In this paper, we detail the combination of the genetic algorithm (GA) inversion technique with the elastic-gravitational model originally developed by Rundle and subsequently refined by Ferna´ndez and others. A sensitivity analysis is performed for the joint inversion of deformation and gravity to each of the model parameters, illustrating the importance of proper identification of both the strengths and limitations of any source model inversion, and this technique in particular. There is a practical comparison of the theoretical results with the inversion of geodetic data observed at the Mayon volcano in the Philippines, where there are gravity changes without significant deformation, after the 1993 eruption.

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In this work we review the development of both established and innovative analytical techniques using numerical simulations of the southern California fault system and demonstrate the viability of these methods with examples using actual data. The ultimate goal of these methods is to better understand how the surface of the Earth is changing on both long-and short-term time scales, and to use the resulting information to learn about the internal processes in the underlying crust and to predict future changes in the deformation and stress field. Three examples of the analysis and visualization techniques are discussed in this paper and include the Karhunen-Loeve (KL) decomposition technique, local Ginsberg criteria (LGC) analysis, and phase dynamical probability change (PDPC). Examples of the potential results from these methods are provided through their application to data from the Southern California Integrated GPS Network (SCIGN), historic seismicity data, and simulated InSAR data, respectively. These analyses, coupled with advances in modeling and simulation, will provide the capability to track changes in deformation and stress through time, and to relate these to the development of space-time correlations and patterns.