The proliferation of large-N seismic acquisition has yielded an explosion in data volumes, challenging traditional telemetry and centralized processing approaches. This session explores edge computing as a transformative paradigm for seismology. By deploying advanced, lightweight AI/ML (TinyML) models directly at the sensor level, edge computing enables real-time signal detection, characterization, and early warning without the severe latency and bandwidth bottlenecks of large data transmission. Timeliness is driven by recent breakthroughs in TinyML, ultra-low-power microprocessors, and inexpensive GPUs which now allow complex signal processing algorithms and neural networks to run autonomously in remote, off-grid environments. We welcome abstracts highlighting innovations in intelligent autonomous research, edge-enabled sensor networks, and the integration of decentralized computing with novel sensing technologies. Topics include: deploying TinyML for on-node seismic processing; applying edge computing to manage high-density fiber-sensing data (DAS, DTS, DSS, SOP...); and case studies demonstrating reduced latency in Earthquake Early Warning systems via edge telemetry.

• NS - Near Surface Geophysics