This research proposes a new MEMS seismometer for detecting geological anomalies in urban areas, indicating its potential for risk assessments in construction.
Purpose The purpose of this paper is to design, build and validate a cost-effective triaxial MEMS seismometer (TerraSense), aiming to provide a practical monitoring tool for urban subsurface risk assessment during construction and operation. Design/methodology/approach TerraSense consists of both hardware and client software components. The hardware includes sensors, acquisition circuits and a microcontroller, peripheral and communication circuits, as well as a power supply system. The client software provides instrument parameter configuration, real-time data display and functions such as time or frequency-domain analysis and filtering. In addition, to evaluate TerraSense’s performance, laboratory shaker tables were used to test the instrument’s time-domain linearity and frequency-domain response characteristics. Furthermore, the horizontal-to-vertical spectral ratio method was applied in urban shallow geological environments for field measurements to verify its capability in detecting shallow geological anomalies. Findings TerraSense demonstrates stable and reliable spectral response characteristics in both the time and frequency domains. Power spectral analysis confirms that the instrument records horizontal and vertical seismic components with sufficient accuracy to support the identification of shallow geological anomalies using the horizontal-to-vertical spectral ratio method. Results from both shaker table tests and field measurements indicate that TerraSense is capable of effectively resolving typical shallow subsurface anomalies in urban environments, supporting its applicability for preliminary underground risk assessment in engineering and construction contexts. Originality/value This study proposes a cost-effective three-axis seismometer based on microelectromechanical systems, providing a reliable solution for shallow geological detection in urban environments.
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Song et al. (2026) studied this question.
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