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. 2023 Jan 12;13:630. doi: 10.1038/s41598-023-27738-6

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The top panel (A) shows the results from a numerical simulation directly solving the elastic wave equation with a previously unseen sample model from population L. Panel (B) shows the seismogram prediction results from the FNO network (previously trained with surface seismic data only) when presented with the same layered model as in panel (A). Panel (C) show the difference between the simulation traces and the predicted FNO network outputs. Again the overall match is very good with all main arrivals captured. There are some discrepancies associated with small amplitude scattering. Some edge artefacts from the training set-up can also be observed. The bottom panel (D) shows three simulated traces compared against the FNO predictions, demonstrating a very good match in the main.