A groundbreaking study recently published in the prestigious journal Engineering introduces an innovative method for investigating urban geohazards, with a particular focus on the detection and characterization of concealed karst features. Karst-related hazards in urban settings pose significant threats, including surface subsidence, sudden ground collapses, and groundwater contamination. These phenomena challenge civil engineering and urban planning due to their unpredictable nature and potential for causing catastrophic damage. Traditional geophysical techniques, while valuable, are often hindered by urban constraints such as limited land accessibility, high ambient noise levels, and complex subsurface conditions.
The research team, led by Jianghai Xia of Zhejiang University, sought to overcome these challenges by developing a synchronous-asynchronous ambient noise observation system. This system enables dense spatial sampling of seismic ambient noise with a comparatively small number of recording stations, thus lowering costs and logistical hurdles. It represents a substantial advancement in urban geohazard monitoring, where conventional dense-array deployments are frequently impractical.
Field experiments were carried out in an abandoned parking lot in the bustling city of Hangzhou, China, an area threatened by hidden karst cavities beneath its surface that imperil impending construction projects. The deployment included 197 nodal seismic receivers, arranged with an average station spacing of just seven meters. The researchers conducted two asynchronous observation intervals, each lasting around 24 hours, complemented by a network of synchronous backbone stations to ensure temporal coherence of the data.
Through meticulous analysis of the recorded ambient seismic noise, the researchers confirmed the extreme complexity of urban noise fields. These noise sources exhibited pronounced diurnal variations influenced by human activities and non-uniform spatial distributions. To tackle this complexity, the team adopted advanced processing strategies. They enhanced noise source signals within stationary-phase zones to derive the first cross-correlogram functions, termed C¹ functions. A novel weighting scheme based on the similarity between multicomponent C¹ functions associated with Rayleigh waves was utilized, effectively reducing biases caused by uneven noise source distributions.
To extract empirical Green’s functions between asynchronously operating stations, the study introduced the calculation of second-order cross-correlation functions, or C² functions. However, these functions initially contained artifacts stemming from higher-mode surface waves, which obscure interpretations. To counter this, the team implemented a filtering procedure exploiting the distinct particle motions of fundamental and higher-mode Rayleigh waves. This separation allowed them to isolate and remove contamination from higher modes, resulting in cleaner empirical Green’s functions essential for accurate tomographic imaging.
Leveraging the derived C¹ and C² functions, the study proceeded to measure Rayleigh wave dispersion curves with high precision. Using these dispersion measurements, a surface wave tomography inversion was performed to reconstruct a detailed three-dimensional shear-wave velocity (S-wave) model of the subsurface. This sophisticated imaging process revealed two prominent low-velocity anomalies at depths between 40 and 60 meters, correlating closely with karst cave systems identified independently by drilling data.
A pivotal feature of this research is the integration of asynchronous observations which greatly enhanced the density of surface wave ray paths. This improvement expanded spatial coverage beyond what synchronous arrays alone could achieve, enabling more uniform imaging of the subsurface structures. Consequently, the novel observation system dramatically reduces the number of stations traditionally required for dense arrays, providing a feasible and cost-effective alternative for urban geohazard investigations.
The implications of this study extend far beyond the immediate case in Hangzhou. The synchronous-asynchronous ambient noise tomography method pioneered here offers a scalable and high-resolution approach for seismic imaging in the noisy and constrained environments typical of urban areas worldwide. Its ability to resolve subsurface features at fine scales will bolster efforts in urban geological hazard prevention, management, and infrastructure safety evaluations.
Moreover, this approach opens new avenues for monitoring dynamic changes in urban subsurface conditions, potentially enabling early warning systems for ground collapse or subsidence events triggered by natural or anthropogenic factors. The versatility and practicality of the method suggest it could be adapted for various urban geotechnical challenges, instigating a paradigm shift in how cities assess and mitigate hidden geological threats.
The research, titled “Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation,” was authored by Ya Liu, Jianghai Xia, Bo Guan, Chaoqiang Xi, Ling Ning, and Hao Zhang. Their collaborative contribution exemplifies the synergy of advanced seismic methodologies with urban engineering applications, pushing the boundaries of what can be achieved in complex, high-noise environments.
As urbanization accelerates globally, and with critical infrastructure expanding over potentially unstable geological formations, the need for innovative, efficient, and accurate subsurface imaging techniques has never been more pressing. This study’s synchronization of asynchronous data acquisition marks a technical milestone, promising safer urban development and smarter management of geohazards.
Ultimately, this novel ambient noise tomography approach not only exemplifies technical ingenuity but also embodies a critical tool for future urban resilience. By illuminating the unseen dangers lurking beneath our cities, this research stands to transform urban planning and geotechnical engineering, enhancing our ability to safeguard human lives and investments in the face of geological uncertainty.
Subject of Research: Urban geohazard investigation using ambient noise tomography focusing on karst features
Article Title: Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
News Publication Date: 11-Feb-2025
Web References:
References:
Liu, Y., Xia, J., Guan, B., Xi, C., Ning, L., & Zhang, H. (2025). Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation. Engineering. https://doi.org/10.1016/j.eng.2025.02.001
Keywords: Urban planning, Chaotic systems, Statistical distributions, Construction techniques, Basic research
