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\begin{document}

\title{Lightweight joint inversion of point-source moment-tensor and
station-specific time shifts}



\author[1]{Thanh-Son Pham}%
\affil[1]{Australian National University}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
The misalignment of the observation and predicted waveforms in regional
moment tensor inversion is mainly due to seismic models' incomplete
representation of the Earth's heterogeneities. Current moment tensor
inversion techniques, allowing station-specific time shifts to account
for the model error, are computationally expensive. Here, we propose a
lightweight method to jointly invert moment-tensor parameters and
unknown station-specific time shifts utilizing the modern
functionalities in deep learning frameworks. A \$L\_2\^{}2\$ misfit
function between predicted synthetic and time-shifted observed
seismograms is defined in the spectral domain, which is differentiable
to all unknowns. The inverse problem is solved by minimizing the misfit
function with a gradient descent algorithm. The method's feasibility,
robustness, and scalability are demonstrated on earthquakes in the Long
Valley Caldera, California. This work presents an example of fresh
opportunities to apply advanced computational infrastructures developed
in deep learning to geophysical problems.%
\end{abstract}%



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