Study of Volcanic Sources at Long Valley Caldera, California, Using Gravity Data and a Genetic Algorithm Inversion Technique

Abstract

We model the source inflation of the Long Valley Caldera, California, using a genetic algorithm technique and micro-gravity data. While there have been numerous attempts to model the magma injection at Long Valley Caldera from deformation data, this has proven difficult given the complicated spatial and temporal nature of the volcanic source. Recent work illustrates the effectiveness of considering micro-gravity measurements in volcanic areas. A genetic algorithm is a problem-solving technique which combines genetic and prescribed random information exchange. We perform two inversions, one for a single spherical point source and another for two-sources that might represent a more spatially distributed source. The forward model we use to interpret the results is the elastic-gravitational Earth model which takes into account the source mass and its interaction with the gravity field. The results demonstrate the need to incorporate more variations in the model, including another source geometry and the faulting mechanism. In order to provide better constraints on intrusion volumes, future work should include the joint inversion of gravity and deformation data during the same epoch.