Toward Improving Point-Source Moment-Tensor Inference by Incorporating 1D Earth Model's Uncertainty: Implications for the Long Valley Caldera Earthquakes

Abstract

To infer seismic moment tensor (MT) of moderate earthquakes at regional scales, seismologists typically simulate waveforms using available structural models of the Earth to match the observed seismograms. This procedure is known as waveform seismic MT inversion. However, the seismic data are noisy, and the Earth model is inevitably different from the actual structure of the Earth; hence, there is a discrepancy between the predicted and the observed waveforms. This discrepancy arises from the noise in the data and imperfections in theoretical predictions, stemming most significantly from the Earth model. This study introduces structural uncertainty, estimated empirically, and referred to as “theory uncertainty,” as part of the combined covariance matrix. In the synthetic setting, we first show through a series of synthetic experiments that the structural uncertainty plays a critical role in retrieving MT solutions, especially for short-period waveforms. The method is then benchmarked against the waveforms of non-double-couple earthquakes in Long Valley Caldera, California. We confirm the highly isotropic nature of the source in a pilot event but find a non-negligible CLVD component that was overlooked in past studies ignoring the uncertainty in Earth model. Thus, careful consideration of the Earth model's uncertainty as part of the MT inversion schemes will be necessary for future applications to better understand the complicated physics of earthquake sources. Show more