Yumin Zhao

Ambient noise sources in urban environments are generally not perfectly uniformly distributed or travel exactly along the axial direction of the receiver array. The horizontally deployed distributed acoustic sensing (DAS) fiber‐optic cable is able to record both Rayleigh and Love waves. Therefore, resolving the source propagation direction and the content of Rayleigh and Love waves from the DAS ambient noise data is essential to resolve a reliable near‐surface shear‐wave velocity model. However, a standard geophone‐based beamforming procedure may not apply to DAS ambient noise data due to the complex measurements of DAS. To address this issue, we investigate the validity of beamforming results by analyzing the simulated seismic ambient noise recorded by a DAS array close to an L‐shape under different source conditions. Synthetic examples demonstrate the importance of correcting amplitude imbalances and polarity reversals in the DAS ambient noise data to ensure a reliable beamforming analysis. In addition, we estimate the Rayleigh‐to‐Love wave (R/L) ratio from the DAS ambient noise data with the resolved source direction, the known array geometry, and the measured amplitude information. Finally, we apply the method to two 2 min DAS ambient noise data recorded by the Stanford DAS Array‐1. The results suggest that the analyzed data were mainly generated from the local traffic and dominated by Love waves.

Seismic ambient noise contains rich information about the physical properties of the critical zone. From the motor vehicle noise, we extract the daily seismic attenuation by quantifying the linear relationship between the frequency and amplitude ratio of paired instantaneous spectra. After verifying the reliability of the proposed method, we apply it to seismic ambient noise data collected at three urban sites. The estimated attenuation is compared against three environmental variables: rainfall, temperature, and traffic volume. The results show that the estimated seismic attenuation correlates most strongly with precipitation with positive time lags, suggesting a high attenuation sensitivity to changes in soil moisture and groundwater system. Furthermore, differences in attenuation‐precipitation correlations indicate that near‐surface physical properties may vary significantly due to local site conditions. Our statistical method extracts reliable near‐surface information from highly complex urban ambient noise data, achieving unprecedented spatial and temporal resolution compared to existing ambient noise imaging methods.

Earth’s surface is constantly vibrating due to natural processes inside and human activities on the surface of the Earth. These vibrations form the ambient seismic fields that are measured by sensitive seismometers. Compared with natural processes, anthropogenic vibrations dominate the seismic measurements at higher frequency bands, demonstrate clear temporal and cyclic variability, and are more heterogeneous in space. Consequently, urban ambient seismic fields are a rich information source for human activity monitoring. Improving from the conventional energy-based seismic spectral analysis, we utilize advanced signal processing techniques to extract the occurrence of specific urban activities, including motor vehicle counts and runner activities, from the high-frequency ambient seismic noise. We compare the seismic energy in different frequency bands with the extracted activity intensity at different locations within a one-kilometer radius and highlight the high-resolution information in the seismic data. Our results demonstrate the intense heterogeneity in a highly developed urban space. Different sectors of urban society serve different functions and respond differently when urban life is severely disturbed by the impact of the COVID-19 pandemic in 2020. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution, which potentially could be utilized by government regulators and policymakers for dynamic monitoring and urban management.

Background: The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective: The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods: We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results: We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions: The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

Human foot traffic in urban environments provides essential information for city planners to manage the urban resources and urban residents to plan their activities. Compared to camera or mobile-based solutions, seismic sensors detect human footstep signals with fewer privacy concerns. However, seismic sensors often record signals generated from multiple sources, particularly in an urban outdoor environment. In this article, we monitor people’s running activities during COVID-19 pandemic with a seismic sensor in a park in Singapore. We compare the spectra of natural and urban events in the recorded seismic data. For each 3 s seismic data, we define hierarchical screening criteria to identify footsteps based on the spectrum of the signal and its envelope. We derive the cadence of each runner by detecting the primary frequency of the footstep signals. The resulting algorithm achieves higher accuracy and higher temporal resolution for weak and overlapping signals compared to existing methods. Runner statistics based on four-month long seismic data show that urban running activities have clear daily and weekly cycles. Lockdown measures to mitigate COVID-19 pandemic promoted running activities, particularly over the weekends. Cadence statistics show that morning runners have higher cadence on average.

