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Seismic Studies

Davy (2008) used marine seismic reection techniques to image the subsurface of the Waitemata Basin in high resolution at shallow depths, typically not exceeding 1500 m. The Davy (2008) study revealed the structure of the cover rock, and also penetrated into the westward-dipping Waipapa Terrane greywacke basement. The cover rock imaged was identied as Te Kuiti Group strata over the basement, overlain by Waitemata Group strata and some Auckland Volcanics.
Stern et al. (1987) used seismic refraction techniques to obtain p-wave velocity models for Cape Reinga, Lake Taupo, and Bream Trail and from these interpolated crustal velocities for the land in between, yielding crustal p-wave velocities of 5.4-6.36 kms?1 in the AVF, an average crustal thickness of 25 2 km , and mantle velocities increasing from 7.6 kms?1 at 25 km depth to 7.9 kms?1 at 80 km depth (Figure 1.6). Ashenden et al. (2011) also used seismic refraction techniques, to rene the crustal component of the Stern et al. (1987) model for use in Auckland. This yielded crustal p-wave velocities of 2.8 kms?1 at depths 0-1 km, 4.5 kms?1 at depths 1-4.5 km, 6.0 kms?1 at depths 4.6-27 km. Seismic refraction techniques, however, struggle to detect low-velocity layers.

Ocean Generated Seismic Noise

Surface gravity waves on the water-air interface of the oceans and seas continuously generate seismic waves in the solid earth (Schulte-Pelkum et al., 2004). The generation processes are not well understood but is expected to result from mechanisms such as surface gravity waves out at sea directly applying pressure to the sea oor, their interactions out at sea and along the coast (primary and reected wave interaction), and through direct interactions between of swell with the ocean oor in shallow waters (Ardhuin et al., 2011). The resulting microseisms are mostly Rayleigh waves (Haubrich and McCamy, 1969), a type of seismic surface wave. Ocean generated Rayleigh wave signals can have a periods the range 3-300 s, but the most energetic band sits within 3-10 s (Ardhuin et al., 2015).

Rayleigh Waves

Rayleigh waves are a combination of compression and vertical shear waves interacting near a free surface and travelling parallel to that surface (Strutt, 1885). In isotropic media, Rayleigh wave ground motion is elliptical in a plane that is perpendicular to the free surface and parallel to the propagation direction. Near the surface, the ground motion is retrograde but changes with depth to become pro-grade (Lay and Wallace, 1995). Rayleigh wave amplitudes attenuate exponentially with depth; amplitudes also decrease with radius r from the source, proportional to the largest amplitude waves that show up on vertical seismograms. This study uses the vertical components of the seismic records.

Surface Wave Tomography

Surface wave tomography is a well established method (Brune and Dorman, 1963; Bloch et al., 1969; Knopo, 1972; Kovach, 1978) to obtain information on the subsurface and is still widely used today (Du and Foulger, 2004; Feng et al., 2010; Khan et al., 2011). Ambient seismic noise tomography employs the same inversion and modelling techniques, but on stacked cross-correlation functions reconstructed from ambient seismic noise recorded at pairs of seismometers. A coherent dispersive surface wave signal emerges when many cross-correlations are stacked (Shapiro and Campillo, 2004), and performing surface wave tomography techniques on these signals has also been widely successful and often resolve structures in the upper crust at a higher resolution than normal surface wave tomography (Shapiro et al., 2005; Yao et al., 2006; Lin et al., 2008). Ambient seismic noise tomography typically involves seismometer pairs with inter-station distances that are hundreds to thousands of kilometres (Shapiro and Campillo, 2004). The inter-station distances between all seismometer pairs in this study are shorter than 65 km. Standard surface wave tomography studies exhibit dispersive signals at short distances and are increasingly
used for geotechnical purposes (Shaaban et al., 2013; Roy et al., 2013; Babacan et al., 2014), so by analogy, ambient seismic noise tomography may also be expected to work for short inter-station seismometer pairs. The method involves three stages to interrogate the subsurface:

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Cross-correlating ambient seismic noise time series for pairs of seismic sensors.

The cross-correlation of noise recorded on two stations can provide an estimate of the impulse response, or Green’s Function, between these stations. This means the resulting time series is an estimate of the waveeld as if there is a true seismic source at the one station, and a receiver at the other. A cross-correlation function obtained from 24 hour time series is noisy. It contains information, but it is specic to the noise source. Stacking many of these cross-correlation functions for a pair of seismometers incorporates signals from many sources and a broad range of frequencies.
A surface wave signal emerges that is a composite of the many weak surface wave signals that comprise the noise in the seismic record. The stacked cross-correlation functions provide an estimate of the surface wave impulse response between pairs of seismometers. Variation in impulse responses are expected to occur as a result of dierences in a number of things including, but not limited to, geological structures (Porritt et al., 2011), temperature proles (Sens-Schönfelder and Larose, 2008), average groundwater levels (Sens-Schö

1 Introduction
1.1 Tectonic and Geological Setting
1.2 Previous Studies .
1.3 Seismic Wave Sources
1.4 Surface Wave Tomography
2 Data Preparation
2.1 Data Format
2.2 Waveform Archive Format .
2.3 Instrument Response Correction .
2.4 RBAZ .
2.5 Conclusion
3 Reconstructing Impulse Responses
3.1 Introduction
3.2 MSNoise
3.3 Results and Discussion
3.4 Conclusion
4 Computation of Surface Wave Dispersion Data
4.1 Introduction .
4.2 do_mft
4.3 Rayleigh Wave Group and Phase Velocities in the AVF
4.4 Conclusion .
5 Inversion and Modelling
5.1 Introduction .
5.2 Modelling with surf96
5.3 Model Interpretation
6 Conclusion and Recommendations for Further Research
6.1 Main Findings .
6.2 Suggestions for Further Research