Bertrand Rouet-Leduc, Romain Jolivet, Manon Dalaison, Paul A. Johnson, Claudia Hulbert 

Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning

Nature Communications 12 (2021)

In this paper we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California. 

Bertrand Rouet‐Leduc, Claudia Hulbert, Ian W. McBrearty, Paul A. Johnson

Probing Slow Earthquakes With Deep Learning

Geophysical Research Letters 47 (2020)

By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi‐continuous tremor, providing a proxy that quantifies the slow slip rate. 

Claudia Hulbert, Bertrand Rouet-Leduc, Romain Jolivet, Paul A. Johnson 

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

Nature Communications 11 (2020)

In this paper we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months. 

Bertrand Rouet-Leduc, Claudia Hulbert, Paul A. Johnson 

Continuous chatter of the Cascadia subduction zone revealed by machine learning

Nature Geoscience 12 (2018)

In this paper we show that the Cascadia subduction zone is apparently continuously broadcasting a low-amplitude, tremor-like signal that precisely informs of the fault displacement rate throughout the slow slip cycle. 

Bertrand Rouet‐Leduc, Claudia Hulbert, Nicholas Lubbers, Kipton Barros, Colin J. Humphreys, Paul A. Johnson

Machine Learning Predicts Laboratory Earthquakes

Geophysical Research Letters 44 (2017)

In this paper, we applied machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal and do not make use of its history. 

Bertrand Rouet‐Leduc, Claudia Hulbert, David C. Bolton, Christopher X. Ren, Jacques Riviere, Chris Marone, Robert A. Guyer, Paul Johnson

Estimating Fault Friction From Seismic Signals in the Laboratory

Geophysical Research Letters 45 (2018)

We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. 

Claudia Hulbert, Bertrand Rouet-Leduc, Paul A Johnson, Christopher X Ren, Jacques Riviere, David C Bolton, Chris Marone

Similarity of fast and slow earthquakes illuminated by machine learning 

Nature Geoscience 12 (2018)

In this paper, we report on laboratory earthquakes and show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Using machine learning, we find that acoustic emissions generated during shear of quartz fault gouge under normal stress of 1–10 MPa predict the timing and duration of laboratory earthquakes. 

Christopher X Ren, Omid Dorostkar, Bertrand Rouet‐Leduc, Claudia Hulbert, Dominik Strebel, Robert A Guyer, Paul Allan Johnson, Jan Carmeliet 

Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Geophysical Research Letters 46 (2019)

In this paper we use machine learning to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick‐slip dynamics. 

Bertrand Rouet-Leduc, Kipton Barros, Turab Lookman, Colin J. Humphreys

Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

Scientific Reports 6 (2016)

Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty.