A driven and knowledgeable research associate offering proven background in applied mathematics and geophysics. History of developing novel algorithms related to geophysical inverse problems, signal processing and machine learning.
PhD in Geophysics, 2016
Delft University of Technology (TU Delft), Delft, The Netherlands.
Master of Science and Technology, 2012
Indian Institute of Technology (Indian School of Mines), IIT (ISM), Dhanbad, India.
The scientific field where recordings of seismic waves are used to infer information about geological structures deep underground.
Techniques that study the interference phenomena between pairs of noise signals in order to gain useful information about the subsurface.
Scientific study of earthquakes and the propagation of elastic waves through the Earth.
Investigating novel methods of machine learning in the context of seismic imaging.
Developed algorithms to factorize i.e., blindly deconvolve seismic noise signals, and thereby output key features pertaining to either sources (e.g., earthquakes) or subsurface characteristics.
Developed robust seismic full-waveform inversion algorithms, in consideration of near-surface imaging applications, which employ auxiliary signal models to produce an accurate reconstruction of the subsurface properties.
Spearheaded research towards the establishment of super-virtual seismic interferometry, which presents a collection of methods to denoise seismic refractions and diffractions.
A robust factorization of the teleseismic waveforms resulting from an earthquake.
Data-driven Green’s function retrieval from the multi-channel seismic data of a noisy source.
When the seismic waveforms are too complicated to be analyzed during inversion, a simplification of them into envelope-like bumpy waveforms can be useful.