BeckerFraserPhotos May 2011 photograph 0129

Images, UC QuakeStudies

Photograph captioned by BeckerFraserPhotos, "173 Gloucester Street".

BeckerFraserPhotos May 2011 photograph 0130

Images, UC QuakeStudies

Photograph captioned by BeckerFraserPhotos, "173 Gloucester Street".

BeckerFraserPhotos May 2011 photograph 0970

Images, UC QuakeStudies

Photograph captioned by BeckerFraserPhotos, "267 Manchester Street".

BeckerFraserPhotos June 2011 photograph 174

Images, UC QuakeStudies

Photograph captioned by BeckerFraserPhotos, "The north-west corner of Manchester and Gloucester Streets (Map World). Extensive frontal collapse of the Gloucester Street frontage during the 13 June 2011 earthquake".

BeckerFraserPhotos September 2011 photograph 566

Images, UC QuakeStudies

Photograph captioned by BeckerFraserPhotos, "Intersection of Gloucester and Manchester Streets (north-west view)".

WEMO ERT Set 3 - February 2011 - Photograph 103

Images, UC QuakeStudies

A photograph of cars on Manchester Street which has been crushed by falling rubble. Bricks and other rubble from the earthquake-damaged shops above are scattered across the road in front. The word "clear" has been spray-painted on the side of the cars.

On the Relationship Between Geospatial Liquefaction-Model Performance and …

Research papers, University of Canterbury Library

Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.