Using ambient noise tomography and MAPS for high resolution stratigraphic identification in Hangzhou urban area

Chen, Xinhua; Zhang, Hongyu; Zhou, Changjiang; Pang, Jingyin; Xing, Huaixue; Chang, Xiaojun

doi:10.1016/j.jappgeo.2021.104327

Cited by 12 publications

(2 citation statements)

References 39 publications

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“…Ambient Noise Tomography (ANT) is an excellent seismological technique that can be used for imaging shallow sub-surface of planetary bodies where occurrences of quakes are infrequent (e.g., Shapiro & Campillo, 2004;Shapiro et al, 2005). It provides a high-resolution 3D shear wave velocity (V S ) image of the planetary upper crust up to a few hundred meters depth (Chen et al, 2021;Mordret et al, 2014Mordret et al, , 2019, and in some cases, it could be up to a few 10 km deep (Lin et al, 2007;Nthaba et al, 2022;Yao et al, 2006). Hence, this technique is useful for imaging the subsurface of impact craters.…”

Section: Introductionmentioning

confidence: 99%

Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt, With Planetary Applications

Kumari,

Gupta,

Senthil Kumar

2024

JGR Planets

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Meteoroid impacts produce different types of fractures and damage zones beneath impact craters. The 3D geometry of these features reflects the trajectory and energetics of an impact event. In this study, we mapped the impact damage zone beneath the 1.88‐km‐diameter Lonar crater, emplaced in Deccan basalts, using Ambient Noise Tomography (ANT). A network of 23 broadband seismic stations in and around the crater yielded a 1.2 km deep 3D shear wave velocity (VS) image covering ∼7 km by ∼5 km area. It revealed ∼500–900‐m‐thick heterogeneous target basalt flows, underlain by an undulating Archean granite‐gneiss basement. A substantial reduction in VS is observed beneath the crater. The original crater floor was found at a depth of 400 m below the crater rim, which is filled by impact breccia and lake sediments. Beneath the original floor, we found an oval‐shaped, asymmetric 200‐m‐thick lensoidal low‐velocity layer with a tongue‐like feature beneath the southwestern ejecta blanket. The damage zone is inferred to have formed as a result of oblique impact, in which the projectile arrived from northeast to southwest direction. The VS reduction in the low‐velocity layer was used to calculate the amount of impact damage in it. The oblique impact produced a more elevated southwestern crater rim. Impact‐related near‐surface fracture zones up to a radial distance of >1 km beneath the ejecta blanket were also found. We suggest that impact damage beneath impact craters on Earth and other planetary bodies may be imaged using ANT.

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Section: Introductionmentioning

confidence: 99%

Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt, With Planetary Applications

Kumari,

Gupta,

Senthil Kumar

2024

JGR Planets

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“…After getting rid of the shackles of artificial sources, passive surface-wave surveys prefer to use the dense seismic array to achieve higher accuracy and resolution [3]. Although the dense array promotes the resolution, it will bring a tremendous workload.…”

Section: Introductionmentioning

confidence: 99%

Detection of Low-Velocity Layer Using Passive Surface-Wave Data and Deep Learning: An Application in The Urban Area of Hangzhou, China

Chen,

Xing,

Pang

et al. 2023

J. Phys.: Conf. Ser.

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Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) can release a tremendous workload brought by dense seismic arrays. We presented a case study of shear-wave velocity (Vs) structure imaging in the Hangzhou urban area (eastern China) using DL inversion. Noise data were recorded by dense linear arrays with approximately 5 m spacing deployed along two crossing roads for investigating the top 80 m of the subsurface. Phase-velocity dispersion curves are extracted from virtual shot gathers using multichannel analysis of surface waves. We divided the area where the low-velocity layer (LVL) may exist into three layers with a thickness of 5 m. We gave the four layers weak constraints to generate training dataset and adopted a convolutional neural network to directly invert fundamental-mode Rayleigh-wave phase velocity for 1D Vs models. To improve the accuracy, we further applied the sensitivities to weight the loss function in DL inversion. The obtained pseudo-2D Vs profiles correspond to the velocities estimated from logging data and previous survey. The well-trained neural network successfully identified that the LVL is located at 50-60 m deep. And this network was also achieved accurately the inversion of a dense seismic network nearby. The results of this survey demonstrate the accuracy and efficiency of delineating near-surface structures from traffic-induced noise using the DL technique, which has great potential for monitoring subsurface changes in urban areas.

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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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Using ambient noise tomography and MAPS for high resolution stratigraphic identification in Hangzhou urban area

Cited by 12 publications

References 39 publications

Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt, With Planetary Applications

Ambient Noise Tomography Reveals Asymmetric Impact Damage Zone Beneath Lonar Crater, India: Implications for Oblique Impact Cratering in Heterogeneous Basalt, With Planetary Applications

Detection of Low-Velocity Layer Using Passive Surface-Wave Data and Deep Learning: An Application in The Urban Area of Hangzhou, China

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

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