Automated time-window selection based on machine learning for full-waveform inversion

Due to increased computational capabilities afforded by modern and future computer architectures, the seismology community is demanding a more comprehensive understanding of full waveform information from recorded seismic data. Full waveform inversion seeks to match observed seismic data with synthesized seismograms by iteratively updating subsurface model parameters. Synthetic data are generated by solving the seismic wave equation using an effective and efficient numerical algorithm. In order to ensure inversion accuracy and stability, both synthesized and observed seismograms must be carefully pre-processed. More specifically, when synthetic and observed data have a large waveform mismatch during the initial iterations, waveforms should be carefully selected for calculating the misfit gradient in order to avoid instability. We introduce a fully automated algorithm based on machine learning (ML) to intelligently select time windows for calculating the misfit between observed and synthetic seismograms. The training dataset can be prepared using time windows obtained based on the FLEXWIN method, in which selection parameters are finely tuned. Results show that automatically selected time windows are of sufficiently high quality compared with the benchmark FLEXWIN method.