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arXiv · 1105.4263

The Adjoint Method Applied to Time-Distance Helioseismology

Shravan Hanasoge·Aaron Birch·Laurent Gizon·Jeroen Tromp
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Abstract

For a given {\it misfit function}, a specified optimality measure of a model, its gradient describes the manner in which one may alter properties of the system to march towards a stationary point. The adjoint method, arising from partial-differential-equation-constrained optimization, describes a means of extracting derivatives of a misfit function with respect to model parameters through finite computation. It relies on the accurate calculation of wavefields that are driven by two types of sources, namely the average wave-excitation spectrum, resulting in the {\it forward wavefield}, and differences between predictions and observations, resulting in an {\it adjoint wavefield}. All sensitivity kernels relevant to a given measurement emerge directly from the evaluation of an interaction integral involving these wavefields. The technique facilitates computation of sensitivity kernels (Fr\'{e}chet derivatives) relative to three-dimensional heterogeneous background models, thereby paving the way for non-linear iterative inversions. An algorithm to perform such inversions using as many observations as desired is discussed.

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Shravan Hanasoge, Aaron Birch, Laurent Gizon, Jeroen Tromp. 2011-05-21. The Adjoint Method Applied to Time-Distance Helioseismology. https://doi.org/10.1088/0004-637x/738/1/100

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