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In this work, we illustrate an example of estimating the macro-model of velocities in the subsurface through the use of global optimization methods (GOMs). The optimization problem is solved using DEAP (Distributed Evolutionary Algorithms in Python) and Devito, python frameworks for evolutionary and automated finite difference computations, respectively. We implement a Particle swarm optimization (PSO) with an elitism strategy on top of DEAP, leveraging its transparent, simple and coherent environment for implementing of evolutionary algorithms (EAs). The high computational effort, due to the huge number of cost function evaluations (each one demanding a forward modeling step) required by PSO, is alleviated through the use of Devito as well as through parallelization with Dask. The combined use of these frameworks yields not only an efficient way of providing acoustic macro models of the P-wave velocity field (Vp), but also significantly reduces the amount of human effort in fulfilling this task.
The Hessian matrix plays an important role in correct interpretation of the multiple scattered wave fields inside the FWI frame work. Due to the high computational costs, the computation of the Hessian matrix is not feasible. Consequently, FWI produc
We describe a novel framework for estimating subsurface properties, such as rock permeability and porosity, from time-lapse observed seismic data by coupling full-waveform inversion, subsurface flow processes, and rock physics models. For the inverse
In this article, continuous Galerkin finite elements are applied to perform full waveform inversion (FWI) for seismic velocity model building. A time-domain FWI approach is detailed that uses meshes composed of variably sized triangular elements to d
Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoti
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown ex