Background and methodology The Mw 7.8, 14th November 2016 earthquake centred (item b, figure 1) in the Hurunui District of the South Island, New Zealand, damaged critical infrastructure across North Canterbury and Marlborough. We investigate the impacts to infrastructure and adaptations to the resulting service disruption in four small rural towns (figure 1): Culverden (a), Waiau (c), Ward (d) and Seddon (e). This is accomplished though literary research, interviews and geospatial analysis. Illustrating our methods, we have displayed here a Hurunui District hazard map (figure 2b) and select infrastructure inventories (figures 2a, 3).
Motivation This poster aims to present fragility functions for pipelines buried in liquefaction-prone soils. Existing fragility models used to quantify losses can be based on old data or use complex metrics. Addressing these issues, the proposed functions are based on the Christchurch network and soil and utilizes the Canterbury earthquake sequence (CES) data, partially represented in Figure 1. Figure 1 (a) presents the pipe failure dataset, which describes the date, location and pipe on which failures occurred. Figure 1 (b) shows the simulated ground motion intensity median of the 22nd February 2011 earthquake. To develop the model, the network and soil characteristics have also been utilized.
This poster presents work to date on ground motion simulation validation and inversion for the Canterbury, New Zealand region. Recent developments have focused on the collection of different earthquake sources and the verification of the SPECFEM3D software package in forward and inverse simulations. SPECFEM3D is an open source software package which simulates seismic wave propagation and performs adjoint tomography based upon the spectral-element method. Figure 2: Fence diagrams of shear wave velocities highlighting the salient features of the (a) 1D Canterbury velocity model, and (b) 3D Canterbury velocity model. Figure 5: Seismic sources and strong motion stations in the South Island of New Zealand, and corresponding ray paths of observed ground motions. Figure 3: Domain used for the 19th October 2010 Mw 4.8 case study event including the location of the seismic source and strong motion stations. By understanding the predictive and inversion capabilities of SPECFEM3D, the current 3D Canterbury Velocity Model can be iteratively improved to better predict the observed ground motions. This is achieved by minimizing the misfit between observed and simulated ground motions using the built-in optimization algorithm. Figure 1 shows the Canterbury Velocity Model domain considered including the locations of small-to-moderate Mw events [3-4.5], strong motion stations, and ray paths of observed ground motions. The area covered by the ray paths essentially indicates the area of the model which will be most affected by the waveform inversion. The seismic sources used in the ground motion simulations are centroid moment tensor solutions obtained from GeoNet. All earthquake ruptures are modelled as point sources with a Gaussian source time function. The minimum Mw limit is enforced to ensure good signal-to-noise ratio and well constrained source parameters. The maximum Mw limit is enforced to ensure the point source approximation is valid and to minimize off-fault nonlinear effects.
Introduction This poster presents the inferred initial performance and recovery of the water supply network of Christchurch following the 22 February 2011 Mw 6.2 earthquake. Results are presented in a geospatial and temporal fashion. This work strengthens the current understanding of the restoration of such a system after a disaster and quantifies the losses caused by this earthquake in respect with the Christchurch community. Figure 1 presents the topology of the water supply network as well as the spatial distribution of the buildings and their use.
INTRODUCTION This project falls under the Flagship 3: Wellington Coordinated Project. It supports other projects within FP3 to create a holistic understanding of risks posed by collapsed buildings due to future earthquake/s and the secondary consequences of cordoning in the short, mid and long term. Cordoning of the Christchurch CBD for more than two years and its subsequent implications on people and businesses had a significant impact on the recovery of Christchurch. Learning from this and experiences from the Kaikōura earthquake (where cordons were also established around selected buildings, Figure 3) have highlighted the need to understand the effects of cordons and plan for it before an earthquake occurs
Heathcote Valley school strong motion station (HVSC) consistently recorded ground motions with higher intensities than nearby stations during the 2010-2011 Canterbury earthquakes. For example, as shown in Figure 1, for the 22 February 2011 Christchurch earthquake, peak ground acceleration at HVSC reached 1.4 g (horizontal) and 2 g (vertical), the largest ever recorded in New Zealand. Strong amplification of ground motions is expected at Heathcote Valley due to: 1) the high impedance contrast at the soil-rock interface, and 2) the interference of incident and surface waves within the valley. However, both conventional empirical ground motion prediction equations (GMPE) and the physics-based large scale ground motions simulations (with empirical site response) are ineffective in predicting such amplification due to their respective inherent limitations.
Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.
essential systems upon which the well-being and functioning of societies depend. They deliver a service or a good to the population using a network, a combination of spatially-distributed links and nodes. As they are interconnected, network elements’ functionality is also interdependent. In case of a failure of one component, many others could be momentarily brought out-of-service. Further problems arise for buried infrastructure when it comes to buried infrastructure in earthquake and liquefaction-prone areas for the following reasons: • Technically more demanding inspections than those required for surface horizontal infrastructure • Infrastructure subject to both permanent ground displacement and transient ground deformation • Increase in network maintenance costs (i.e. deterioration due to ageing material and seismic hazard) These challenges suggest careful studies on network resilience will yield significant benefits. For these reasons, the potable water network of Christchurch city (Figure 1) has been selected for its well-characterized topology and its extensive repair dataset.
1. Background and Objectives This poster presents results from ground motion simulations of small-to-moderate magnitude (3.5≤Mw≤5.0) earthquake events in the Canterbury, New Zealand region using the Graves and Pitarka (2010,2015) methodology. Subsequent investigation of systematic ground motion effects highlights the prediction bias in the simulations which are also benchmarked against empirical ground motion models (e.g. Bradley (2013)). In this study, 144 earthquake ruptures, modelled as point sources, are considered with 1924 quality-assured ground motions recorded across 45 strong motion stations throughout the Canterbury region, as shown in Figure 1. The majority of sources are Mw≥4.0 and have centroid depth (CD) 10km or shallower. Earthquake source descriptions were obtained from the GeoNet New Zealand earthquake catalogue. The ground motion simulations were performed within a computational domain of 140km x 120km x 46km with a finite difference grid spacing of 0.1km. The low-frequency (LF) simulations utilize the 3D Canterbury Velocity Model while the high-frequency (HF) simulations utilize a generic regional 1D velocity model. In the LF simulations, a minimum shear wave velocity of 500m/s is enforced, yielding a maximum frequency of 1.0Hz.
In this paper we apply Full waveform tomography (FWT) based on the Adjoint-Wavefield (AW) method to iteratively invert a 3-D geophysical velocity model for the Canterbury region (Lee, 2017) from a simple initial model. The seismic wavefields was generated using numerical solution of the 3-D elastodynamic/ visco- elastodynamic equations (EMOD3D was adopted (Graves, 1996)), and through the AW method, gradients of model parameters (compression and shear wave velocity) were computed by implementing the cross-adjoint of forward and backward wavefields. The reversed-in-time displacement residual was utilized as the adjoint source. For inversion, we also account for the near source/ station effects, gradient precondition, smoothening (Gaussian filter in spatial domain) and optimal step length. Simulation-to-observation misfit measurements based on 191 sources at 78 seismic stations in the Canterbury region (Figure 1) were used into our inversion. The inversion process includes multiple frequency bands, starting from 0-0.05Hz, and advancing to higher frequency bands (0-0.1Hz and 0-0.2Hz). Each frequency band was used for up to 10 iterations or no optimal step length found. After 3 FWT inversion runs, the simulated seismograms computed using our final model show a good matching with the observed seismograms at frequencies from 0 - 0.2 Hz and the normalized least-squared misfit error has been significantly reduced. Over all, the synthetic study of FWT shows a good application to improve the crustal velocity models from the existed geological models and the seismic data of the different earthquake events happened in the Canterbury region.
