Journey to Time-Variable Moment Tensors through Inversion of Acoustic and Seismoacoustic Data
We explore the capability of acoustic and seismoacoustic datasets to directly resolve a complex, time-variable source consisting of a buried mechanism, represented as a moment tensor, and a spall mechanism, represented as a vertical force at the surface. Traditionally, each component of a resolved moment tensor assumes one underlying source time function, which likely fails to capture the full evolution of a dynamic source, such as an explosion followed by slip on near-source joints or development of spallation. Specifically, we expand previous work to resolve a time-variable moment tensor using single-modality and joint-modality inversion frameworks through analysis of infrasound and seismoacoustic data recorded as part of the Source Physics Experiment Phase II: Dry Alluvium Geology (DAG). We investigate the impact of including signals from seismic-to-air coupling that are local to each infrasound sensor in comparison to mainly atmosphere-propagating acoustic signals, which occur from coupling of the wavefield from the subsurface to the atmosphere directly above the source. Additionally, we assess the ability of our inversion algorithm to fit observed infrasound data using a variety of time-variable source mechanisms. First, we consider the buried moment tensor source alone, which assumes that the determined Green’s functions incorporate effects from spallation or that the impact from spallation is minimal. Second, we examine the estimated buried moment tensor and vertical surface spallation as terms that must both be resolved in the inversion. Third, we assess the ability for an estimated vertical surface spallation source to fit the acoustic data on its own. Finally, we compare results from the joint inversion of both seismic geophone and infrasound acoustic data for the buried-only source compared to buried and spallation sources. Our results are a preliminary investigation into the applications of the inversion technique to recorded datasets and show the technique has limited capabilities using acoustic data alone. Instead, this method shows promise for seismic and seismoacoustic datasets to resolve the time-variable mechanisms of a buried source.