![]() ![]() Sornette, D.: Critical Phenomena in Natural Sciences, Chaos, Fractals, Selforganization and Disorder: Concepts and Tools. Mignan, A.: Retrospective on the Accelerating Seismic Release (ASR) hypothesis: controversy and new horizons. Vere-Jones, D., Ben-Zion, Y., Zuniga, R.: Statistical seismology. 38, 15032–15039 (2011)ĭeVries, P.M.R., Viégas, F., Wattenberg, M., Meade, B.J.: Deep learning of aftershock patterns following large earthquakes. ![]() Moustra, M., Avraamides, M., Christodoulou, C.: Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Geller, R.J., Jackson, D.D., Kagan, Y.Y., Mulargia, F.: Earthquakes cannot be predicted. Panakkat, A., Adeli, H.: Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Kong, Q., Trugman, D.T., Ross, Z.E., Bianco, M.J., Meade, B.J., Gerstoft, P.: Machine learning in seismology: turning data into insights. Pathak, J., Hunt, B., Girvan, M., Lu, Z., Ott, E.: Model-free prediction of large spatiotemporally chaotic systems from data: a reservoir computing approach. Han, J., Jentzen, A., Weinan, E.: Solving high-dimensional partial differential equations using deep learning. Science 349(6245), 255–260 (2015)Ĭarleo, G., Troyer, M.: Solving the quantum many-body problem with artificial neural networks. ![]() Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Complexification is a controversial trend in all of Science and first principles should be applied wherever possible to gain physical interpretations of neural networks. Not only seems deep learning to be “excessive” in the present case, the simpler ANN streamlines the process of aftershock forecasting, limits model bias, and provides better insights into aftershock physics and possible model improvement. AUC = 0.85 is again reached with an ANN, now with only two geometric and kinematic features. ![]() Following first principle guidance, we then bypass the elastic stress change tensor computation, making profit of the tensorial nature of neural networks. We first show that a simple artificial neural network (ANN) of 1 hidden layer yields a similar performance, suggesting that aftershock patterns are not necessarily highly abstract objects. The performance of their DNN was assessed using ROC with AUC = 0.85 obtained. who defined a DNN of 6 hidden layers with 50 nodes each, and with an input layer of 12 stress features, to predict aftershock patterns in space. We investigate the results of De Vries et al. Although encouraging results have been obtained recently, deep neural networks (DNN) may sometimes create the illusion that patterns hidden in data are complex when this is not necessarily the case. Earthquake prediction - a recognized moonshot challenge - is obviously worthwhile exploring with deep learning. In the last years, deep learning has solved seemingly intractable problems, boosting the hope to find (approximate) solutions to problems that now are considered unsolvable. ![]()
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