The state of the art in processing ground motion timeseries.

Nicholas A Alexander, A.A. Chanerley

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Abstract

In this paper, we review the methods employed in the scientific literature for the
correction and processing of timeseries data obtained from strong motion
accelerographs. Noise/error reduction is the key. For the legacy analogue, the
sources of error found in strong motion instruments and their recordings were, (i)
instrument noise (caused by low dynamic range, saturation, etc.), (ii) nonlinear
instrument system responses (including poor performance at deconvolution DC),
(iii) triggering post event start, (iv) analogue data storage and digitization. For
digital accelerographs, many of these problems have been ameliorated by improved design. However, the presence of noise caused by instrument tilting/rotating [1-5] has not been corrected as modern digital accelerographs are still only 3 (translational) axis instruments. The signal processing techniques conventionally applied [6-11] were baseline correction, band pass filtering, and instrument deconvolution.The least controversial of these techniques is the high-cut filter which acts as an anti-alias filter. Instrument deconvolution does require knowledge of its characteristic frequency response function, which may be unknown. In this case, [12-14] applied inverse system identification using various adaptive least squares approaches. Low-cut filtering is more problematic, as it assumes that information within some frequency stop-bands is all noise. Noise reduction (at low frequencies) is critical in obtaining ground displacements from ground acceleration timeseries [11]. [15] suggests a piecewise linear detrend; but a better alternative is a de-noising
approach [16]. [3, 4] use the stationary wavelet transformer to separate the
acceleration timeseries into low frequency sub-band (LFS) and high frequency subbands (HFS). The LFS is de-noised [16] and resulting displacements are a better match to GPS results. This highlights one of the main weaknesses of modern instruments, namely that they are still only 3-axis and not 6-axis instruments

Original languageEnglish
Title of host publication Computational Technology Reviews
EditorsB.H.V Topping
PublisherSaxe-Coburg Publications
Volume8
DOIs
Publication statusPublished - 2013

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    Alexander, N. A., & Chanerley, A. A. (2013). The state of the art in processing ground motion timeseries. In B. H. V. Topping (Ed.), Computational Technology Reviews (Vol. 8). Saxe-Coburg Publications. https://doi.org/doi:10.4203/ctr.8.4