Ambient noise generated by the anthropological activities in the urban environments may contain both Rayleigh and Love waves. Due to the differences in the physics of Rayleigh and Love waves, a pre-knowledge of the wave modes in the cross-correlogram is essential for an accurate inversion of the subsurface velocity model. Several studies (Martin and Biondi, 2017; Martin et al., 2017; Luo et al., 2020) demonstrated that only Rayleigh waves can be extracted by cross-correlation if the virtual source is colinear with the DAS array based on the assumption that the ambient noise sources are random and uniformly distributed. However, in realistic cases, ambient noise sources may come from a certain direction (e.g., Dou et al., 2017; Zhang et al., 2019). Moreover, the source propagation direction should be resolved and used to correct the apparent dispersion curves. Zhao et al. (2020) and van den Ende et al. (2020) proposed that beamforming results are not always reliable due to the measurements of DAS.Based on the synthetic DAS ambient noise data recorded by a near “L” shape array (Source-West corner of the Stanford DAS-1 array), we prove that beamforming can resolve the source direction when the ambient sources are mainly coming from one direction. Two important processing procedures are that: check the polarity in the data and apply polarity flip on one part of the data; apply amplitude normalization on the data if strong amplitude difference exits in the data. Based on the source direction, the coordinate of the DAS array, and amplitude ratio of the data recorded by the two segments of the DAS array, we propose an inversion method to calculate the amplitude ratio of the Rayleigh and Love waves generated by the ambient sources.We apply the method to two 100-second DAS ambient noise data recorded by the Stanford DAS-1 array. We first resolve the source propagation direction from the two data. The results indicate that the ambient noise in the data were mainly generated by the motor vehicles running on the Campus Drive in the northwest of the array. Then we invert for the Rayleigh and Love waves amplitude ratio using the proposed method. The ratios for the two data are 0.2 and 0.13, respectively. The results suggest that the ambient noise generated by motor vehicles running on the northwest corner of the Campus Drive mainly contain Love waves.

Urban activities cause minute vibrations of the earth surface that can be detected by highly sensitive seismometers. In the island city-state Singapore, human activities have been dramatically changed since April 2020 by government measures to suppress the spread of COVID-19. By analyzing the high- frequency seismic signals, the impact of the pandemic and its corresponding mitigation measures were quantified as traffic flow at the exit of the National University of Singapore (NUS) and at the intersection between Pasir Panjang Port Terminal and West Coast Park, representing activities at nonessential workplaces, essential workplaces, and recreational areas. The anonymity of seismic data enabled an unprecedented spatial and temporal resolution that is pivotal to understand the heterogeneity and evolution of pandemic responses in different sectors of an urban society. The rich information extracted from seismic data provide an opportunity for real time activity monitoring and dynamic policy making in order to ensure a successful pandemic mitigation and a less disturbed urban lifestyle.

Urban subsurface monitoring requires high temporal‐spatial resolution, low maintenance cost, and minimal intrusion to nearby life. Distributed acoustic sensing (DAS), in contrast to conventional station‐based sensing technology, has the potential to provide a passive seismic solution to urban monitoring requirements. Based on data recorded by the Stanford Fiber Optic Seismic Observatory, we demonstrate that near‐surface velocity changes induced by the excavation of a basement construction can be monitored using existing fiber optic infrastructure in a noisy urban environment. To achieve satisfactory results, careful signal processing comprising of noise removal and source signature normalization are applied to raw DAS recordings. Repeated blast signals from quarry sites provide free, unidirectional, and near‐impulsive sources for periodic urban seismic monitoring, which are essential for increasing the temporal resolution of passive seismic methods. Our study suggests that DAS will likely play an important role in urban subsurface monitoring.