Open AccessRobust bearing estimation for 3-component stations
Author(s) -
J. P. Claassen
Publication year - 2000
Language(s) - English
Resource type - Reports
DOI - 10.2172/752011
Subject(s) - bearing (navigation) , computer science , set (abstract data type) , component (thermodynamics) , exploit , process (computing) , data mining , construct (python library) , estimation , calibration , prior information , algorithm , artificial intelligence , statistics , mathematics , engineering , physics , computer security , systems engineering , thermodynamics , programming language , operating system
A robust bearing estimation process for 3-component stations has been developed and explored. The method, called SEEC for Search, Estimate, Evaluate and Correct, intelligently exploits the inherent information in the arrival at every step of the process to achieve near-optimal results. In particular the approach uses a consistent framework to define the optimal time-frequency windows on which to make estimates, to make the bearing estimates themselves, to construct metrics helpful in choosing the better estimates or admitting that the bearing is immeasurable, and finally to apply bias corrections when calibration information is available to yield a single final estimate. The algorithm was applied to a small but challenging set of events in a seismically active region. It demonstrated remarkable utility by providing better estimates and insights than previously available. Various monitoring implications are noted from these findings