Haipeng Li

Aging bridges pose significant risks to public safety. Although existing sensing technologies offer a potential solution for structural health monitoring, the high cost and complexity of installing and maintaining sensing systems have limited their scalability and long-term deployment. Here, we present a scalable and cost-effective approach that turns pre-existing telecommunication (telecom) fiber-optic cables into a dense array of dynamic strain sensors to continuously monitor bridges. Using telecom fibers, we retrieve guided wavefields across bridge superstructures, which was impossible with existing sensing technologies due to their limited scalability and the prohibitive cost of ultra-dense sensor arrays required to capture meter-scale wavelengths. We also estimate quasi-static displacements with millimeter-level accuracy and extract modal parameters. These results demonstrate that our approach is robust to noise from imperfect fiber-bridge coupling, a limitation inherent to the non-dedicated nature of telecom fibers used as sensors. Furthermore, we demonstrated the effectiveness of this approach through field evaluations across six bridges in the United States and South Korea. Because telecom fibers are widely deployed and co-located with civil infrastructure systems, this study introduces a new sensing paradigm that leverages existing fiber-optic networks to enable large-scale, real-time structural monitoring without the cost and logistical burden of dedicated sensors.

Understanding groundwater dynamics is critical for sustainable water management, particularly as climate extremes become more intense and frequent. However, the resolution of existing subsurface observational tools remains inadequate for detailed aquifer monitoring and imaging. We introduce an innovative technique for groundwater monitoring using time‐lapse full‐waveform inversion, leveraging fiber‐optic cables as dense sensors and vehicular traffic as repetitive seismic sources. Over a two‐year period along Sandhill Road, California, this approach captures detailed spatiotemporal S‐wave velocity variations, revealing a 2.9% reduction corresponding to a 9.0‐m groundwater table rise following atmospheric river storms in Water Year 2023. Notably, our analysis enables high‐resolution daily estimates of aquifer recharge rates. We also observe spatially inhomogeneous subsurface responses, with less reduction beneath impervious paved zones than grassy areas, showcasing the importance of urbanization on the natural recharge of aquifers. Our method has the potential for high‐resolution spatiotemporal monitoring and quantitative characterization of groundwater systems.

Urban monitoring is important for maintaining safe and sustainable urban life through infrastructure assessment, resource optimization, hazard mitigation, and public safety enhancement. Vibration and seismic signals offer rich insights into urban environments, reflecting both physical and environmental conditions and societal activities. Recent advancements in Distributed Acoustic Sensing (DAS) have leveraged pre-existing telecommunication (telecom) fiber-optic networks as dense arrays of seismic and vibration sensors, providing a scalable, cost-effective, and continuous urban monitoring solution. DAS technology uses internal imperfections in fiber-optic cables to measure dynamic strain (rate), enabling fibers to capture seismic waves and structural vibrations at meter-scale intervals over tens of kilometers. By utilizing pre-existing telecom fiber networks as urban-scale sensing platforms, DAS has demonstrated broad applications ranging from seismic monitoring and subsurface imaging to urban activity detection and structural health assessment. As research and technological advancements continue, DAS is positioned to become prominent in next-generation urban sensing, supporting smarter, more resilient, and more sustainable urban planning and development. Building on these developments, this paper reviews the latest progress in urban monitoring using pre-existing telecom fiber networks as DAS systems.

Urban environments require high-resolution, continuous subsurface imaging and monitoring to address challenges in infrastructure resilience, groundwater management, and seismic hazard assessment. However, traditional seismic networks are often limited by sparse spatial coverage, high deployment costs, and logistical constraints in urban areas. Distributed acoustic sensing (DAS) has emerged as a promising sensing technology, repurposing preexisting telecommunication fiber networks into dense, large-scale seismic arrays. This study demonstrates the potential of urban DAS for near-surface characterization and time-lapse monitoring using data from the Stanford DAS-2 experiment. A targeted interferometry workflow extracts high-quality virtual source gathers from vehicle-induced signals, enabling daily time-lapse elastic full-waveform inversion. Ambient noise interferometry extends both the offset range and low-frequency content beyond what targeted interferometry can achieve, while the nonrepeatability of ambient noise suggests the need for careful data selection to ensure robust subsurface monitoring. Additionally, earthquake recordings provide constraints on urban fault structures through the analysis of scattered wavefields. As DAS seismic arrays continue to expand in urban settings, they are poised to play a critical role in high-resolution, continuous near-surface characterization and monitoring.

