Modern Signal Extraction Methods In Computational Seismology
Tetsuo Takanami, Genshiro Kitagawa · Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University) · 2000
The record of Earth motion obtained by a seismograph contains information about the nature of seismic source that generated the motion as well as properties of the media through which the seismic disturbances were propagated. Our problem is to extract such information from the observed record. However the seismograph is under continuous influence of a variety of natural forces such as the effect of microtremor by the natural sources of winds and waves in the sea and by the common artificial sources by human activity. Since it is almost impossible to describe the response to these noise inputs precisely, for automatic processing of seismic data, proper statistical modeling is necessary. In this paper, we show the several specific examples of time series modeling for signal extraction problems related to seismol· ogy. Namely, we consider the extraction of small seismic signal from noisy data by the local likelihood method and the self-organizing state space modeling. The extraction of weak signals from noisy data has been shown to exemplify the power of the above procedures. 1.