The hazard assessment of earthquakes is closely related to the propagation of their associated ruptures. This research
responds to numerous fundamental challenges involved in directly measuring source signals
that originate from a propagating rupture.
It is desirable to directly measure the source pulses
at the seismometers and subsequently infer
quantities that are related to the rupture propagation.
the signals measured in place of those pulses are affected by the subsurface properties through which they propagate
before reaching these stations.
Thus, instead of measuring the earthquake source signal directly,
each seismic station records
two types of information that are convoluted into a single signal:
information about the earthquake (source pulse) and information about the
subsurface features through which
it passed (path effects).
The path effects may distort the earthquake source signal, for example,
one or more reflections off of geological layers in the subsurface;
intrinsic attenuation of the porous-rock medium.
Consequently, an accurate characterization of the earthquake rupture
involves reliably analyzing the recorded seismograms to separate the path effects
from the earthquake pulses. Current methods for separating out the two types of information
rely on dubious assumptions, and may be confounded because extracting source pulse
requires assumptions about the path, but conversely
extracting path effects requires assumptions about the source.
This research introduces to seismology a new analysis method, focused blind deconvolution (FBD),
that can be used to extract source or path information
without relying on traditional assumptions.
Instead, this method compares data from the same source picked up by multiple receivers,
and uses advanced signal processing to identify similarities and differences among the data.
Similarities among the signals can be identified as source effects,
while dissimilarities indicate path effects.
Because it does not require the aforementioned assumptions,
this method will provide more accurate and reliable source information to seismologists.
Pawan is an assistant professor in the Center for Earth Sciences at the Indian Institute of Science (IISc). He enjoys developing novel algorithms related to geophysical inverse problems, signal processing and machine learning.