Imaging New Zealand's Crustal Structure Using Ambient Seismic Noise Recordings from Permanent and Temporary Instruments

<p>We use ambient seismic noise to image the crust and uppermost mantle, and to determine the spatiotemporal characteristics of the noise field itself, and examine the way in which those characteristics may influence imaging results. Surface wave information extracted from ambient seismic noise using cross-correlation methods significantly enhances our knowledge of the crustal and uppermost mantle shear-velocity structure of New Zealand. We assemble a large dataset of three-component broadband continuous seismic data from temporary and permanent seismic stations, increasing the achievable resolution of surface wave velocity maps in comparison to a previous study. Three-component data enables us to examine both Rayleigh and Love waves using noise cross-correlation functions. Employing a Monte Carlo inversion method, we invert Rayleigh and Love wave phase and group velocity dispersion curves separately for spatially averaged isotropic shear velocity models beneath the Northland Peninsula. The results yield first-order radial anisotropy estimates of 2% in the upper crust and up to 15% in the lower crust, and estimates of Moho depth and uppermost mantle velocity compatible with previous studies. We also construct a high-resolution, pseudo-3D image of the shear-velocity distribution in the crust and uppermost mantle beneath the central North Island using Rayleigh and Love waves. We document, for the first time, the lateral extent of low shear-velocity zones in the upper and mid-crust beneath the highly active Taupo Volcanic Zone, which have been reported previously based on spatially confined 1D shear-velocity profiles. Attributing these low shear-velocities to the presence of partial melt, we use an empirical relation to estimate an average percentage of partial melt of < 4:2% in the upper and middle crust. Analysis of the ambient seismic noise field in the North Island using plane wave beamforming and slant stacking indicates that higher mode Rayleigh waves can be detected, in addition to the fundamental mode. The azimuthal distributions of seismic noise sources inferred from beamforming are compatible with high near-coastal ocean wave heights in the period band of the secondary microseism (~7 s). Averaged over 130 days, the distribution of seismic noise sources is azimuthally homogeneous, indicating that the seismic noise field is well-suited to noise cross-correlation studies. This is underpinned by the good agreement of our results with those from previous studies. The effective homogeneity of the seismic noise field and the large dataset of noise cross-correlation functions we here compiled, provide the cornerstone for future studies of ambient seismic noise and crustal shear velocity structure in New Zealand.</p>

Imaging New Zealand's Crustal Structure Using Ambient Seismic Noise Recordings from Permanent and Temporary Instruments

<p>We use ambient seismic noise to image the crust and uppermost mantle, and to determine the spatiotemporal characteristics of the noise field itself, and examine the way in which those characteristics may influence imaging results. Surface wave information extracted from ambient seismic noise using cross-correlation methods significantly enhances our knowledge of the crustal and uppermost mantle shear-velocity structure of New Zealand. We assemble a large dataset of three-component broadband continuous seismic data from temporary and permanent seismic stations, increasing the achievable resolution of surface wave velocity maps in comparison to a previous study. Three-component data enables us to examine both Rayleigh and Love waves using noise cross-correlation functions. Employing a Monte Carlo inversion method, we invert Rayleigh and Love wave phase and group velocity dispersion curves separately for spatially averaged isotropic shear velocity models beneath the Northland Peninsula. The results yield first-order radial anisotropy estimates of 2% in the upper crust and up to 15% in the lower crust, and estimates of Moho depth and uppermost mantle velocity compatible with previous studies. We also construct a high-resolution, pseudo-3D image of the shear-velocity distribution in the crust and uppermost mantle beneath the central North Island using Rayleigh and Love waves. We document, for the first time, the lateral extent of low shear-velocity zones in the upper and mid-crust beneath the highly active Taupo Volcanic Zone, which have been reported previously based on spatially confined 1D shear-velocity profiles. Attributing these low shear-velocities to the presence of partial melt, we use an empirical relation to estimate an average percentage of partial melt of < 4:2% in the upper and middle crust. Analysis of the ambient seismic noise field in the North Island using plane wave beamforming and slant stacking indicates that higher mode Rayleigh waves can be detected, in addition to the fundamental mode. The azimuthal distributions of seismic noise sources inferred from beamforming are compatible with high near-coastal ocean wave heights in the period band of the secondary microseism (~7 s). Averaged over 130 days, the distribution of seismic noise sources is azimuthally homogeneous, indicating that the seismic noise field is well-suited to noise cross-correlation studies. This is underpinned by the good agreement of our results with those from previous studies. The effective homogeneity of the seismic noise field and the large dataset of noise cross-correlation functions we here compiled, provide the cornerstone for future studies of ambient seismic noise and crustal shear velocity structure in New Zealand.</p>

