Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series

TL;DR

This paper introduces a novel particle filter driven by optical flow and level set methods to track the evolving salt boundary in X-ray CT time-series, integrating physical modeling with stochastic image processing.

Contribution

It presents a new combined approach using level set-based particle filtering driven by optical flow for non-linear deformation tracking in CT images.

Findings

Successfully tracked salt boundary deformation over time.

Demonstrated the effectiveness of the method on CT image sequences.

Enhanced understanding of salt structure evolution under stress.

Abstract

Image-based computational fluid dynamics have long played an important role in leveraging knowledge and understanding of several physical phenomena. In particular, probabilistic computational methods have opened the way to modelling the complex dynamics of systems in purely random turbulent motion. In the field of structural geology, a better understanding of the deformation and stress state both within the salt and the surrounding rocks is of great interest to characterize all kinds of subsurface long-terms energy-storage systems. The objective of this research is to determine the non-linear deformation of the salt boundary over time using a parallelized, stochastic filtering approach from x-ray computed tomography (CT) image time series depicting the evolution of salt structures triggered by gravity and under differential loading. This work represents a first step towards bringing together physical modeling and advanced stochastic image processing methods where model uncertainty is taken into account.