Inversion of travel-time data under a statistical model for seismic velocities and layer interfaces

• Published in: GEOPHYSICS
• Main Authors: Bosch, M., Barton, P. J., Singh, S. C., Trinks, I.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2005
• Subjects: 02 - Geodynamics; Geophysics and Tectonics; Northeast Atlantic
• Online Access: http://eprints.esc.cam.ac.uk/1585/; http://eprints.esc.cam.ac.uk/1585/1/Bosch_M._et_al_Geophysics_70,4_%282005%29R33-R43.pdf; https://doi.org/10.1190/1.1993712

We invert large-aperture seismic reflection and refraction data from a geologically complex area on the northeast Atlantic margin to jointly estimate seismic velocities and depths of major interfaces. Our approach combines this geophysical data information with prior information on seismic compressional velocities and the structural interpretation of seismic sections. We constrain expected seismic velocities in the prior model using information from well logs from a nearby area. The layered structure and prior positions of the interfaces follow information from the seismic section obtained by processing the short offsets. Instead of using a conventional regularization technique to smooth the interface-velocity model, we describe the spatial correlation of interfaces and velocities with a geostatistical model, using a multivariate Gaussian probability density function. We impose a covariance function on the velocity field in each layer and on each interface in the model to control the smoothness of the solution. The inversion is performed by minimizing an objective function with two terms, one term measuring traveltime residuals and the other measuring the fit to the statistical model. We calculate the posterior uncertainties and evaluate the relative influence of data and the prior model on estimated interface depths and seismic velocities. The method results in the estimation of velocity and interface geometry beneath a basaltic sill system down to 7 km depth. This method aims to enhance the interpretation process by combining multidisciplinary information in a quantitative model-based approach.