On the generation of geometry-independent noise models for microseismic monitoring purposes

&lt;p&gt;As a result of the world-wide interest in carbon storage and geothermal energy production, increased emphasis is nowadays placed on the development of reliable microseismic monitoring techniques for hazard monitoring related to fluid movement and reactivation of faults. In the process of developing and benchmarking these techniques, the incorporation of realistic noise into synthetic datasets is of vital importance to predict their effectiveness once deployed in the real world. Similarly, the recent widespread use of Machine Learning in seismological applications calls for the creation of synthetic seismic datasets that are indistinguishable from the field data to which they will be applied.&amp;#160;&lt;/p&gt;&lt;p&gt;Noise generation procedures can be split into two categories: model-based and data-driven. The distributed surface sources approach is the most common method in the first category: however, it is well-known that this fails to capture the complexity of recorded noise (Dean et al., 2015). Pearce and Barley (1977)&amp;#8217;s convolutional approach offers a data-driven procedure that has the ability to accurately capture the frequency content of noise however imposes that noise must be stationary. Birnie et al. (2016)&amp;#8217;s covariance-based approach removes the stationarity requirement accurately capturing spatio-temporal characterisations of noise, however, like all other data-driven approaches it is constrained to the survey geometry in which the noise data has been collected.&amp;#160;&lt;/p&gt;&lt;p&gt;In this work, we propose an extension of the covariance-based noise modelling workflow that aims to generate a noise model over a user-defined geometry. The extended workflow comprises of two steps: the first step is responsible for the characterisation of the recorded noise field and the generation of multiple realisations with the same statistical properties, constrained to the original acquisition geometry. Gaussian Process Regression (GPR) is subsequently applied over each time slice of the noise model transforming the model into the desired geometry.&lt;/p&gt;&lt;p&gt;The workflow is initially validated on synthetically generated noise with a user-defined input covariance matrix. This allows us to prove that the noise statistics (i.e., covariance and variogram) can be kept almost identical between the noise extracted from the synthetic dataset and the various steps of the noise model procedure. The workflow is further applied to the openly available ToC2ME passive dataset from Alberta, Canada consisting of 69 geophones arranged in a pseudo-random pattern. The noise is modelled and transformed into a 56-sensor, gridded array, which is shown to a very close resemblance to the recorded noise field.&amp;#160;&lt;/p&gt;&lt;p&gt;Whilst the importance of using realistic noise in synthetic datasets for benchmarking algorithms or training ML solutions cannot be overstated, the ability to transform such noise models into arbitrary receiver geometries opens up a host of new opportunities in the area of survey design. We argue that by coupling the noise generation and monitoring algorithms, the placement of sensors can be optimized based on the expected microseismic signatures as well as the surrounding noise behaviour. This could be of particular interest for geothermal and CO&lt;sub&gt;2&lt;/sub&gt; storage sites where processing plants are likely to be in close proximity to the permanent monitoring stations.&lt;/p&gt;

Shear velocity structure of the Northland Peninsula, New Zealand, inferred from ambient noise correlations

