On robust and reliable automated baseline corrections for strong motion seismology. Issue 3 (31st March 2013)

• Record Type: Journal Article
• Authors: Melgar, Diego; Bock, Yehuda; Sanchez, Dominga; Crowell, Brendan W.
• Is Part Of: Journal of geophysical research. Volume 118:Issue 3(2013:Mar.)
• Page Start: 1177
• Page End: 1187
• Publication Date: 2013-03-31
• Subjects: Seismology -- Strong Motion -- GPS; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1002/jgrb.50135
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: [1] Computation of displacements from strong motion inertial sensors is to date an open problem. Two distinct methodologies have been proposed to solve it. One involves baseline corrections determined from the inertial data themselves and the other a combination with other geophysical sensors such as GPS. Here we analyze a proposed automated baseline correction algorithm using only accelerometer data and compare it to the results from the real‐time combination of strong motion and GPS data. The analysis is performed on 48 collocated GPS and accelerometers in Japan that recorded the 2011 Mw 9.0 Tohoku‐oki earthquake. We study the time and frequency domain behavior of both methodologies. We find that the error incurred from automated baseline corrections that rely on seismic data alone is complex and can be large in both the time and frequency domains of interest in seismological and engineering applications. The GPS/accelerometer combination has no such problems and can adequately recover broadband strong motion displacements for this event. The problems and ambiguities with baseline corrections and the success of the GPS/accelerometer combination lead us to advocate for instrument collocations as opposed to automated baseline correction algorithms for accelerometers. Key Points: We evaluate the performance of baseline correction The error in the corrections is in the frequencies of interest to seismology Algorithms that combine multiple types of data perform best