With the continued increase in the worldwide demand for fossil fuels, seismic exploration has become increasingly more important. This increased demand is calling for innovations in seismic processing,imaging & inversion and my research is directed towards

• the development of new methods for seismic exploration including data processing techniques and approaches where the acquisition and subsequent computational costs are in par with the desired detail rather than being dictated by the size of the discretization of the underlying inversion problem. This technology is considered as break-through by the oil & gas industry and is currently being evaluated for commercialization.
• the development of new method to detect, characterize, and model major unconformities in the ubsurface using binary mixture models.

My research program revolves around three main questions:

1. How to obtain better quality seismic images of the Earth's subsurface at reduced computational and data acquisition costs-i.e., how to improve the signal-to-noise ratio and resolution of crustal and global-scale images. Improvements in the image quality are instrumental for (i) better understanding of the processes that take place in the Earth's subsurface and (ii) improved exploration and production of hydrocarbons and other resources.
2. How to characterize what is in the image-i.e., how to obtain a parameterization for the properties of seismic reflectors that can be accessed from seismic data. These parameterizations provide information on the fine structure of the major transitions in the Earth and help us constrain the physical and geological changes that
   are responsible for these transitions.
3. How to understand why certain reflectors/transitions occur-i.e., how to (physically) model seismic reflectors, using mixture models and percolation theory. This modeling gives me insight as to how changes
   in the rock's composition are translated into rapid changes in the seismic properties.

Current Industry projects

SINBAD: Seismic Imaging by Next-generation BAsis-function Decomposition

ChaRM: Characterization of Reflectors and Modeling

DNOISE: Dynamic nonlinear optimization for imaging in seismic exploration

Sinbad introduces the Curvelet transform as a vehicle to improve seismic imaging and processing algorithms, which explore the properties of Curvelets. Not only are the images improved in this way but also the imaging is formulated computationally more efficient.

ChaRM's goal is to find a quantitative description for reflectors other than the strength of the reflectors. The description is designed for use by geologists to help their interpretations and by reservoir engineers and rock physicists to constrain the elastic and transport properties of rocks. The modeling part of this project concerns itself with the derivation of rock-physical models that predict more general transition models as a function of the rock’s composition. I proposed a critical Site Percolation models that gives rise to a generalized transition for bi-compositional mixtures. I applied this model to explain upper-mantle discontinuities.

DNOISE is a NSERC Collaborative Research and Development Grant and funds a multidisciplinary research project involving faculty members from the Department of Mathematics, Computer Science, and Earth and Ocean Sciences of the University of British Columbia. DNOISE focuses on one of the most pressing questions in the oil and gas industry namely ---"How to image more deeply and with more detail?" This pressing question needs to be answered if our energy-intensive society is to adequately address the current surge in demand for hydrocarbon resources. DNOISE matches the industrial funding dollar-for-dollar as part of the SINBAD and ChARM projects.