High-Resolution Regularized Elastic Full Waveform Inversion Assisted by Deep Learning

Abstract

Elastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface. However, in practice, the resolution and illumination of EFWI are limited by the bandwidth and aperture of seismic data. The often-present wells in developed fields as well as some exploratory regions can provide a complementary illumination to the target area. We, thus, introduce a regularization technique, which combines the surface seismic and well log data, to help improve the resolution of EFWI. Using deep fully connected layers, we train our neural network to identify the relation between the means and variances at the well, with the inverted model from an initial EFWI application. The network is used to map the means and variances extracted from the well to the whole model domain. We then perform another EFWI in which we fit the predicted data to the observed one as well as fit the model over a Gaussian window to the predicted means the variances. The tests on the synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and illumination of deep-buried reservoirs, which are less illuminated by seismic data.