Coseismic Rupture and Preliminary Slip Estimates for the Papatea Fault and Its Role in the 2016 Mw 7.8 Kaikōura, New Zealand, Earthquake

Abstract

Coseismic rupture of the 19‐km‐long north‐striking and west‐dipping sinistral reverse Papatea fault and nearby structures and uplift/translation of the Papatea block are two of the exceptional components of the 14 November 2016 Mw 7.8 Kaikōura earthquake. The dual‐stranded Papatea fault, comprising main (sinistral reverse) and western (dip‐slip) strands, ruptured onshore and offshore from south of Waipapa Bay to George Stream in the north, bounding the eastern side of the Papatea block. Fault rupture mapping was aided by the acquisition of multibeam bathymetry, light detection and ranging (lidar) topography and other imagery, as well as differential lidar (D‐lidar) from along the coast and Clarence River valley. On land, vertical throw and sinistral offset on the Papatea fault was assessed across an aperture of ±100 m using uncorrected D‐lidar and field data to develop preliminary slip distributions. The maximum up‐to‐the‐west throw on the main strand is ∼9.5±0.5 m⁠, and the mean throw across the Papatea fault is ∼4.5±0.3 m⁠. The maximum sinistral offset, measured near the coast on the main strand, is ∼6.1±0.5 m⁠. From these data, and considering fault dip, we calculate a maximum net slip of 11.5±2 m and an average net slip of 6.4±0.2 m for the Papatea fault surface rupture in 2016. Large sinistral reverse displacement on the Papatea fault is consistent with uplift and southward escape of the Papatea block as observed from Interferometric Synthetic Aperture Radar (InSAR) and optical image correlation datasets. The throw and net slip are exceedingly high for the length of the Papatea fault; such large movements likely only occur during multifault Kaikōura‐type earthquakes that conceivably have recurrence times of ≥5000–12,000 yrs⁠. The role of the Papatea fault in the Kaikōura earthquake has significant implications for characterizing complex fault sources in seismic hazard models.