Uncertainties in Ray-Tracing Tomography Models Used for Sub-Basalt Seismic Imaging

Sanford, O.G.; Hobbs, R.W.; Brown, R.J.; Schofield, N.

Abstract

Sequences of buried basalt lavas found along continental margins may be associated with hydrocarbon deposits. However, they have heterogeneous velocity and density structures that degrade seismic imaging of basalt and sub-basalt structures and make them difficult to interpret. Hence, ray-tracing tomography is commonly performed on long-offset data to provide a velocity model as an aid, for advanced processing such as pre-stack migration, or as a starting point for full-waveform inversion. This is based on the assumption that at low-frequencies, the basalt sequence may be approximated by a single layer with a simple velocity gradient, which generates a turning wave refraction whose travel-time can be inverted to give both the velocity and thickness of the basalt layer. In this study, we generate synthetic long-offset data from a geologically constrained basalt model and perform subsequent ray-tracing inversion to provide an insight into the uncertainties that may be present by following this tomographic approach. By using Monte-Carlo analysis, we show that the thickness of the basalt layer may be under or over-estimated when compared to the true thickness, indicating that simple ray-tracing based on the assumption of turning rays is not appropriate when considering long-offset refractions from data over regions affected by extrusive basalt sequences.

Citation

Sanford, O., Hobbs, R., Brown, R., & Schofield, N. (2023). Uncertainties in Ray-Tracing Tomography Models Used for Sub-Basalt Seismic Imaging. Pure and Applied Geophysics, 180(1), 145-156. https://doi.org/10.1007/s00024-022-03199-2

• Journal Article Type: Article
• Acceptance Date: Nov 24, 2022
• Online Publication Date: Dec 5, 2022
• Publication Date: 2023-01
• Deposit Date: Jan 13, 2023
• Publicly Available Date: Dec 6, 2023
• Journal: Pure and Applied Geophysics
• Print ISSN: 0033-4553
• Electronic ISSN: 1420-9136
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 180
• Issue: 1
• Pages: 145-156
• DOI: https://doi.org/10.1007/s00024-022-03199-2
• Public URL: https://durham-repository.worktribe.com/output/1183062