← Back to Sessions

Time-variable Moment Tensor Inversion Applied to Seismic Data Collected at the PE1-A Experiment

Description: 

Time-variable moment tensor inversions recover moment function models that capture the evolution of source characteristics instead of a static moment tensor matrix. These inversion formulations are able to better categorize complex sources that change through time, allowing for an estimate of the moment functions for each moment tensor component without requiring a priori information regarding the type of source time function. This means that a complex source, such as strike-slip motion on a fault followed by cracking, can be represented in the order these source mechanisms occurred in time, and therefore not subsumed into a scalar representation that might miss one or both aspects of the changing source, as is typically done in traditional, static moment tensor inversions.

We apply our time-variable moment tensor inversion to seismic data recorded as part of the Physics Experiment 1-A (PE1-A). We use various three-component seismic station configurations, as well as filter the seismic data using two different frequency bands to evaluate the performance of our time-variable moment tensor inversion with differing frequency content and recording geometries. Comparison between the recovered moment function results using data filtered between different frequency bands, as well as seismic data from 3 - 21 three-component stations, leads to similar recovered source mechanisms. However, when including fewer stations in the time-variable moment tensor inversions, differing isotropic moments were recovered compared to inversions using data with higher station coverage.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Session: Advancements in Forensic Seismology and Explosion Monitoring [Poster]

Type: Poster

Date: 4/17/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Andréa

Student Presenter: No

Invited Presentation: 

Poster Number: 85

Authors