Geoelectric hazard maps for the continental United States

Geophysical Research Letters 43, published online 19 September 2016

By Love, J. J., et al.

“In support of a multiagency project for assessing induction hazards, we present maps of extreme-value geoelectric amplitudes over about half of the continental United States. These maps are constructed using a parameterization of induction: estimates of Earth surface impedance, obtained at discrete geographic sites from magnetotelluric survey data, are convolved with latitude-dependent statistical maps of extreme-value geomagnetic activity, obtained from decades of magnetic observatory data. Geoelectric amplitudes are estimated for geomagnetic waveforms having 240 s sinusoidal period and amplitudes over 10 min that exceed a once-per-century threshold.


Enter a caption

Maps showing synthetic geoelectric amplitudes at EarthScope and U.S. Geological Survey sites for (a) north-south and (b) east-west geomagnetic induction with an amplitude of b(ω) = 1 nT and at period T = 2π/ω = 240 s. Constructed using the methods of Bedrosian and Love [2015, Figure 4].

“As a result of the combination of geographic differences in geomagnetic activity and Earth surface impedance, once-per-century geoelectric amplitudes span more than 2 orders of magnitude and are an intricate function of location. For north-south induction, once-per-century geoelectric amplitudes across large parts of the United States have a median value of 0.26 V/km; for east-west geomagnetic variation the median value is 0.23 V/km. At some locations, once-per-century geoelectric amplitudes exceed 3 V/km.”

Satellite Derived Bathymetry using Adaptive-Geographically Weighted Regression Model

umgd20-v039-i05-coverMarine Geodesy, published online 07 October 2016

By Poliyapram Vinayaraj, Venkatesh Raghavan, and Shinji Masumoto

“The common practice adopted in previous attempts on Satellite Derived Bathymetry (SDB) has been to calibrate a single set of coefficients using global regression model. In this study we propose an Adaptive Geographically Weighted Regression (A-GWR) model that takes into account local factors in determining the regression coefficients. A-GWR model is examined as an effective solution for addressing heterogeneity and could provide better water depth estimates in near-shore region. The study has been carried out for a 30 km stretch and covers 160 km2 of a complex near-shore coastal region of Puerto Rico, Northeastern Caribbean Sea. Medium resolution (Landsat-8) and high resolution (RapidEye) images were used to estimate water depth. Results demonstrate that the A-GWR model performs well in estimating bathymetry for shallow water depths (1 to 20m), showing the correlation coefficient (R) of 0.98 and 0.99, determination coefficient (R2) of 0.95 and 0.99 and Root Mean Square Error (RMSE) of 1.14m and 0.4m) for Landsat-8 and RapidEye respectively. The data processing workflow has been entirely implemented in an Open Source GIS environment and can be easily adopted in other areas.”