Stochastic estimation of aquifer geometry using seismic refraction data with borehole depth constraints

Abstract

We develop a Bayesian model to invert surface seismic refraction data with depth constraints from boreholes for characterization of aquifer geometry and apply it to seismic and borehole data sets collected at the contaminated Oak Ridge National Laboratory site in Tennessee. Rather than the traditional approach of first inverting the seismic arrival times for seismic velocity and then using that information to aid in the spatial interpolation of wellbore data, we jointly invert seismic first arrival time data and wellbore‐based information, such as depths of key lithological boundaries. We use a staggered‐grid finite difference algorithm with second‐order accuracy in time and fourth‐order accuracy in space to model seismic full waveforms and use an automated method to pick the first arrival times. We use Markov Chain Monte Carlo methods to draw many samples from the joint posterior probability distribution, on which we can estimate the key interfaces and their associated uncertainty as a function of horizontal location and depth. We test the developed method on both synthetic and field case studies. The synthetic studies show that the developed method is effective at rigorous incorporation of multiscale data and the Bayesian inversion reduces uncertainty in estimates of aquifer zonation. Applications of the approach to field data, including two surface seismic profiles located 620 m apart from each other, reveal the presence of a low‐velocity subsurface zone that is laterally persistent. This geophysically defined feature is aligned with the plume axis, suggesting it may serve as an important regional preferential flow pathway.