Deep learning framework for true amplitude imaging: Effect of conditioners and initial models

Abstract

ABSTRACTWe propose a workflow to correct migration amplitudes by estimating the inverse Hessian operator weights using a neural network–based framework. We train the network such that it learns the transformation between the migration output and true amplitude reflectivity constrained by different conditioners. We analyse the network output with a velocity model and with source illumination as a conditioner. Compared to the velocity model, source illumination as a conditioner performs better because source illumination encodes the geometrical spreading information and accounts for non‐stationarity. We further use the output of the deep neural network as a starting model for accelerating the convergence of iterative least‐squares reverse time migration. Using a deep learning framework, the proposed method combines the model domain and data domain least‐squares migration approaches to recover images with interpretable amplitudes, attenuated migration artefacts, better signal‐to‐noise ratio and improved resolution. We compare the output of the proposed algorithm with conventional least‐squares output and show that the proposed workflow is more robust, especially in the areas with weak illumination.