The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, ...The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, where individual errors ei quantify misfit between predictions and observations, to quantify goodness of fit. So-called adjoint state methods allow the gradient ∂E/∂mi to be computed extremely efficiently from an adjoint field, facilitating image reconstruction by gradient-descent methods. We examine the structure of the differential equation for the adjoint field under the ray approximation and find that it has the same form as the transport equation, whose solution involves the well-known geometrical spreading function R Consequently, as R is routinely tabulated as part of a ray calculation, no extra work is needed to compute the adjoint field, permitting a rapid calculation of the gradient?∂E/∂mi.展开更多
The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem ar...The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem are closely related to the classical discrete Wardrop’s equilibrium.We propose an adjoint state method to numerically approximate continuous traffic congestion equilibria through the continuous formulation.The method formally derives an adjoint state equation to compute the gradient descent direction so as to minimize a nonlinear functional involving the equilibrium metric and the resulting geodesic distances.The geodesic distance needed for the state equation is computed by solving a factored eikonal equation,and the adjoint state equation is solved by a fast sweeping method.Numerical examples demonstrate that the proposed adjoint state method produces desired equilibrium metrics and outperforms the subgradient marching method for computing such equilibrium metrics.展开更多
最小二乘逆时偏移(Least-Square Reverse Time Migration,LSRTM)相比于常规偏移具有更高的成像分辨率、振幅保幅性及均衡性等优势,是当前研究的热点之一.然而,目前LSRTM算法大多是基于二阶常密度标量声波方程建立的,忽略了密度变化对振...最小二乘逆时偏移(Least-Square Reverse Time Migration,LSRTM)相比于常规偏移具有更高的成像分辨率、振幅保幅性及均衡性等优势,是当前研究的热点之一.然而,目前LSRTM算法大多是基于二阶常密度标量声波方程建立的,忽略了密度变化对振幅的影响,因而基于振幅匹配策略的常规LSRTM很难在变密度介质下取得保真的成像结果.一阶速度-应力方程能够很好地处理变密度介质,但简单地将一阶速度-应力方程应用到LSRTM中缺乏理论基础.为此,本文从LSRTM的正问题入手,提出了基于交错网格的一阶速度-应力方程LSRTM理论方法.首先将一阶波动方程线性化,建立了一阶方程LSRTM的目标泛函,随后推导其伴随方程,并借助伴随状态法给出了迭代更新流程,最终建立了基于一阶速度-应力方程LSRTM的理论框架.进一步,通过在相位编码LSRTM中引入随机最优化思想,极大地减小了计算量、提高了计算效率.最后,通过模型试算验证了本算法的正确性和有效性.展开更多
文摘The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, where individual errors ei quantify misfit between predictions and observations, to quantify goodness of fit. So-called adjoint state methods allow the gradient ∂E/∂mi to be computed extremely efficiently from an adjoint field, facilitating image reconstruction by gradient-descent methods. We examine the structure of the differential equation for the adjoint field under the ray approximation and find that it has the same form as the transport equation, whose solution involves the well-known geometrical spreading function R Consequently, as R is routinely tabulated as part of a ray calculation, no extra work is needed to compute the adjoint field, permitting a rapid calculation of the gradient?∂E/∂mi.
基金supported by NSF 0810104 and NSF 1115363Leung was supported in part by Hong Kong RGC under Grant GRF603011HKUST RPC under Grant RPC11SC06.
文摘The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem are closely related to the classical discrete Wardrop’s equilibrium.We propose an adjoint state method to numerically approximate continuous traffic congestion equilibria through the continuous formulation.The method formally derives an adjoint state equation to compute the gradient descent direction so as to minimize a nonlinear functional involving the equilibrium metric and the resulting geodesic distances.The geodesic distance needed for the state equation is computed by solving a factored eikonal equation,and the adjoint state equation is solved by a fast sweeping method.Numerical examples demonstrate that the proposed adjoint state method produces desired equilibrium metrics and outperforms the subgradient marching method for computing such equilibrium metrics.
文摘最小二乘逆时偏移(Least-Square Reverse Time Migration,LSRTM)相比于常规偏移具有更高的成像分辨率、振幅保幅性及均衡性等优势,是当前研究的热点之一.然而,目前LSRTM算法大多是基于二阶常密度标量声波方程建立的,忽略了密度变化对振幅的影响,因而基于振幅匹配策略的常规LSRTM很难在变密度介质下取得保真的成像结果.一阶速度-应力方程能够很好地处理变密度介质,但简单地将一阶速度-应力方程应用到LSRTM中缺乏理论基础.为此,本文从LSRTM的正问题入手,提出了基于交错网格的一阶速度-应力方程LSRTM理论方法.首先将一阶波动方程线性化,建立了一阶方程LSRTM的目标泛函,随后推导其伴随方程,并借助伴随状态法给出了迭代更新流程,最终建立了基于一阶速度-应力方程LSRTM的理论框架.进一步,通过在相位编码LSRTM中引入随机最优化思想,极大地减小了计算量、提高了计算效率.最后,通过模型试算验证了本算法的正确性和有效性.
