151 citations · 187 across the 5 of their papers we have counts for
5 papers
Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning
Fantine Huot, Robert G. Clapp, Biondo L. Biondi
With fiber-optic seismic acquisition development, continuous dense seismic monitoring is becoming increasingly more accessible. Repurposing fiber cables in telecommunication condui…
Detection and characterization of microseismic events from fiber-optic DAS data using deep learning
Fantine Huot, Ariel Lellouch, Paige Given +5
Microseismic analysis is a valuable tool for fracture characterization in the earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, t…
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data
Fantine Huot, R. Lily Hu, Nita Goyal +3
Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…
High-resolution imaging on TPUs
Fantine Huot, Yi-Fan Chen, Robert Clapp +2
The rapid evolution of artificial intelligence (AI) is leading to a new generation of hardware accelerators optimized for deep learning. Some of the designs of these accelerators a…