No.E1115401)supported by National Natural Science Foundation of China(Grant No.11971258).
文摘The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.This study applies the NAD method to solving three-dimensional(3D)acoustic wave equations in the frequency-domain.This forward modeling approach is then used as the“engine”for implementing 3D frequency-domain full waveform inversion(FWI).In the numerical modeling experiments,synthetic examples are first given to show the superiority of the NAD method in forward modeling compared with traditional finite difference methods.Synthetic 3D frequency-domain FWI experiments are then carried out to examine the effectiveness of the proposed methods.The inversion results show that the NAD method is more suitable than traditional methods,in terms of computational cost and stability,for 3D frequency-domain FWI,and represents an effective approach for inversion of subsurface model structures.
基金supported by the National Natural Science Foundation of China (No. 40774062)
文摘Full waveform inversion（FWI） directly minimizes errors between synthetic and observed data.For the surface acquisition geometry,reflections generated from deep reflectors are sensitive to overburden structure,so it is reasonable to update the macro velocity model in a top-to-bottom manner.For models dominated by horizontally layered structures,combination of offset/time weighting and constant update depth control（CUDC） is sufficient for layer-stripping FWI.CUDC requires ray tracing to determine reflection traveltimes at a constant depth.As model complexity increases,the multi-path effects will have to be considered.We developed a new layer-stripping FWI method utilizing damped seismic reflection data,which does not need CUDC and ray tracing.Numerical examples show that effective update depth（EUD） can be controlled by damping constants even in complex regions and the inversion result is more accurate than conventional methods.
基金National Nature Science Foundation of China (40334040) & Joint Seismological foundation of CEA (101026)
文摘A genetic algorithm of body waveform inversion is presented for better understanding of crustal and upper mantle structures with deep seismic sounding （DSS） waveform data. General reflection and transmission synthetic seismogram algorithm, which is capable of calculating the response of thin alternating high and low velocity layers, is applied as a solution for forward modeling, and the genetic algorithm is used to find the optimal solution of the inverse problem. Numerical tests suggest that the method has the capability of resolving low-velocity layers, thin alternating high and low velocity layers, and noise suppression. Waveform inversion using P-wave records from Zeku, Xiahe and Lintao shots in the seismic wide-angle reflection/refraction survey along northeastern Qinghai-Xizang （Tibeteau） Plateau has revealed fine structures of the bottom of the upper crust and alternating layers in the middle/lower crust and topmost upper mantle.
基金supported by the Major Scientific and Technological Project of PetroChina (ZD2019-183-003)Project of National Natural Science Foundation of China (42074133)+1 种基金the Fundamental Research Funds for the Central Universities (19CX02056A)Project of State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development (33550000-21-FW0399-0009)
文摘The fi rst arrival waveform inversion(FAWI)has a strong nonlinearity due to the objective function using L2 parametrization.When the initial velocity is not accurate,the inversion can easily fall into local minima.In the full waveform inversion method,adding a cross-correlation function to the objective function can eff ectively reduce the nonlinearity of the inversion process.In this paper,the nonlinearity of this process is reduced by introducing the correlation objective function into the FAWI and by deriving the corresponding gradient formula.We then combine the first-arrival wave travel-time tomography with the FAWI to form a set of inversion processes.This paper uses the limited memory Broyden-Fletcher-Goldfarb-Shanno(L-BFGS)algorithm to improve the computational effi ciency of inversion and solve the problem of the low effi ciency of the FAWI method.The overthrust model and fi eld data test show that the method used in this paper can eff ectively reduce the nonlinearity of inversion and improve the inversion calculation effi ciency at the same time.
