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Research papers, University of Canterbury Library

Abstract. Natural (e.g., earthquake, flood, wildfires) and human-made (e.g., terrorism, civil strife) disasters are inevitable, can cause extensive disruption, and produce chronic and disabling psychological injuries leading to formal diagnoses (e.g., post-traumatic stress disorder [PTSD]). Following natural disasters of earthquake (Christchurch, Aotearoa/New Zealand, 2010–11) and flood (Calgary, Canada, 2013), controlled research showed statistically and clinically significant reductions in psychological distress for survivors who consumed minerals and vitamins (micronutrients) in the following months. Following a mass shooting in Christchurch (March 15, 2019), where a gunman entered mosques during Friday prayers and killed and injured many people, micronutrients were offered to survivors as a clinical service based on translational science principles and adapted to be culturally appropriate. In this first translational science study in the area of nutrition and disasters, clinical results were reported for 24 clients who completed the Impact of Event Scale – Revised (IES-R), the Depression Anxiety Stress Scales (DASS), and the Modified-Clinical Global Impression (M-CGI-I). The findings clearly replicated prior controlled research. The IES-R Cohen’s d ESs were 1.1 (earthquake), 1.2 (flood), and 1.13 (massacre). Effect sizes (ESs) for the DASS subscales were also consistently positive across all three events. The M-CGI-I identified 58% of the survivors as “responders” (i.e., self-reported as “much” to “very much” improved), in line with those reported in the earthquake (42%) and flood (57%) randomized controlled trials, and PTSD risk reduced from 75% to 17%. Given ease of use and large ESs, this evidence supports the routine use of micronutrients by disaster survivors as part of governmental response.

Research papers, The University of Auckland Library

This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.

Research papers, Victoria University of Wellington

The last seven years have seen southern New Zealand a ected by several large and damaging earthquakes: the moment magnitude (MW) 7.8 Dusky Sound earthquake on 15 July 2009, the MW 7.1 Dar eld (Canterbury) earthquake on 4 September 2010, and most notably the MW 6.2 Christchurch earthquake on 22 February 2011 and the protracted aftershock sequence. In this thesis, we address the postseismic displacement produced by these earthquakes using methods of satellite-based geodetic measurement, known as Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS), and computational modelling.  We observe several ground displacement features in the Canterbury and Fiordland regions during three periods: 1) Following the Dusky Sound earthquake; 2) Following the Dar eld earthquake and prior to the Christchurch earthquake; and 3) Following the Christchurch earthquake until February 2015.  The ground displacement associated with postseismic motion following the Dusky Sound earthquake has been measured by continuous and campaign GPS data acquired in August 2009, in conjunction with Di erential Interferometric Synthetic Aperture Radar (DInSAR) observations. We use an afterslip model, estimated by temporal inversion of geodetic data, with combined viscoelastic rebound model to account for the observed spatio-temporal patterns of displacement. The two postseismic processes together induce a signi cant displacement corresponding to principal extensional and contractual strain rates of the order of 10⁻⁷ and 10⁻⁸ yr⁻¹ respectively, across most of the southern South Island.  We also analyse observed postseismic displacement following the Dusky Sound earthquake using a new inversion approach in order to describe afterslip in an elasticviscoelastic medium. We develop a mathematical framework, namely the "Iterative Decoupling of Afterslip and Viscoelastic rebound (IDAV)" method, with which to invert temporally dense and spatially sparse geodetic observations. We examine the IDAV method using both numerical and analytical simulations of Green's functions.  For the post-Dar eld time interval, postseismic signals are measured within approximately one month of the mainshock. The dataset used for the post-Dar eld displacement spans the region surrounding previously unrecognised faults that ruptured during the mainshock. Poroelastic rebound in a multi-layered half-space and dilatancy recovery at shallow depths provide a satisfactory t with the observations.  For the post-Christchurch interval, campaign GPS data acquired in February 2012 to February 2015 in four successive epochs and 66 TerraSAR-X (TSX) SAR acquisitions in descending orbits between March 2011 and May 2014 reveal approximately three years of postseismic displacement. We detect movement away from the satellite of ~ 3 mm/yr in Christchurch and a gradient of displacement of ~ 4 mm/yr across a lineament extending from the westernmost end of the Western Christchurch Fault towards the eastern end of the Greendale East Fault. The postseismic signals following the Christchurch earthquake are mainly accounted for by afterslip models on the subsurface lineament and nearby faults.