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\begin{document}

\title{Energy Rate Functions: An Overview of HHT-based Earthquake Source
Characterization using Strong Motion Data}



\author[1]{Swapnil Mache}%
\author[1]{Nishant Chauhan}%
\author[2]{Avigyan Chatterjee}%
\author[1]{Kusala Rajendran}%
\affil[1]{Indian Institute of Science}%
\affil[2]{University of Oregon}%


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  \date{May 30, 2024}


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\begin{abstract}
Subduction zone earthquakes show varying energy release patterns and
frequency content, based on their tectonic settings and hypocentral
depths. Resolving these features from the nonlinear and non-stationary
seismograms is a challenge. Our work in the Japan Trench follows studies
by Huang et al. (1998, 2001) and Zhang et al. (2003), who demonstrated
the use of empirical mode decomposition to separate records into
multiple timescales, or intrinsic mode functions (IMFs). Zhang et al.
observed that IMFs 2-5 represented the source rupture process for the
1994 Northridge earthquake. Chauhan (master's thesis, 2019) used
time-frequency distributions, short-time Fourier and continuous wavelet
transforms, of IMFs of strong-motion data for a pair of
interplate-intraslab earthquakes to identify the dominant, short
duration, low-frequency energy release for the intraslab event. He found
a high correlation between the original signal and a linear combination
of IMFs 3 and 4, possibly representing the source. Chatterjee et al.
(AGU, 2018) observed an association between time-frequency-energy
distributions of certain IMFs and moment rate functions (MRFs) from
teleseismic waveform models, for five earthquakes. Chatterjee et al.
(AGU, 2019) and Mache et al. (AGU, 2019) used Hilbert spectral analysis
(Huang et al., 1998) of IMFs selected based on their frequency and
energy and observed better match between the two. This new function,
which they regard as the Energy Rate Function (ERF), can reproduce the
MRF's essential elements, i.e., its duration and shape, but Mache
(master's thesis, 2020) observed that results depended on the selection
of stations. As the next step, Mache and Rajendran (JpGU-AGU, 2020)
based the selection criteria on the slip distribution, strike, and JMA
intensity distribution maps (JMA 1996) and applied the method to 7
earthquakes from various tectonic settings of the Japan Trench. Here we
present an overview of the various methods for analyzing KiK-net
strong-motion data for selected earthquakes to extract information on
their time-frequency-energy distributions. The ERF generated through
this analysis is a physically compatible expression of the MRF and,
therefore, more useful in predicting the shaking effects of earthquakes.%
\end{abstract}%



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