PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 26, 2011Bulletin of the Seismological Society of America198 citations

An Improved Automatic Scheme for Empirical Baseline Correction of Digital Strong-Motion Records

View Full Paper
RWR. WangBSBernd SchurrCMClaus Milkereit

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract Compared with seismic waves, near-field static deformation can provide more robust constraints on earthquake size and slip distribution because it is less sen-sitive to the rupture process and Earth structure. The static deformation data are now obtained using space-geodetic measurements. For early warning and rapid hazard assessment, such geodetic measurements are less useful because they are usually avail-able only with a time delay of days to weeks or even longer. Recent studies have shown that coseismic static displacements can be estimated frommodern seismometer records after an appropriate correction for baseline errors that may be caused by rotational motion, tilt, instrument, and other effects. For this purpose, several empirical baseline correction methods have been proposed. Algorithms, in which an acceleration or other acceleration record derived threshold is used to determine the timing of baseline shift, can be easily implemented. In practice, however, the baseline shift is not necessarily accompanying the strongest ground shaking; methods based on the threshold approach tend to lead to an over- or underestimation of the true baseline shift. Other correction schemes, which can be performed by manual calibration, rely on subjective decisions for the choice of correction parameters. In this paper, an automatic scheme is presented, in which the method used to determine the baseline shift has a stronger physical basis.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Wang et al. (2011) studied this question.

synapsesocial.com/papers/6a21d4e64c1bee377cecb5d7https://doi.org/10.1785/0120110039
Ask AI
Helpful
Bookmark
Share
View Full Paper