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

Wang, Xin; Zhan, Zhongwen; Zhong, Minyan; Persaud, Patricia; Clayton, Robert W.

doi:10.1029/2021jb022279

Cited by 30 publications

(54 citation statements)

References 49 publications

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“…In recent years, the deployment of dense seismic arrays makes it possible to resolve the fine-scale velocity structure of the top 5 km sedimentary layer (Castellanos and Clayton, 2021;Jia and Clayton, 2021;Lin et al, 2013). In addition to the ambient noise correlation, receiver functions are also evaluated from the dense array datasets to constrain the basement depth within the sedimentary basins Liu et al, 2018;Wang et al, 2021). Receiver function using our linear dense arrays has shown a coherent converted phase at the basin bottom can be observed in the SG and SB area, which provide an independent constraint on the basin structure in this area (Liu et al, 2018;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Shear Wave Velocities in the San Gabriel and San Bernardino Basins, California

Villa

Clayton

et al. 2022

Preprint

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We construct a 3D Vs model in San Gabriel and San Bernardino basins using ambient noise correlation between dense array nodal, broadband and accelerometer stations.• We separated the Rayleigh wave fundamental mode and first higher mode in dispersion analysis based on the Rayleigh wave particle motion.• Our Vs model predicts deeper and slower sedimentary basins than the SCEC CVMS model, yet is consistent with geological and drilling data in these basins.

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Section: Introductionmentioning

confidence: 99%

Shear Wave Velocities in the San Gabriel and San Bernardino Basins, California

Villa

Clayton

et al. 2022

Preprint

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“…High-resolution images of shallow structures could help the planning of buildings and the utilization of underground space, the simulation of ground motions (Bonnefoy-Claudet et al 2006), and the identification of sinkholes (Tran et al 2013), which are of great significance for city development. Seismological methods have been widely used for imaging and monitoring the subsurface by its high precision (Ma and Qian 2020), and remarkable results have been achieved (e.g., Foti et al 2011;Krawczyk et al 2012;Nakata et al 2011, 2015, Bora et al 2020Wang et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

Sensing Shallow Structure and Traffic Noise with Fiber-optic Internet Cables in an Urban Area

et al. 2021

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Distributed acoustic sensing (DAS) is a novel seismic observation system developed in recent years that can realize ultrahigh density observations and has attracted extensive attention in the field of seismology. DAS uses fiber-optic cables as sensing units, which are easy to incorporate with urban telecommunication fiber-optic cables for seismological observations. Compared with seismometers, DAS has the advantages of being rapidly deployed and recyclable, being able to acquire dense observations at low cost, and convenient data collection. In this study, a 5.2 km long telecom fiber-optic internet cable was utilized as a DAS array in an urban area to record ambient noise, and the noise cross-correlation function (NCF) was calculated. There are two different distribution types of ambient noise sources along the cable, regular along-road trucks (Taihe Road) and complex ambient noise, including human activities and traffic sources along and across the Jinniu road. In the first case, we constructed a 2D S-wave velocity model down to 100 m depth and a low-velocity zone was revealed. The S-wave model well explained the traffic signal along the Taihe road and the low-velocity zone is also consistent with the results obtained from co-located geophone arrays. In the second case, due to the complexity of the traffic noise distribution, empirical Green's functions were barely achieved. Therefore, we performed a synthetic test obtaining different NCFs with different source distributions, and two specific cases that dominate the NCF results were matched. Finally, we obtained the traffic noise distribution along the road, which is consistent with the power spectra density of the ambient noise. In conclusion, by combining DAS and urban fiber-optic internet cables with urban traffic noise, we can effectively reveal the traffic activities and image shallow structures with high resolution, which could offer a reference for urban construction and disaster prevention. Keywords Distributed acoustic sensing • Urban area • Ambient noise tomography • Ambient noise source distribution Article Highlights• DAS turns the urban fiber-optic internet cables into ultra-dense permanent seismic observation arrays • We revealed a high-resolution shallow structure using urban fiber-optic internet cables • We obtained the distribution of traffic activities along the road

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“…Further, passive methods with nodal arrays can resolve the structure of sedimentary basins in urban environments, where conventional active source seismic techniques are too obtrusive. Nodal arrays and passive imaging techniques such as interferometry (Castellanos & Clayton, 2021) and receiver functions (X. Wang et al., 2021) provide an alternative path to characterizing seismic hazard at high resolution.…”

Section: Science Enabled By Big Data Seismologymentioning

confidence: 99%

“…The use of active source surveys in urban settings is costly, intrusive, and logistically challenging. The analysis of data from the Long Beach array, and additional Large‐N nodal arrays deployed in the Los Angeles area, have utilized passive techniques including ambient noise interferometry of surface waves (Jia & Clayton, 2021; Lin et al., 2013), body waves (Nakata et al., 2015), and receiver functions (X. Wang et al., 2021) to reconstruct high‐resolution 3D velocity models from the surface to depths of a few kilometers. 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.…”

Section: Science Enabled By Big Data Seismologymentioning

confidence: 99%

Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

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The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

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

Cited by 30 publications

References 49 publications

Shear Wave Velocities in the San Gabriel and San Bernardino Basins, California

Shear Wave Velocities in the San Gabriel and San Bernardino Basins, California

Sensing Shallow Structure and Traffic Noise with Fiber-optic Internet Cables in an Urban Area

Big Data Seismology

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