The Stone Jug Fault (SJF) ruptured during the November 14th, 2016 (at 12:02 am), Mw 7.8 Kaikōura Earthquake which initiated ~40 km west-southwest of the study area, at a depth of approximately 15 km. Preliminary post-earthquake mapping indicated that the SJF connects the Conway-Charwell and Hundalee faults, which form continuous surface rupture, however, detailed study of the SJF had not been undertaken prior to this thesis due to its remote location and mountainous topography. The SJF is 19 km long, has an average strike of ~160° and generally carries approximately equal components of sinistral and reverse displacement. The primary fault trace is sigmoidal in shape with the northern and southern tips rotating in strike from NNW to NW, as the SJF approaches the Hope and Hundalee faults. It comprises several steps and bends and is associated with many (N=48) secondary faults, which are commonly near irregularities in the main fault geometry and in a distributed fault zone at the southern tip. The SJF is generally parallel to Torlesse basement bedding where it may utilise pre-existing zones of weakness. Horizontal, vertical and net displacements range up to 1.4 m, with displacement profiles along the primary trace showing two main maxima separated by a minima towards the middle and ends of the fault. Average net displacement along the primary trace is ~0.4m, with local changes in relative values of horizontal and vertical displacement at least partly controlled by fault strike. Two trenches excavated across the northern segment of the fault revealed displacement of mainly Holocene stratigraphy dated using radiocarbon (N=2) and OSL (N=4) samples. Five surface-rupturing paleoearthquakes displaying vertical displacements of <1 m occurred at: 11,000±1000, 7500±1000, 6500±1000, 3500±100 and 3 (2016 Kaikōura) years BP. These events produce an average slip rate since ~11 ka of 0.2-0.4 mm/yr and recurrence intervals of up to 5500 years with an average recurrence interval of 2750 yrs. Comparison of these results with unpublished trench data suggests that synchronous rupture of the Hundalee, Stone Jug, Conway-Charwell, and Humps faults at ~3500 yrs BP cannot be discounted and it is possible that multi-fault ruptures in north Canterbury are more common than previously thought.
This dissertation addresses a diverse range of topics in the physics-based broadband ground motion simulation, with a focus on New Zealand applications. In particular the following topics are addressed: the methodology and computational implementation of a New Zealand Velocity Model for broadband ground motion simulation; generalised parametric functions and spatial correlations for seismic velocities in the Canterbury, New Zealand region from surface-wave-based site characterisation; and ground motion simulations of Hope Fault earthquakes. The paragraphs below outline each contribution in more detail. A necessary component in physics-based ground motion simulation is a 3D model which details the seismic velocities in the region of interest. Here a velocity model construction methodology, its computational implementation, and application in the construction of a New Zealand velocity model for use in physics-based broadband ground motion simulation are presented. The methodology utilises multiple datasets spanning different length scales, which is enabled via the use of modular sub-regions, geologic surfaces, and parametric representations of crustal velocity. A number of efficiency-related workflows to decrease the overall computational construction time are employed, while maintaining the flexibility and extensibility to incorporate additional datasets and re- fined velocity parameterizations as they become available. The model comprises explicit representations of the Canterbury, Wellington, Nelson-Tasman, Kaikoura, Marlborough, Waiau, Hanmer and Cheviot sedimentary basins embedded within a regional travel-time tomography-based velocity model for the shallow crust and provides the means to conduct ground motion simulations throughout New Zealand for the first time. Recently developed deep shear-wave velocity profiles in Canterbury enabled models that better characterise the velocity structure within geologic layers of the Canterbury sedimentary basin to be developed. Here the development of depth- and Vs30-dependent para-metric velocity and spatial correlation models to characterise shear-wave velocities within the geologic layers of the Canterbury sedimentary basin are presented. The models utilise data from 22 shear-wave velocity profiles of up to 2.5km depth (derived from surface wave analysis) juxtaposed with models which detail the three-dimensional structure of the geologic formations in the Canterbury sedimentary basin. Parametric velocity equations are presented for Fine Grained Sediments, Gravels, and Tertiary layer groupings. Spatial correlations were developed and applied to generate three-dimensional stochastic velocity perturbations. Collectively, these models enable seismic velocities to be realistically represented for applications such as 3D ground motion and site response simulations. Lastly the New Zealand velocity model is applied to simulate ground motions for a Mw7.51 rupture of the Hope Fault using a physics-based simulation methodology and a 3D crustal velocity model of New Zealand. The simulation methodology was validated for use in the region through comparison with observations for a suite of historic small magnitude earthquakes located proximal to the Hope Fault. Simulations are compared with conventionally utilised empirical ground motion models, with simulated peak ground velocities being notably higher in regions with modelled sedimentary basins. A sensitivity analysis was undertaken where the source characteristics of magnitude, stress parameter, hypocentre location and kinematic slip distribution were varied and an analysis of their effect on ground motion intensities is presented. It was found that the magnitude and stress parameter strongly influenced long and short period ground motion amplitudes, respectively. Ground motion intensities for the Hope Fault scenario are compared with the 2016 Kaikoura Mw7.8 earthquake, it was found that the Kaikoura earthquake produced stronger motions along the eastern South Island, while the Hope Fault scenario resulted in stronger motions immediately West of the near-fault region. The simulated ground motions for this scenario complement prior empirically-based estimates and are informative for mitigation and emergency planning purposes.
On 14 November 2016, the Mw 7.8 Kaikōura earthquake caused widespread damage along the east coast of the South Island, New Zealand. Kaikōura town itself was isolated from the rest of the country by landslides blocking off major roads. While impacts from the Kaikōura earthquake on large, urban population centres have been generally well documented, this thesis aims to fill gaps in academic knowledge regarding small rural towns. This thesis investigates what, where and when critical infrastructure and lifeline service disruption occurred following the 2016 Kaikōura earthquake in a selection of small towns, and how the communities in these areas adapted to disruption. Following a robust review of literature and news media, four small rural towns were selected from North Canterbury (Culverden & Waiau) and Marlborough (Seddon & Ward) in the South Island, New Zealand. Semi-structured interview sessions with a special focus on these towns were held with infrastructure managers, emergency response and recovery officials, and organisation leaders with experience or expertise in the 2016 Kaikōura earthquake. Findings were supplemented with emergency management situation reports to produce hazard maps and infrastructure exposure maps. A more detailed analysis was conducted for Waiau involving interdependence analyses and a level of service timeline for select lifeline services. The earthquake impacted roads by blocking them with landslides, debris and surface rupture. Bridges where shaken off their abutments, breaking infrastructure links such as fibre landlines as they went. Water supplies and other forms of infrastructure relied heavily on the level of service of roads, as rough rural terrain left few alternatives. Adapting to an artificial loss of road service, some Waiau locals created their own detour around a road cordon in order to get home to family and farms. Performance of dwellings was tied to socioeconomic factors as much as proximity to the epicentre. Farmers who lost water access pulled out fences to allow stock to drink from rivers. Socioeconomic differences between farmland and township residents also contributed to resilience variations between the towns assessed in this study. Understanding how small rural towns respond and adapt to disaster allows emergency management officials and policy to be well informed and flexible with planning for multiple size classes of towns.
