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See the following announcements:
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- Call For Abstracts - AGU Session S013: Fault Zone Complexity and Earthquake Dynamics: From Nucleation to Rupture
- AGU session S024: Next-Generation Seismology - Leveraging Edge Computing and AI for Real-Time Observation
On behalf of Evan Marschall, UCSD
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Call For Abstracts - AGU Session S013: Fault Zone Complexity and Earthquake Dynamics: From Nucleation to Rupture
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Session Description: Tectonic fault zones are inherently complex systems, rarely conforming to simplistic or homogenous structural models. They are instead characterized by complex fault geometries, spatially and temporally variable frictional properties, and heterogeneous material properties. This session aims to showcase recent interdisciplinary advances that quantify how fault zone complexity influences earthquake nucleation, rupture propagation, termination, and post-seismic deformation. We welcome contributions that utilize a broad range of approaches, including geodetic measurements of fault creep and slow-slip, in-situ field experiments, and seismic field observations. Submissions employing laboratory experiments, theoretical analyses, and numerical modeling are encouraged, particularly those that focus on understanding how frictional properties, fault geometry, material properties, and fault roughness control earthquake rupture processes. We also encourage work that bridges spatial and temporal scales or integrates multiple datasets to provide a more holistic view of fault system behavior.
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Looking forward to seeing you all at AGU.
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David Chas BoltonPatricia Martínez-Garzón
Camilla Cattania
Evan Marschall
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On behalf of Ettore Biondi, Stanford University
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AGU session S024: Next-Generation Seismology - Leveraging Edge Computing and AI for Real-Time Observation
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Please consider submitting an abstract to our seismology session on leveraging edge computing and AI/ML for real-time seismic observations. More details can be found below. S024: Next-Generation Seismology - Leveraging Edge Computing and AI for Real-Time Observation
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Session Description: 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. Abstract deadline: August 5th, 2026, 23:59 ET/03:59 UTC
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Conveners: Maeva Pourpoint (AFRL), Jonathan B Ajo Franklin (Rice University), Ettore Biondi (Stanford University)
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