Abstract
Abstract
Seismic monitoring is entering a high data volume, multimodal sensing regime driven by dense sensor deployments, including Micro-Electro-Mechanical Systems (MEMS) arrays, large-N (100s-1000s) Nodal arrays, Distributed Acoustic Sensing (DAS), and other geophysical instruments. Machine learning models, with appropriate tuning and calibration, are capable of achieving close to expert-level performance in seismic detection and phase picking. The emerging operational challenge is how to scale that machine intelligence across future multimodal sensing networks. Effective incorporation of these capabilities remains constrained by telemetry bandwidth, centralized computation architectures, and communication latency. Here, we present XiaoNet, a proof-of-concept edge machine learning architecture for seismicity monitoring based on a simple system principle: perform first-pass inference near the sensor and transmit concise metadata rather than continuous streams. Using knowledge distillation, we compress a state-of-the-art PhaseNet model by over 93% into XiaoNet, a 240 KB model designed for edge deployment, while preserving strong phase-picking performance across datasets. Deployment on a Raspberry Pi 5 shows sub-millisecond inference latency for 30-second 3-channel waveform windows. By shifting inference to the edge and transmitting concise event metadata payloads, the framework demonstrated the potential for edge inference to reduce communication latency. Substituting an event metadata message payload for continuous raw waveform stream largely mitigates the dominant source of variable telemetry delay under moderate to heavy load. This result supports a feasible architecture in which edge inference devices complements centralized observatories, potentially improving resilience and response speed for seismic monitoring and may extend to future multimodal subsurface and infrastructure sensing systems.