Smart pilot points using reversible‐jump Markov‐chain Monte Carlo. Issue 5 (26th May 2016) - Jiménez, S.
Abstract: Pilot points are typical means for calibration of highly parameterized numerical models. We propose a novel procedure based on estimating not only the pilot point values, but also their number and suitable locations. This is accomplished by a trans‐dimensional Bayesian inversion procedure that also allows for uncertainty quantification. The utilized algorithm, reversible‐jump Markov‐Chain Monte Carlo (RJ‐MCMC), is computationally demanding and this challenges the application for model calibration. We present a solution for fast, approximate simulation through the application of a Bayesian inversion. A fast pathfinding algorithm is used to estimate tracer travel times instead of doing a full transport simulation. This approach extracts the information from measured breakthrough curves, which is crucial for the reconstruction of aquifer heterogeneity. As a result, the "smart pilot points" can be tuned during thousands of rapid model evaluations. This is demonstrated for both a synthetic and a field application. For the selected synthetic layered aquifer, two different hydrofacies are reconstructed. For the field investigation, multiple fluorescent tracers were injected in different well screens in a shallow alluvial aquifer and monitored in a tomographic source‐receiver configuration. With the new inversion procedure, a sand layer was identified and reconstructed with a high spatial resolution in 3‐D. The sand layer was successfully validated through additional slug tests at the site. The promising results encourage further applications in hydrogeological model calibration, especially for cases with simulation of transport. Key Points: An inversion with flexible pilot points settings is presented The inversion is validated with a tracer tomography field study Pathfinding algorithm approximates transport behavior of tracers