Continuous seismic monitoring of the near-surface structure is crucial for urban infrastructure safety, aiding in the detection of sinkholes, subsidence, and other seismic hazards. Utilizing existing telecommunication optical fibers as distributed acoustic sensing (DAS) systems offers a cost-effective method for creating dense seismic arrays in urban areas. DAS leverages roadside fiber-optic cables to record vehicle-induced surface waves for near-surface imaging. However, the influence of roadway vehicle characteristics, such as vehicle weight, size, and driving speed, on their induced surface waves and the resulting imaging of near-surface structures is poorly understood. In this study, we investigate surface waves generated by vehicles of varying weights and speeds to provide insights into accurate and efficient near-surface characterization. We first classify vehicles into light, midweight, and heavy based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed (slow, medium, and fast) using their arrival times at DAS channels. To investigate how vehicle characteristics influence the induced surface waves, we extract phase velocity dispersion and invert the subsurface structure for each vehicle class by retrieving virtual shot gathers. Our results reveal that heavy vehicles produce higher signal-to-noise ratio surface waves, and a sevenfold increase in vehicle weight can reduce uncertainties in phase velocity measurements from dispersion spectra up to 3 × , particularly at lower frequencies. Thus, data from heavy vehicles better constrain structures at greater depths. In addition, with driving speeds ranging from 5 to 30 m per second in our study, differences in the dispersion curves due to vehicle speed are less pronounced than those due to vehicle weight. Our results suggest judiciously selecting and processing surface-wave signals from certain vehicle types can improve the quality of near-surface imaging in urban environments.

The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics.

Full waveform inversion (FWI) is able to construct high‐resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open‐source AD‐based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD‐based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state‐of‐the‐art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large‐scale inversion associated with AD, the proposed framework adopts strategies such as mini‐batch and checkpointing. Through tests on synthetic and field data, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and development of new inversion strategies.

Continuous seismic monitoring of the near-surface structure is crucial for urban infrastructure safety, aiding in the detection of sinkholes, subsidence, and other seismic hazards. Utilizing existing telecommunication optical fibers as Distributed Acoustic Sensing (DAS) systems offers a cost-effective method for creating dense seismic arrays in urban areas. DAS leverages roadside fiber-optic cables to record vehicle-induced surface waves for near-surface imaging. However, the influence of roadway vehicle characteristics on their induced surface waves and the resulting imaging of near-surface structures is poorly understood. We investigate surface waves generated by vehicles of varying weights and speeds to provide insights into accurate and efficient near-surface characterization. We first classify vehicles into light, mid-weight, and heavy based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed using their arrival times at DAS channels. To investigate how vehicle characteristics influence the induced surface waves, we extract phase velocity dispersion and invert the subsurface structure for each vehicle class by retrieving virtual shot gathers (VSGs). Our results reveal that heavy vehicles produce higher signal-to-noise ratio surface waves, and a sevenfold increase in vehicle weight can reduce uncertainties in phase velocity measurements from dispersion spectra by up to 3X. Thus, data from heavy vehicles better constrain structures at greater depths. Additionally, with driving speeds ranging from 5 to 30 meters per second in our study, differences in the dispersion curves due to vehicle speed are less pronounced than those due to vehicle weight. Our results suggest judiciously selecting and processing surface wave signals from certain vehicle types can improve the quality of near-surface imaging in urban environments.

The Tanlu Fault Zone (TFZ) is considered as a suture of the intra‐continental collision between the South and North China plates, and it has attracted considerable geological, geochemistry, and geophysical investigations. However, its deep structures still lack clear delineation and the related tectonic processes are still debated without a clear consensus. To better characterize the TFZ as a plate boundary in east China, a 2‐D land seismic survey extending 87 km was conducted across its southern segment to investigate the underlying complex structures. Acoustic full‐waveform inversion (FWI) is applied to the early arrivals of the land seismic data to reconstruct the high‐resolution P‐wave velocity model of the upper crust (above ∼3.5 km). Structures of the crust and the uppermost mantle are further constrained through deep seismic profiling (DSP). The integrated geological interpretation from the FWI and DSP results suggests massive magma upwelling activities, characterized by the reflection‐free zone revealed by DSP and the high‐velocity anomaly recovered by FWI. The TFZ in the study area is found to be a deeply seated fault system with a positive flower structure at its shallow part, which suggests it has undergone a transpression regime. Also, the DSP result provides evidence for the subduction of the South China plate under the North China plate. Finally, a three‐stage model including the compression, transpression, and extension stages is proposed based on the new seismic results to better constrain the tectonic evolution of the southern segment of the TFZ.