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Found 4 papers, 1 papers with code
Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable.
One neuron is more informative than a deep neural network for aftershock pattern forecasting
We reformulate the 2017 results in probabilistic terms using logistic regression (i. e., one neural network node) and obtain AUC = 0. 85 using 2 free parameters versus the 13, 451 parameters used by DeVries et al. (2018).
Geophysics
Generalized Earthquake Frequency-Magnitude Distribution Described by Asymmetric Laplace Mixture Modelling
An asymmetric Laplace mixture model (GFMD- ALMM) is thus proposed with its parameters (detection parameter kappa, Gutenberg-Richter beta-value, mc distribution, as well as number K and weight w of eFMD components) estimated using a semi-supervised hard expectation maximization approach including BIC penalties for model complexity.
Text Classification of the Precursory Accelerating Seismicity Corpus: Inference on some Theoretical Trends in Earthquake Predictability Research from 1988 to 2018
We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification.
