HR: 0800h
AN: S41A-0931    [Abstracts]
TI: Integrating Laboratory Compaction Data With Numerical Fault Models: a Bayesian Framework
AU: * Fitzenz, D D
EM: fitzenz@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road MS 977, Menlo Park, CA 94025
AU: Jalobeanu, A
EM: ajalobea@riacs.edu
AF: USRA/RIACS, NASA Ames Research Center, Moffet Field, CA 94035-1000
AU: Hickman, S H
EM: hickman@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road MS 977, Menlo Park, CA 94025
AB: Both the recurrence times and the potential sizes of earthquakes on a fault are crucial ingredients of seismic hazard assessment. The recovery of fault strength as well as the rate of effective stress build-up after a large earthquake depend on the post-seismic time-evolutions of the hydraulic, frictional, rheological, and poroelastic properties of the healing fault zone. These interrelated physical and chemical processes determine how long it will take for different parts of the previously ruptured fault to reach failure again, thus controlling both the timing and the size of the next rupture. To further explore this phenomenon, we are conducting forward modelling of the hydraulic properties of fault zones during the interseismic period using a bayesian methodology. This approach uses lab-derived compaction laws and their uncertainties to calculate porosity as a function of time. This allows us to determine the statistical robustness of the results of process-based deterministic fault models utilizing experimental uncertainties in both the input parameters and the constitutive relationships. We also develop an inverse method using repeated measurements or estimations of porosity and/or pore pressure in laboratory or field fault zones to derive the constitutive relationships, parameters and uncertainties controlling pore pressure evolution in faults. In this approach, which is complimentary to the forward modelling approach, the bayesian framework allows us to make use of all available prior knowledge (e.g., lithology, permeability and porosity, as well as spatial heterogeneity in these parameters) and to take into account what we know about the data acquisition. Our approach is limited by the fact that existing experimental data are rarely adequate to completely define a single constitutive relationship for a given fault gouge mineralogy and grain size distribution over temperature and effective confining pressures of relevance to actual fault zones. We therefore focus on one experimentally derived compaction law, and emphasize what pieces of information are critical to perform both the forward and the inverse approach.
DE: 8010 Fractures and faults
DE: 8045 Role of fluids
DE: 8159 Rheology--crust and lithosphere
DE: 7209 Earthquake dynamics and mechanics
DE: 3260 Inverse theory
SC: Seismology [S]
MN: 2004 AGU Fall Meeting