Integrating Laboratory Compaction Data With Numerical Fault Models: a Bayesian Framework

When analyzing rock deformation experimental data, one deals with both uncertainty and complexity. Though each part of the problem might be simple, the relationships between them can form a complex system. This often leads to partial or only qualitative data analyses from the experimental rock mechanics community, which limits the impact of these studies in other communities (e.g., modelling). However, it is a perfect case study for graphical models.We present here a Bayesian framework that can be used both to infer the parameters of a constitutive model from rock compaction data, and to simulate porosity reduction within direct fault models from a known (e.g. lab‐derived) constitutive relationship, while keeping track of all the uncertainties. This latter step is crucial if we are to go toward process‐based seismic hazard assessment. Indeed, the rate of effective stress build‐up (namely due to fault compaction) as well as the recovery of fault strength determine how long it will take for different parts ...