C
Clifford H. Thurber
Researcher at University of Wisconsin-Madison
Publications - 231
Citations - 13037
Clifford H. Thurber is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Seismic tomography & Fault (geology). The author has an hindex of 50, co-authored 226 publications receiving 11689 citations. Previous affiliations of Clifford H. Thurber include Tohoku University & State University of New York System.
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Book
Parameter estimation and inverse problems
TL;DR: "Parameter Estimation and Inverse Problems, 2/e" introduces readers to both Classical and Bayesian approaches to linear and nonlinear problems with particular attention paid to computational, mathematical, and statistical issues related to their application to geophysical problems.
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Earthquake locations and three-dimensional crustal structure in the Coyote Lake Area, central California
TL;DR: In this article, a three-dimensional model for the velocity structure of the upper crust in an area encompassing the rupture zone of the Coyote Lake earthquake of August 1979 is presented.
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A fast algorithm for two-point seismic ray tracing
Junho Um,Clifford H. Thurber +1 more
TL;DR: In this article, an initial path estimate is perturbed using a geometric interpretation of the ray equations, and the travel time along the path is minimized in a piecewise fashion, iteratively performed until the travel times converges within a specified limit.
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Double-Difference Tomography: The Method and Its Application to the Hayward Fault, California
TL;DR: In this paper, a double-difference (DD) seismic tomography method was developed that makes use of both absolute and relative arrival times to produce an improved velocity model and yields event locations of a quality equivalent to those of the DD location method.
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Automatic P-Wave Arrival Detection and Picking with Multiscale Wavelet Analysis for Single-Component Recordings
TL;DR: In this article, an automatic P-wave arrival detection and picking algorithm based on the wavelet transform and Akaike information criteria (AIC) picker was developed. But the algorithm was not applied to the Parkfield dataset, and the results showed that only 81% of the picks were within 0.2sec of the corresponding analyst pick for the Dead Sea dataset.