Least-Squares Seismic Inversion with Stochastic Conjugate Gradient Method

With the development of computational power,there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image,such as full-waveform inversion and least squares migration.However,though more advanced than conventional methods,these data fitting methods can be very expensive in terms of computational cost.Recently,various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency.In this study,we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems.We first prescribe the basic theory of our method and then give synthetic examples.Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.