Boise State University

About: Boise State University is a education organization based out in Boise, Idaho, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 3698 authors who have published 8664 publications receiving 210163 citations. The organization is also known as: BSU & Boise State.

Measuring Thaw Depth Beneath Peat-Lined Arctic Streams Using Ground-Penetrating Radar

Abstract: In arctic streams, depth of thaw beneath the stream channel is likely a significant parameter controlling hyporheic zone hydrology and biogeochemical cycling. As part of an interdisciplinary study of this system, we conducted a field investigation to test the effectiveness of imaging substream permafrost using ground-penetrating radar (GPR). We investigated three sites characterized by low-energy water flow, organic material lining the streambeds, and water depths ranging from 0·2 to 2 m. We acquired data using a 200 MHz pulsed radar system with the antennas mounted in the bottom of a small rubber boat that was pulled across the stream while triggering the radar at a constant rate. We achieved excellent results at all three sites, with a clear continuous image of the permafrost boundary both peripheral to and beneath the stream. Our results demonstrate that GPR can be an effective tool for measuring substream thaw depth. Copyright © 2005 John Wiley & Sons, Ltd.

A Newton root-finding algorithm for estimating the regularization parameter for solving ill-conditioned least squares problems

Abstract: We discuss the solution of numerically ill-posed overdetermined systems of equations using Tikhonov a priori based regularization. When the noise distribution on the measured data is available to appropriately weight the fidelity term, and the regularization is assumed to be weighted by inverse covariance information on the model parameters, the underlying cost functional becomes a random variable that follows a χ2 distribution. The regularization parameter can then be found so that the optimal cost functional has this property. Under this premise a scalar Newton root-finding algorithm for obtaining the regularization parameter is presented. The algorithm, which uses the singular value decomposition of the system matrix, is found to be very efficient for parameter estimation, requiring on average about 10 Newton steps. Additionally, the theory and algorithm apply for generalized Tikhonov regularization using the generalized singular value decomposition. The performance of the Newton algorithm is contrasted to standard techniques, including the L-curve, generalized cross validation and unbiased predictive risk estimation. This χ2-curve Newton method of parameter estimation is seen to be robust and cost effective in comparison to other methods, when white or colored noise information on the measured data is incorporated.