Search USGS⌕ Search
Home/Archive/USGS

USGS · 70191695

Using pad‐stripped acausally filtered strong‐motion data

David M. Boore·Aida Azari Sisi·Sinan Akkar
View original source ↗Export metadata ↓

Abstract

Most strong‐motion data processing involves acausal low‐cut filtering, which requires the addition of sometimes lengthy zero pads to the data. These padded sections are commonly removed by organizations supplying data, but this can lead to incompatibilities in measures of ground motion derived in the usual way from the padded and the pad‐stripped data. One way around this is to use the correct initial conditions in the pad‐stripped time series when computing displacements, velocities, and linear oscillator response. Another way of ensuring compatibility is to use postprocessing of the pad‐stripped acceleration time series. Using 4071 horizontal and vertical acceleration time series from the Turkish strong‐motion database, we show that the procedures used by two organizations—ITACA (ITalian ACcelerometric Archive) and PEER NGA (Pacific Earthquake Engineering Research Center–Next Generation Attenuation)—lead to little bias and distortion of derived seismic‐intensity measures.

Explore related subjects

Bulletin of the Seismological Society of AmericaArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

David M. Boore, Aida Azari Sisi, Sinan Akkar. 2012-03-29. Using pad‐stripped acausally filtered strong‐motion data. https://doi.org/10.1785/0120110222

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Re-evaluation of shaking, dynamic strain, and the source of the 1886 Charleston, South Carolina, earthquake

As the largest historical event in the eastern United States, the 1886 Charleston, South Carolina, earthquake, plays a prominent role in our nation’s seismological and human history and an outsized role in our probabilistic ground‐motion hazard estimates, affecting building codes, insurance rates, and geophysical monitoring. Thus, constraining the characteristics of the 1886 earthquake source, as well as the nature of its shaking distribution and impacts to the extent possible, is essential. Given its vintage, however, societal, geological, and geophysical datasets are limited compared with those used in modern seismological analyses. Because of these limitations, some characteristics of the rupture model are fundamentally nonunique. Still, by re‐evaluating all available data, I can constrain several key aspects of this earthquake. I show that several interpretations in recent analyses are inconsistent regarding the inferred source; shaking; ground failure; and, particularly, railway impacts. Importantly, I have shown that although dynamic strain caused the ground failure that led to track deformation, without the intermediate liquefaction step, dynamic strain alone was unlikely to be the source of rail deformation, contrary to earlier studies. Had earlier pronouncements that dynamic strain is a real concern for bending railways held true, there would be serious ramifications for railway engineering as well as for other above‐ and below‐ground linear infrastructure.

South Carolina↗
USGS2026

ETAS-positive: Extracting unbiased epidemic-type aftershock models from incomplete earthquake catalogs

Earthquake catalogs are inherently incomplete, and short-term fluctuations in catalog completeness can strongly bias estimates of seismicity parameters. Here, we extend the positive statistics framework recently introduced for estimating Gutenberg–Richter a and b values to the Epidemic-Type Aftershock Sequence (ETAS) model. The approach focuses on complete magnitude–time intervals defined by pairs of earthquakes in which the second event is larger than the first. Comprehensive synthetic tests demonstrate that the proposed ETAS + method retrieves nearly unbiased parameter estimates even from highly incomplete catalogs, whereas parameters obtained using classical estimation approaches are strongly biased. Application to Southern California reveals a similar trend, with the productivity scaling parameter α approaching the b value, consistent with self-similar earthquake triggering within estimation uncertainties. Owing to its robustness against catalog incompleteness and computational efficiency, the ETAS + method provides a powerful new tool for seismicity analysis and earthquake forecasting.

California↗