Regional Moment Tensor Source-Type Discrimination Sensitivity Analysis

Abstract:

Waveform inversion to determine the seismic moment tensor is a standard approach in determining the source mechanism of natural and manmade seismicity, and can be used to identify, or discriminate different types of seismic sources such as explosions, collapses, earthquakes, and geothermal and volcanic events. The most-trusted approach to compute Green's functions, especially in areas of low-seismicity where high-resolution velocity models are not available, is through waveform modeling of regional earthquakes to produce a 1D velocity model. However, the 1D velocity model assumption is the greatest source of error in the moment tensor solution and has never been thoroughly investigated for source-type discrimination. We propose to determine the effect of a 1D velocity model assumption as a function of data quality, passband, and sensor configuration through a synthetic study, as well as through application to Western US events where we have well-calibrated moment tensor solutions. In the synthetic study we will produce waveforms with the effects of 3D velocity heterogeneity. The effect of the 1D velocity model assumption can then be interpreted via the difference between input and inverted solutions. By varying the heterogeneity strength, passband, signal-to-noise, and station configuration, we can derive error covariance matrices for various recording configurations. These matrices can then be used to calculate moment-tensor-derived confidence for use in discriminant analysis