Adapting PLUM: Earthquake Early Warning with Node-Level Processing in New Zealand

Authors

  • Chanthujan Chandrakumar Joint Center for Disaster Research, Massey University Author
  • Marion Lara Tan Joint Centre for Disaster Research Massey University Author
  • Caroline Holden SeismoCity Ltd., Wellington Author
  • Max T. Stephens Civil and Environmental Engineering University of Auckland Author
  • Raj Prasanna Joint Centre for Disaster Research Massey University Author

Keywords:

Earthquake Early Warning (EEW), Node-Level Processing, PLUM Algorithm, Citizen Seismology, Low-cost sensors

Abstract

Is running the Propagation of Local Undamped Motion (PLUM) algorithm in a community-engaged earthquake early warning (EEW) network feasible, and can it function effectively at the node level without centralised processing units? This study investigates the practicality of deploying the PLUM algorithm within a node-level architecture, shifting away from traditional centralised seismic data processing methods. The study uses cost-effective MEMS-based seismographs to decentralise EEW. The preliminary phase of the research included the deployment of sensors and the establishment of a two-tiered Primary-Secondary node structure for node-level intensity prediction and alert generation, with the sensors functioning as independent prediction points. Future work includes threshold calibration for optimal alert issuance, and network expansion to reduce blind spots. This work-in-progress paper discusses progress towards a scalable, efficient EEW system that could serve as a replicable model for earthquake-prone regions globally, aiming for operational readiness that empowers communities against the threat of earthquakes.

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Published

2024-05-15

How to Cite

Chandrakumar, C., Tan, M. L., Holden, C., Stephens, M. T. ., & Prasanna, R. (2024). Adapting PLUM: Earthquake Early Warning with Node-Level Processing in New Zealand. ISCRAM Proceedings, 21. https://ojs.iscram.org/index.php/Proceedings/article/view/53

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