Earthquake Detection with TinyML

Earthquake detection is the critical first step in Earthquake Early Warning (EEW) systems. For robust EEW systems, detection accuracy, detection latency and sensor density are critical to providing real-time earthquake alerts. Traditional EEW systems use fixed sensor networks or, more recently, networks of mobile phones equipped with micro-electromechanical systems (MEMS) accelerometers. Internet of things (IoT) edge devices, with built-in machine learning (ML) capable microcontrollers, and always-on, always internet-connected, stationary MEMS accelerometers provide the opportunity to deploy ML-based earthquake detection and warning using a single-station approach at a global scale. Here, we test and evaluate deep learning ML algorithms for earthquake detection on an Arduino Cortex M4 microcontroller with just 256 kB of RAM. We show the trade-offs between detection accuracy and latency on resource-constrained microcontrollers.