School of Earth, Energy and Environmental Sciences
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Ph.D. Student in Geophysics
Current Research and Scholarly InterestsThe most destructive tsunamis are generated by earthquakes, posing hazard to coastlines around the world. Open questions about these events are, how are they generated, what parameters will cause the most destructive waves, and how do we interpret existing seafloor data to create tsunami and earthquake early warning? To answer these questions, computer simulations (modeling) have been an effective method to study past events and assess a region's potential hazard. Many modelers use an approximate approach for modeling how earthquakes generate tsunamis, but recent events have shown assumptions in these approaches do not hold in all cases. Since these models do not fully describe the physics, they are less effective in predicting future hazards.
A more rigorous full-physics method has been developed by a previous group member that does not approximate tsunami generation, creating a more realistic model of earth/ocean interactions. This full-physics method has only been developed in 2D; however, a 3D model is needed to allow for comparison to real-world data. In collaboration with the University of Munich, I am currently incorporating the full-physics method into the open-source 3D earthquake software. This software will be the first 3D full-physics model for earthquake tsunamigenesis, providing greater insight into tsunami physics and valuable information for tsunami early warning.
In addition to my thesis work, I have focused on two other projects to study hazards. I have completed my starter project studying frictional effects on earthquake behavior and completed my second project working with the US Geological Survey on improving ground motion prediction equations used in the earthquake early warning systems.
Ph.D. Student in Geophysics
BioAakash Ahamed (BS, with honors, Franklin and Marshall College; MSc, Boston College; PhD Candidate, Stanford University) is a hydrologist developing scientific methods for satellite and airborne remote sensing measurements with applications to water resources, natural hazards, and agricultural systems. As a PhD Candidate in the Department of Geophysics, his current doctoral project focuses on modeling, monitoring, and forecasting key hydrologic components of the Central Valley Aquifer System in California using techniques in data assimilation and machine learning. Aakash previously worked as a support scientist in the Hydrological Sciences Lab at NASA Goddard Space Flight Center, where he constructed satellite-based models of flood and landslide hazards. He has also developed remote sensing analyses and software at Ceres Imaging, a successful precision agriculture start up based in Silicon Valley, and interned as a GIS analyst at the World Wildlife Fund for Nature in Washington, DC.
Postdoctoral Scholar, Geophysics
BioSarfaraz Alam is a Postdoctoral Scholar at Stanford University, where he is modeling nitrate transport in groundwater and surface water to improve approaches to enforcement. His research integrates hydrologic modeling, contaminant transport, remote sensing, and data science to understand how climate and human-induced changes affect water resources and the environment. Sarfaraz earned his Ph.D. in Civil Engineering from UCLA in 2021.
Sarfaraz received an Outstanding Ph.D. student award, Dissertation Year Fellowship, and Graduate Division Fellowship at UCLA. He authored nine peer-reviewed journal articles and presented his research in over ten international conferences.
Wayne Loel Professor of Earth Science
Current Research and Scholarly InterestsEarthquake seismology
Barney and Estelle Morris Professor
Current Research and Scholarly InterestsResearch
My students and I devise new algorithms to improve the imaging of reflection seismic data. Images obtained from seismic data are the main source of information on the structural and stratigraphic complexities in Earth's subsurface. These images are constructed by processing seismic wavefields recorded at the surface of Earth and generated by either active-source experiments (reflection data), or by far-away earthquakes (teleseismic data). The high-resolution and fidelity of 3-D reflection-seismic images enables oil companies to drill with high accuracy for hydrocarbon reservoirs that are buried under two kilometers of water and up to 15 kilometers of sediments and hard rock. To achieve this technological feat, the recorded data must be processed employing advanced mathematical algorithms that harness the power of huge computational resources. To demonstrate the advantages of our new methods, we process 3D field data on our parallel cluster running several hundreds of processors.
I teach a course on seismic imaging for graduate students in geophysics and in the other departments of the School of Earth Sciences. I run a research graduate seminar every quarter of the year. This year I will be teaching a one-day short course in 30 cities around the world as the SEG/EAGE Distinguished Instructor Short Course, the most important educational outreach program of these two societies.
2007 SEG/EAGE Distinguished Instructor Short Course (2007); co-director, Stanford Exploration Project (1998-present); founding member, Editorial Board of SIAM Journal on Imaging Sciences (2007-present); member, SEG Research Committee (1996-present); chairman, SEG/EAGE Summer Research Workshop (2006)