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Jérome Faillettaz

Publications and source records attributed to Jérome Faillettaz.

2 recordsLinked to original sources

A Unified Conceptual Framework for Gravitational Instabilities: From Latent System State to Failure

Gravitational instabilities are among the most widespread natural hazards and are expected to become increasingly significant under ongoing environmental change. Despite substantial advances in process understanding, monitoring, and modeling, predicting the timing of failure remains a fundamental challenge because instability emerges from complex interactions operating across multiple spatial and temporal scales. Existing approaches often focus on specific processes, forcing mechanisms, or observational signatures, resulting in a fragmented view of how systems evolve toward failure. This article propose a unified conceptual framework in which gravitational instabilities are interpreted as the progressive evolution of a latent system state controlled by damage accumulation, stress redistribution and external forcing to catastrophic failure. Within this perspective, failure emerges from the continuous interplay between internal system dynamics and external forcing, while predictability depends on our ability to infer the evolving state of the system from incomplete and indirect observations. The framework provides a common structure linking physical processes, observable manifestations, monitoring strategies, modeling approaches, and forecasting methods across a wide range of gravitational hazards. By integrating concepts from geomechanics, fracture mechanics, statistical physics, and data-driven sciences, the proposed framework shifts the focus from the search for universal precursors toward the reconstruction of evolving system states and their proximity to instability. It offers a unifying perspective for understanding failure processes and developing more robust approaches to hazard assessment and early warning.

physics.geo-ph↗

Event-triggered Natural Hazard Monitoring with Convolutional Neural Networks on the Edge

In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.

cs.LG↗