Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data

Low-frequency (LF) signal content in seismic data as well as a realistic initial model are key ingredients for robust and efficient full-waveform inversions (FWIs). However, acquiring LF data is challenging in practice for active seismic surveys. Data-driven solutions show promise to extrapolate LF data given a high-frequency counterpart. While being established for synthetic acoustic examples, the application of bandwidth extrapolation to field datasets remains nontrivial. Rather than aiming to reach superior accuracy in bandwidth extrapolation, we propose to jointly reconstruct LF data and a smooth background subsurface model within a multitask deep learning framework. We automatically balance data, model, and trace-wise correlation loss terms in the objective functional and show that this approach improves the extrapolation capability of the network. We also design a pipeline for generating synthetic data suitable for field data applications. Finally, we apply the same trained network to synthetic and real marine streamer datasets and run an elastic FWI from the extrapolated dataset.