Large‐Scale Crustal Structure Beneath Singapore Using Receiver Functions From a Dense Urban Nodal Array

Abstract

Geophysics has a role to play in the development of "smart cities". This is particularly true for Singapore, one of the world's most densely populated countries. Imaging of Singapore's subsurface is required to identify geological faults, model shaking from future earthquakes, and provide a framework for underground development. A noninvasive geophysical technique that is well suited for urban areas is passive seismic surveys using compact seismic instruments called nodes. Here we image Singapore's crustal structure using receiver functions generated by a 40-day deployment of a 88 station nodal array. We generate high resolution receiver functions, despite the noisy environment and short recording time and also create common-conversion point images. Our results reveal a complex crustal structure, containing multiple discontinuities. Azimuthal variations indicate a distinct change in crustal structure on either side of the postulated Bukit Timah fault, which has implications for seismic hazard.Plain Language Summary By 2050 over two thirds of the global population is expected to live in cities (United Nations, Department of Economic and Social Affairs, Population Division, 2019). Such dense population centers increase the exposure to nearby natural hazards such as earthquakes. Rapid urbanization also creates an ever pressing need to understand the subsurface of our cities, for example, for underground development. One technological advancement that allows us to image the subsurface of cities are passive seismic surveys using nodes. Nodes are small seismic instruments that can be deployed in urban areas such as schools and parks. We present results of a passive seismic survey using 88 nodes deployed in Singapore for 40 days. We generate high resolution receiver functions across the array, despite the short recording time, by using array stacking techniques. The dense nature of the array also allows continuous high frequency signals to be traced. Our results reveal that the structure of the crust is different on either side of the Bukit Timah fault, which agrees with geological information. Ancient faults in peninsula Malaysia have been reactivated in the recent past due to stress transfer from the Sumatran subduction zone; therefore, a geological fault under a densely populated urban center warrants further study for seismic hazard.

Urban Basin Structure Imaging Based on Dense Arrays and Bayesian Array‐Based Coherent Receiver Functions

“…To address these challenges, passive source methods using earthquake data and ambient noise have been used in mapping basin structure in urban areas (Agostinetti et al, 2018;Cunningham & Lekic, 2019;Finlay et al, 2019;Lin et al, 2013;Liu et al, 2018;Ma & Clayton, 2016;Viens et al, 2016;Yeck et al, 2013). The development of easy-to-deploy and robust nodal-type sensors has further facilitated the deployment of large-N arrays in urban environments for high-resolution basin imaging (Clayton, 2020;Finlay et al, 2019;Lin et al, 2013;Liu et al, 2018;Lythgoe et al, 2020).…”

Pervasive Crustal Volcanic Mush in the Highly Stretched Sunda Plate Margin of Northern Sumatra

“…Our nodal array observations have a high level of noise, likely due to the poor deployment condition. Fortunately, the dense array allows for spatial stacking to increase the signal‐to‐noise ratio (SNR) (Lythgoe et al., 2020; Ward et al., 2018). Here we apply a spatial stacking technique, called “Virtual Station Stacking,” to group RF traces.…”

Big Data Seismology

“…High-resolution velocity models can provide important constraints for earthquake hazard assessment, which relies on models of peak ground acceleration (Castellanos & Clayton, 2021), a property that is very sensitive to crustal properties and can vary spatially across multiple scales. Nodal arrays have subsequently been deployed in other urban areas including Albuquerque and Singapore (Finlay et al, 2020;Lythgoe et al, 2020). As discussed in the section on non-earthquake sources, sensors in urban environments record a rich variety of signals from anthropogenic sources.…”