@article{freshwaterSR,
title = {Aquifer systems extending far offshore on the U.S. Atlantic margin},
author = {Chloe Gustafson and Kerry Key and Rob L Evans },
url = {https://doi.org/10.1038/s41598-019-44611-7},
doi = {10.1038/s41598-019-44611-7},
year = {2019},
date = {2019-06-19},
journal = {Scientific Reports},
volume = {9},
number = {1},
abstract = {Low-salinity submarine groundwater contained within continental shelves is a global phenomenon. Mechanisms for emplacing offshore groundwater include glacial processes that drove water into exposed continental shelves during sea-level low stands and active connections to onshore hydrologic systems. While low-salinity groundwater is thought to be abundant, its distribution and volume worldwide is poorly understood due to the limited number of observations. Here we image laterally continuous aquifers extending 90 km offshore New Jersey and Martha’s Vineyard, Massachusetts, on the U.S. Atlantic margin using new shallow water electromagnetic geophysical methods. Our data provide more continuous constraints on offshore groundwater than previous models and present evidence for a connection between the modern onshore hydrologic system and offshore aquifers. We identify clinoforms as a previously unknown structural control on the lateral extent of low-salinity groundwater and potentially a control on where low-salinity water rises into the seafloor. Our data suggest a continuous submarine aquifer system spans at least 350 km of the U.S. Atlantic coast and contains about 2800 km3 of low-salinity groundwater. Our findings can be used to improve models of past glacial, eustatic, tectonic, and geomorphic processes on continental shelves and provide insight into shelf geochemistry, biogeochemical cycles, and the deep biosphere.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@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 = {http://emlab.ldeo.columbia.edu/wp-content/uploads/2019/12/6FA32540-AE06-4DF8-93A2-E5FA1D3AAEC8.pdf},
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}
}