BibTeX
@ARTICLE{
         Cao2015Acm,
       author = "Danping Cao and Wenyuan Liao",
       title = "A computational method for full waveform inversion of crosswell seismic data using
         automatic differentiation",
       journal = "Computer Physics Communications",
       volume = "188",
       pages = "47--58",
       year = "2015",
       issn = "0010-4655",
       doi = "10.1016/j.cpc.2014.11.002",
       url = "http://www.sciencedirect.com/science/article/pii/S0010465514003725",
       keywords = "Full waveform inversion, Adjoint state method, Automatic differentiation, Numerical
         optimization, Inverse problem, Crosswell seismic data",
       abstract = "Abstract Full waveform inversion (FWI) is a model-based data-fitting technique that
         has been widely used to estimate model parameters in Geophysics. In this work, we propose an
         efficient computational approach to solve the \{FWI\} of crosswell seismic data. The
         \{FWI\} problem is mathematically formulated as a partial differential equation
         (PDE)-constrained optimization problem, which is numerically solved using a gradient-based
         optimization method. The efficiency and accuracy of \{FWI\} are mainly determined by the
         three main components: forward modeling, gradient calculation and model update which usually
         involves the gradient-based optimization algorithm. Given the large number of iterations needed by
         FWI, an accurate gradient is critical for the success of FWI, as it will not only speed up the
         convergence but also increase the accuracy of the solution. However computing the gradient still
         remains a challenging task even after the adjoint \{PDE\} has been derived. Automatic
         differentiation (AD) tools have been proved very effective in a variety of application areas
         including Geoscience. In this work we investigated the feasibility of integrating TAPENADE, a
         powerful \{AD\} tool into FWI, so that the \{FWI\} workflow is simplified to
         allow us to focus on the forward modeling and the model updating. In this paper we choose the
         limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method due to
         its robustness and fast convergence. Numerical experiments have been conducted to demonstrate the
         effectiveness, efficiency and robustness of the new computational approach for FWI.",
       ad_area = "Geophysics",
       ad_tools = "TAPENADE"
}