: 22nd International Conference on Computational Science and Its Applications, ICCSA 2022. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Malaga, Spain, July 4-7, 2022)
: 22nd International Conference on Computational Science and Its Applications, ICCSA 2022. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Malaga, Spain, July 4-7, 2022)
We present an approach to construct the training dataset for the numerical dispersion mitigation network (NDM-net). The network is designed to suppress numerical error in the simulated seismic wavefield. The training dataset is the wavefield simulated using a fine grid, thus almost free from the numerical dispersion. Generation of the training dataset is the most computationally intense part of the algorithm, thus it is important to reduce the number of seismograms used in the training dataset to improve the efficiency of the NDM-net. In this work, we introduce the discrepancy between seismograms and construct the dataset, so that the discrepancy between the dataset and any seismogram is below the prescribed level.
