A nonlinear approach to assess network performance for moment-tensor studies of long-period signals in volcanic settings
© The Author(s) 2018. The characterization of seismic source mechanisms and geometries is of critical importance for understanding the underlying causes and physical processes of low-frequency seismicity at volcanoes. Because observational data are often limited in volcanic environments by logistical constraints, we use synthetic modelling to investigate the capability of seismic networks to properly resolve source mechanisms. For 16 synthetic networkswith as many as 40 stations, and variable distance and azimuthal distributions, we perform nonlinear moment-tensor inversion for six input source models. Using a grid search for source type and constrained waveform inversions provides a quantitative measure of source mechanism reliability. If the source location is assumed to be correct, results suggest that complete azimuthal coverage, with stations located at different distance ranges from the source will allow for a higher resolution recovery of the source-time function. In general, a similar degree of uncertainty characterizes configurations with as many as 40 stations and as few as eight stations. Although the level of uncertainty in the source-time function increases when fewer than eight stations are used in the inversions, sources are still recoverable when as few as four stations are used. Deviations from this general trend are present across the different input source models, with performance of configurations with fewer than eight stations showing a strong dependence on the source type.
Geophysical Journal International
A nonlinear approach to assess network performance for moment-tensor studies of long-period signals in volcanic settings.
Geophysical Journal International,
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