Ambient noise correlation has been successfully applied in several cases to regions with dense seismic networks whose geometries are well suited to tomographic imaging. The utility of ambient noise correlation-based methods of seismic imaging where either network or noise field characteristics are less ideal has yet to be fully demonstrated. In this study, we focus on the Northland Peninsula of New Zealand using data from five seismographs deployed in a linear pattern parallel to the direction from which most of the ambient noise arrives. Shear wave velocity profiles computed from Rayleigh and Love wave dispersion curves using the Neighborhood Algorithm are in good agreement with the results of a previous active source refraction experiment and a teleseismic receiver function and surface wave analysis. In particular, we compute a path-averaged Moho depth of ̃28 km along a ̃250 km profile. The use of both Rayleigh and Love wave measurements enables us to estimate the degree of radial anisotropy in the crust, yielding values of 2-15%. These results demonstrate that ambient noise correlation methods provide useful geophysical constraints on lithospheric structure even for nonoptimal network geometries and noise field characteristics. © 2010 by the American Geophysical Union.

Shear velocity structure of the Northland Peninsula, New Zealand, inferred from ambient noise correlations

Ambient noise correlation has been successfully applied in several cases to regions with dense seismic networks whose geometries are well suited to tomographic imaging. The utility of ambient noise correlation-based methods of seismic imaging where either network or noise field characteristics are less ideal has yet to be fully demonstrated. In this study, we focus on the Northland Peninsula of New Zealand using data from five seismographs deployed in a linear pattern parallel to the direction from which most of the ambient noise arrives. Shear wave velocity profiles computed from Rayleigh and Love wave dispersion curves using the Neighborhood Algorithm are in good agreement with the results of a previous active source refraction experiment and a teleseismic receiver function and surface wave analysis. In particular, we compute a path-averaged Moho depth of ̃28 km along a ̃250 km profile. The use of both Rayleigh and Love wave measurements enables us to estimate the degree of radial anisotropy in the crust, yielding values of 2-15%. These results demonstrate that ambient noise correlation methods provide useful geophysical constraints on lithospheric structure even for nonoptimal network geometries and noise field characteristics. © 2010 by the American Geophysical Union.

Fundamental and higher-mode Rayleigh wave characteristics of ambient seismic noise in New Zealand

In order to use ambient seismic noise for mapping Earth's structure, it is important to understand the spatiotemporal characteristics of the noise field. This study uses data collected during four austral winter months of 2002 to investigate New Zealand's ambient seismic noise field in the double-ocean-wave-frequency range (0.1-0.3 Hz). It is shown via beamforming analysis that there are two distinct dispersive waves in the data. These waves can be separated. Their estimated phase velocities (2.5-2 and 4-3 km/s in the frequency range 0.14-0.25 Hz) match well with fundamental and higher-mode Rayleigh dispersion curves. Studies of double-wave-frequency microseisms elsewhere generally show the Rayleigh noise fields to be dominated by fundamental mode waves. The reason why higher-mode signals are observed here may reflect a combination of long ocean wave periods, large waveheights, the direct deep water approach to narrow continental margins, and the proximity of the seismograph array to the source regions. Copyright 2009 by the American Geophysical Union.

Fundamental and higher-mode Rayleigh wave characteristics of ambient seismic noise in New Zealand

In order to use ambient seismic noise for mapping Earth's structure, it is important to understand the spatiotemporal characteristics of the noise field. This study uses data collected during four austral winter months of 2002 to investigate New Zealand's ambient seismic noise field in the double-ocean-wave-frequency range (0.1-0.3 Hz). It is shown via beamforming analysis that there are two distinct dispersive waves in the data. These waves can be separated. Their estimated phase velocities (2.5-2 and 4-3 km/s in the frequency range 0.14-0.25 Hz) match well with fundamental and higher-mode Rayleigh dispersion curves. Studies of double-wave-frequency microseisms elsewhere generally show the Rayleigh noise fields to be dominated by fundamental mode waves. The reason why higher-mode signals are observed here may reflect a combination of long ocean wave periods, large waveheights, the direct deep water approach to narrow continental margins, and the proximity of the seismograph array to the source regions. Copyright 2009 by the American Geophysical Union.