81st EAGE Conference and Exhibition 2019 2019
DOI: 10.3997/2214-4609.201900645
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First-Arrival Traveltime Tomography Based on the Adjoint State Method with Independence of Surface Normal Vectors

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“…According to the surface normal vectors–independent adjoint‐state traveltime tomography method (Han et al, 2019), the ASJT objective function can be defined as J(v)=ω1JTfalse(boldvfalse)+ω2JRfalse(boldvfalse)=ω12normalΩ()tTboldxtTobsboldx2δx,g+ω22normalΩ()tRboldxtRobsboldx2δx,g,$$\begin{eqnarray} J(\mathbf{v}) &=& {\omega}_{1}{J}_{T}(\mathbf{v})+{\omega}_{2}{J}_{R}(\mathbf{v})=\frac{{\omega}_{1}}{2}\underset{\mathrm{\Omega}}{\int}{\left({t}_{T}\left(\mathbf{x}\right)-{t}_{T}^{\textit{obs}}\left(\mathbf{x}\right)\right)}^{2}{\delta}_{\mathbf{x},\mathbf{g}}\nonumber\\ &&+\,\frac{{\omega}_{2}}{2}\underset{\mathrm{\Omega}}{\int}{\left({t}_{R}\left(\mathbf{x}\right)-{t}_{R}^{\textit{obs}}\left(\mathbf{x}\right)\right)}^{2}{\delta}_{\mathbf{x},\mathbf{g}}, \end{eqnarray}$$…”
Section: Theory and Methodsmentioning
confidence: 99%
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“…According to the surface normal vectors–independent adjoint‐state traveltime tomography method (Han et al, 2019), the ASJT objective function can be defined as J(v)=ω1JTfalse(boldvfalse)+ω2JRfalse(boldvfalse)=ω12normalΩ()tTboldxtTobsboldx2δx,g+ω22normalΩ()tRboldxtRobsboldx2δx,g,$$\begin{eqnarray} J(\mathbf{v}) &=& {\omega}_{1}{J}_{T}(\mathbf{v})+{\omega}_{2}{J}_{R}(\mathbf{v})=\frac{{\omega}_{1}}{2}\underset{\mathrm{\Omega}}{\int}{\left({t}_{T}\left(\mathbf{x}\right)-{t}_{T}^{\textit{obs}}\left(\mathbf{x}\right)\right)}^{2}{\delta}_{\mathbf{x},\mathbf{g}}\nonumber\\ &&+\,\frac{{\omega}_{2}}{2}\underset{\mathrm{\Omega}}{\int}{\left({t}_{R}\left(\mathbf{x}\right)-{t}_{R}^{\textit{obs}}\left(\mathbf{x}\right)\right)}^{2}{\delta}_{\mathbf{x},\mathbf{g}}, \end{eqnarray}$$…”
Section: Theory and Methodsmentioning
confidence: 99%
“…The corresponding adjoint Equation () for the calculation of the adjoint‐state variables λT${\lambda}_{T}$ and λR${\lambda}_{R}$ is an extension from adjoint‐state transmission traveltime inversion (Han et al., 2019), which can be derived by the following perturbation theory (Nocedal & Wright, 2006): {·λT()xtT=tT()xtTobs()xδx,g·()λRgboldxtR=()tRboldxtRobsboldxδboldx,boldg·()λRsboldxtT=λRg()xδboldx,xr.$$\begin{equation} \left\{ \def\eqcellsep{&}\begin{array}{c}\nabla \cdot \left({\lambda}_{T}\left(\mathbf{x}\right)\nabla {t}_{T}\right)=\left({t}_{T}\left(\mathbf{x}\right)-{t}_{T}^{\textit{obs}}\left(\mathbf{x}\right)\right){\delta}_{\mathbf{x},\mathbf{g}}\\ \def\eqcellsep{&}\begin{array}{c}{\nabla \cdot \left({\lambda}_{R}^{g}\left(\mathbf{x}\right)\nabla {t}_{R}\right)=\left({t}_{R}\left(\mathbf{x}\right)-{t}_{R}^{\textit{obs}}\left(\mathbf{x}\right)\right){\delta}_{\mathbf{x},\mathbf{g}}}\\ {\nabla \cdot \left({\lambda}_{R}^{s}\left(\mathbf{x}\right)\nabla {t}_{T}\right)={\lambda}_{R}^{g}\left(\mathbf{x}\right){\delta}_{\mathbf{x},{\mathbf{x}}_{\mathbf{r}}}}\end{array} \end{array} \right.. \end{equation}$$…”
Section: Theory and Methodsmentioning
confidence: 99%
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