Smart detection of subsurface anomalies: concept, validation and applications

Abstract

Naturally formed and engineering-induced subsurface anomalies (e.g., cavities and sinkholes) jeopardize infrastructure safety and hinder urban sustainability. Here we report a smart sensing method to detect subsurface anomalies based on the physical characteristics extracted from the effective signals scattered and reflected directly from these anomalies. Potential anomalies at submeter scales can be interpreted based on a sharp variation of anomaly score relative to the background anomaly score, showing the advantage of overcoming subjective uncertainty and biases involved in the traditional geophysical methods. We find that the use of scattered and reflected waves in an intermediate frequency range is well-suited for sensing deep subsurface infrastructure at high resolutions required for civil structures. We also demonstrate that a fast and reliable detection of subsurface anomalies relies solely on the physical characteristics of seismic data in two field cases, promoting geologic hazard forecast and decision-making effectiveness.