Nowcasting Earthquakes:Imaging the Earthquake Cycle in California with Machine Learning

Abstract Data-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly due to large training data sets. In Earth however, earthquake interevent times range from 10's-100's of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission histories and fault-slip from numerical simulations, and generalizes to produce accurate results using laboratory data. Notably slip-predictions markedly improve using the simulation-data trained-model and training the latent space using a portion of a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

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Nowcasting Earthquakes:Imaging the Earthquake Cycle in California with Machine Learning

10.1002/essoar.10506614.1 ◽

2021 ◽

Author(s):

John B. Rundle ◽

Andrea Donnellan ◽

Geoffrey Fox ◽

James P Crutchfield ◽

Robert A Granat

Keyword(s):

Machine Learning ◽

Earthquake Cycle

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Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine Learning: A Comparison of Two Methods

Surveys in Geophysics ◽

10.1007/s10712-021-09655-3 ◽

2021 ◽

Author(s):

John B. Rundle ◽

Andrea Donnellan ◽

Geoffrey Fox ◽

James P. Crutchfield

Keyword(s):

Machine Learning ◽

Earthquake Cycle

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Predicting fault slip via transfer learning

Nature Communications ◽

10.1038/s41467-021-27553-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Kun Wang ◽

Christopher W. Johnson ◽

Kane C. Bennett ◽

Paul A. Johnson

Keyword(s):

Machine Learning ◽

Numerical Simulations ◽

Transfer Learning ◽

Laboratory Experiments ◽

Fault Slip ◽

Geophysical Data ◽

Training Data ◽

Data Sets ◽

Earthquake Cycle ◽

Fault Friction

AbstractData-driven machine-learning for predicting instantaneous and future fault-slip in laboratory experiments has recently progressed markedly, primarily due to large training data sets. In Earth however, earthquake interevent times range from 10’s-100’s of years and geophysical data typically exist for only a portion of an earthquake cycle. Sparse data presents a serious challenge to training machine learning models for predicting fault slip in Earth. Here we describe a transfer learning approach using numerical simulations to train a convolutional encoder-decoder that predicts fault-slip behavior in laboratory experiments. The model learns a mapping between acoustic emission and fault friction histories from numerical simulations, and generalizes to produce accurate predictions of laboratory fault friction. Notably, the predictions improve by further training the model latent space using only a portion of data from a single laboratory earthquake-cycle. The transfer learning results elucidate the potential of using models trained on numerical simulations and fine-tuned with small geophysical data sets for potential applications to faults in Earth.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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