deep learning of aftershock patterns following large earthquakes

Connection to other SSA members via the online membership roster. 09/08/2018 ∙ by Zachary E. Ross, et al. September 6, 2018. Sort by citations Sort by year Sort by title. Issues with Deep Learning of Aftershocks by DeVries. Although encouraging results have been obtained recently, deep neural networks (DNN) may sometimes create … Deep learning of´ aftershock patterns following large earthquakes. 1 Department of Geophysics, Stanford University, Stanford, CA 94305 USA. that was published in Nature, according to Shah, shows a basic problem of Data Leakage and this problem could invalidate all the experiments. On the other hand, 3,288 events (28.9%) were triggered by an increase in shear stress, whereas 635 events (5.6%) were triggered by a decrease in fault strength. Infrastructure is composed of public and private physical improvements such as roads, railways, bridges, tunnels, water supply, sewers, electrical grids, and telecommunications.¹â° Building design and construction play a large role in ensuring that buildings can withstand earthquakes. The US-based researchers ran … Dieterich J (1994), A constitutive law for rate of earthquake production and its application to earthquake clustering. Sort. Last updated: Feburary 28, 2019. DOI: 10.1038/s41586-018-0438-y Journal information: Nature The findings are reported in … Impact of earthquakes on infrastructure. Nevertheless, while exact prediction is not (currently) possible, advancements have been made. A Deeper Look into ‘Deep Learning of Aftershock Patterns Following Large Earthquakes’: Illustrating First Principles in Neural Network Physical Interpretability book, May 2019. Deep Learning of Aftershock Patterns Following Large Earthquakes. Deep learning of aftershock patterns following large earthquakes. Abstract. October 2019. “Deep learning of aftershock patterns following large earthquakes.” Nature 560.7720, (2018): 632. Deep learning of aftershock patterns following large earthquakes Phoebe M. R. DeVries, Fernanda Viégas, Martin Wattenberg & Brendan J. Meade - Nature Other Deep learning of aftershock patterns following large earthquakes. Using deep learning algorithms, the pair analyzed a database of earthquakes from around the world to try to predict where aftershocks might occur, and developed a system that, while still imprecise, was able to forecast aftershocks significantly better than random assignment. Published online 29 August 2018 . Deep learning of aftershock patterns following large earthquakes. Deep Learning of Aftershock Patterns Following Large Earthquakes In this episode, we discuss their recent paper, ‘Deep learning of aftershock patterns following large earthquakes’, and the preliminary steps that guided them to … He earned his PhD in Geophysics at the Institut de Physique du Globe de Paris in France in 2006. DOI: 10.1038/s41586-018-0438-y Journal information: Nature About two months later, a second large earthquake (Ms = 6.7) occurred in the adjacent region. Machine learning algorithms Supervised Learning Unsupervised Learning Discrete Classification Clustering Continuous Regression Dimensionality Both studies shed light on more than a decade of debate on the origin and prevalence of remotely triggered earthquakes. Geophys., 99, 2601-2618 Verified email at fas.harvard.edu - Homepage. Computers & Geosciences , 115:96–104, 2018. In 41st International Symposium on Microarchitecture (MICRO). These aftershocks are considered mainshocks if they are larger than the previous mainshock. In other words, neural networks could be used to develop new methods for assessing aftershock risks during the subsequent― and most high-risk― days and weeks, with a view to preventing them or limiting their effects and potentially saving lives. Reinforcement learning (RL) has made tremendous achievements, e.g., AlphaGo. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle.Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake … Deep learning of aftershock patterns following large earthquakes, Nature (2018). Google Scholar; Phoebe MR DeVries, Fernanda Viégas, Martin Wattenberg, and Brendan J Meade. Dear Editors: A recent paper you published by DeVries, et al., Deep learning of aftershock patterns following large Earthquakes, contains significant methodological errors that undermine its conclusion.These errors should be highlighted, as data science is still an emerging field that hasn’t yet matured to the rigor of other fields. More information: Phoebe M. R. DeVries et al. The maximum magnitude of aftershocks and their temporal decay are well … The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath's law 1 and Omori's law 2 ), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Deep learning of aftershock patterns following large earthquakes. 2018å¹´6月18日に発生した大阪府北部地震では震度6弱の揺れが観測されましたが、本震以降に観測された震度1以上の余震はなんと56回で、最大余震は最大震度4のものでした。 Dr. Arnaud Mignan is a Senior Researcher at ETH Zurich where he is involved with the Institute of Geophysics, Swiss Seismological Service and Swiss Competence Center for Energy Research (SCCER). [15] Aftershocks. PhaseLink: A Deep Learning Approach to Seismic Phase Association. ∙ 0 ∙ share . Applications of deep learning to seismology are also proceeding rapidly, including the detection of P- and S-wave arrival times (Zhu and Beroza 2018), determination of P-wave arrival times and first-motion polarities (Ross et al. 3 Department of Geophysics, University of Science and Technology of China, Hefei, … Google Scholar. (2018) trained a deep neural network on hundreds of observed aftershock patterns and found that the ML algorithm performed better than a standard – but outdated – physical model. Authors: Brendan J Meade. research-article . DOI: 10.1038/s41586-018-0438-y Journal information: Nature Using Machine Learning and Surface Deformation Data to Predict Earthquakes. Kong, Q., Trugman, D. T., Ross, Z. E., ... A Comparison of Geodetic and Geologic Rates Prior to Large Strike-Slip Earthquakes: A Diversity of Earthquake-Cycle Behaviors? Improved aftershock location forecasts compared to previous methods aftershock location forecasts compared to previous methods in an August paper... Distance, are also precise and interpretable predictors of after - shock locations, serving as a potential tool... Improve predictions of where aftershocks will strike following a big earthquake thus forecast, of when earthquakes occur! Identification is very important using deep learning of aftershock patterns following large earthquakes.” Nature 560.7720, ( 2018.. Asencio-Cort es, Jos´ e Luis Aznarte, Cristina Rubio-Escudero, and Brendan Meade! 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