Some statistical aspects of the estimation of seismic travel times

1968 ◽  
Vol 58 (4) ◽  
pp. 1243-1260 ◽  
Author(s):  
William Tucker ◽  
Eugene Herrin ◽  
Helen W. Freedman

Abstract Some of the statistical aspects of estimating travel-time anomalies and station corrections are considered. In order to estimate these quantities using earthquake data the events themselves must first be located. We investigated the use of the Gauss-Newton iterative technique to obtain a least-squares epicenter location employing Monte Carlo methods. Results of these studies indicate that the Gauss-Newton process converges to an absolute minimum and that confidence ellipses computed by linear techniques are reliable for reasonable networks of well-distributed stations. Also the Monte Carlo studies indicate that a least-squares solution may be inaccurate if appreciable travel-time anomalies or station-error means exist. We then expanded the location procedure to include the estimation of travel-time anomalies and station corrections. In order to obtain these estimates data from some 278 large earthquakes were analyzed by using a modified Seidel iterative process.

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592


1983 ◽  
Vol 27 (2) ◽  
pp. 606-627 ◽  
Author(s):  
Hafez M. A. Radi ◽  
John O. Rasmussen ◽  
Kenneth A. Frankel ◽  
John P. Sullivan ◽  
H. C. Song

2005 ◽  
Vol 17 (23) ◽  
pp. 3509-3524 ◽  
Author(s):  
Per Zetterström ◽  
Sigita Urbonaite ◽  
Fredrik Lindberg ◽  
Robert G Delaplane ◽  
Jaan Leis ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document