Detection of Early seismic signal using ARtificiaL Intelligence

ongoing

Earthquakes caused nearly one million fatalities in the last two decades. The hazardous nature of earthquakes is largely due to their unpredictability. The question of whether this unpredictability is ontological (i.e. earthquakes are a chaotic phenomenon that physics cannot predict) or a consequence of our incapacity to model them is still open. In the first case, one may never hope to predict earthquakes and efforts should be focused towards developing early-warning approaches so that the population can prepare for imminent shaking and tsunami. In the second case, earthquake prediction becomes theoretically achievable. In both cases, Artificial Intelligence (AI) may lead to giant steps in anticipating destructive events. I propose here to use AI to identify weak early seismic signals to both speed up early-warning and explore the possibility of earthquake prediction. The first part of the project will be devoted to implementing an early-warning approach not based on P-waves as all current systems but on an earlier signal recently identified. This signal is due to the perturbation of the gravity field generated by an earthquake – which propagates at the speed of light – but is ~6 orders of magnitude smaller than seismic waves, strongly limiting its detection with standard techniques. AI has proven very efficient at detecting low-amplitude signals. I will implement an AI algorithm to systematically detect gravity perturbations generated by magnitude > 7 earthquakes and rapidly estimate from them the location and magnitude of the earthquake. Though the existence of earthquake precursors (i.e. signals preceding the origin of earthquakes themselves) is hypothetical, AI represents a new prowerful mean to discover them. In the second part of the project, I will adapt the AI algorithm developed in the first part to search for earthquake precursors.