An Empirical Analysis of the Influence of Seismic Data Modeling for Estimating Velocity Models with Fully Convolutional Networks

Abstract

Seismic modeling is the process of simulating wave propagations in a medium to represent underlying structures of a subsurface area of the earth. This modeling is based on a set of parameters that determine how the data is produced. Recent studies have demonstrated that deep learning methods can be trained with seismic data to estimate velocity models that give a representation of the subsurface where the seismic data was generated. Thus, an analysis is made on the impact that different sets of parameters have on the estimation of velocity models by a fully convolutional network (FCN). The experiments varied the number of sources among four options (1, 10, 25 or 50 shots) and used three different ranges of peak frequencies: 4, 8 and 16 Hz. The results demonstrated that, although the number of sources have more influence on the computational time needed to train the FCN than the peak frequency, both changes have significant impact on the quality of the estimation. The best estimations were obtained with the experiment of 25 sources with 4 Hz and increasing the peak frequency to 8 Hz improved even more the results, especially regarding the FCN's loss function.

Keywords

• velocity model estimation
• deep learning
• fully convolutional networks
• seismic data analysis
• geophysics