Kerry Key

Kerry Key

Associate Professor

Research Interests
Electromagnetic geophysics, marine geophysics, numerical and computational geophysics, hydrocarbon exploration, mid-ocean ridges, subduction zones, volcanoes, mantle dynamics, instrumentation design.

Education
Ph. D., Geophysics, Scripps Institution of Oceanography, University of California San Diego, 2003
B.S., Earth Sciences, University of California San Diego, 1998
Palomar Community College, 1992-1995

Professional Experience
Associate Professor, Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory, Columbia University, 2017-present
Visiting Associate Professor, Department of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory, Columbia University, 2017
Associate Professor, Scripps Institution of Oceanography, UC San Diego, 2013-2017
Associate Researcher, Scripps Institution of Oceanography, UC San Diego, 2012-2013
Visiting Scientist, Institute de Physique du Globe de Paris, 2009
Assistant Researcher, Scripps Institution of Oceanography, UC San Diego, 2008-2012
Postdoctoral Researcher, Scripps Institution of Oceanography, UC San Diego, 2004-2008

Selected Publications

Gustafson, Chloe; Key, Kerry; Evans, Rob L

Aquifer systems extending far offshore on the U.S. Atlantic margin Journal Article

Scientific Reports, 9 (1), 2019.

Abstract | Links

Blatter, Daniel; Key, Kerry; Ray, Anandaroop; Gustafson, Chloe; Evans, Rob

Bayesian Joint Inversion of Controlled Source Electromagnetic and Magnetotelluric Data to Image Freshwater Aquifer Offshore New Jersey Journal Article

Geophysical Journal International, 2019, ISSN: 0956-540X.

Abstract | Links

@article{10.1093/gji/ggz253,
title = {Bayesian Joint Inversion of Controlled Source Electromagnetic and Magnetotelluric Data to Image Freshwater Aquifer Offshore New Jersey},
author = {Daniel Blatter and Kerry Key and Anandaroop Ray and Chloe Gustafson and Rob Evans},
url = {https://doi.org/10.1093/gji/ggz253},
doi = {10.1093/gji/ggz253},
issn = {0956-540X},
year = {2019},
date = {2019-01-01},
journal = {Geophysical Journal International},
abstract = {Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods – using gradients and model regularization – typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1D Earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods – using gradients and model regularization – typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1D Earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.

Werthmüller, Dieter; Key, Kerry; Slob, Evert C

A tool for designing digital filters for the Hankel and Fourier transforms in potential, diffusive, and wavefield modeling Journal Article

Geophysics, 84 (2), pp. F47-F56, 2019.

Abstract | Links