Modern geodetic techniques provide valuable and near real time observations of volcanic activity. Characterizing the source of deformation based on these observations has become of major importance in related monitoring efforts. We investigate two Random Search approaches, Simulated Annealing (SA) and Genetic Algorithm (GA), and utilize them in an iterated manner. The iterated approach helps to prevent GA in general and SA in particular from getting trapped in local minima and it also increases redundancy for exploring the search space. We apply a statistical competency test for estimating the confidence interval of the inversion source parameters, considering their internal interaction through the model, the effect of the model deficiency, as well as the observational error.

These inversion methods allow derivation of deformation source parameters and their associated quality. Given the confident source parameters, one can estimate other parameters such as stress field.

For theoretical background see the following publications:

• Shirzaei, M.; Walter, T. R. Randomly iterated search and statistical competency as powerful inversion tools for deformation source modeling: Application to volcano interferometric synthetic aperture radar data In: Journal of Geophysical Research, 114, B10401 2009. 10.1029/2008JB006071
• Shirzaei, M.; Walter, T. R. Time-dependent volcano source monitoring using interferometric synthetic aperture radar time series: A combined genetic algorithm and Kalman filter approach In: Journal of Geophysical Research, 115, B10421 2010. published 28 October 2010 10.1029/2010JB007476