Inverse Problems in Geodynamics Using Machine Learning Algorithms

[1]  Y. Ohishi,et al.  Post-Perovskite Phase Transition in MgSiO3 , 2004, Science.

[2]  Rodolfo Ostos,et al.  Machine Learning Approach to Extract Diagnostic and Prognostic Thresholds: Application in Prognosis of Cardiovascular Mortality , 2012, Comput. Math. Methods Medicine.

[3]  Chris Hill,et al.  Oceanic eddy detection and lifetime forecast using machine learning methods , 2016 .

[4]  David G. Sibeck,et al.  A new three‐dimensional magnetopause model with a support vector regression machine and a large database of multiple spacecraft observations , 2013 .

[5]  D. McKenzie,et al.  Convection in a compressible fluid with infinite Prandtl number , 1980, Journal of Fluid Mechanics.

[6]  Daniel Paradis,et al.  Predicting hydrofacies and hydraulic conductivity from direct‐push data using a data‐driven relevance vector machine approach: Motivations, algorithms, and application , 2015 .

[7]  C J Rawlings,et al.  Artificial intelligence in molecular biology: a review and assessment. , 1994, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[8]  C. Humphreys,et al.  Machine Learning Predicts Laboratory Earthquakes , 2017, Geophysical Research Letters.

[9]  Satoshi Kaneshima,et al.  Seismic scatterers in the mid-lower mantle , 2016 .

[10]  David A. Yuen,et al.  Spawning superplumes from the midmantle: The impact of spin transitions in the mantle , 2016 .

[11]  George Helffrich,et al.  Small scale heterogeneity in the mid-lower mantle beneath the circum-Pacific area , 2010 .

[12]  Robert D. van der Hilst,et al.  Searching for seismic scattering off mantle interfaces between 800 km and 2000 km depth , 2003 .

[13]  Renata M. Wentzcovitch,et al.  Two-stage dissociation in MgSiO3 post-perovskite , 2011 .

[14]  Stefano de Gironcoli,et al.  Anomalous thermodynamic properties in ferropericlase throughout its spin crossover transition , 2009 .

[15]  Paul J. Tackley,et al.  Three-dimensional models of mantle convection : influence of phase transitions and temperature-dependent viscosity , 1994 .

[16]  Gregory C. Beroza,et al.  Slow Earthquakes and Nonvolcanic Tremor , 2011 .

[17]  Vivien Mallet,et al.  Ozone ensemble forecast with machine learning algorithms , 2009 .

[18]  Sang-Heon Shim,et al.  Spin state of ferric iron in MgSiO3 perovskite and its effect on elastic properties , 2010 .

[19]  Doheon Lee,et al.  Evaluation of the performance of clustering algorithms in kernel-induced feature space , 2005, Pattern Recognit..

[20]  Minho Lee,et al.  Deep learning of support vector machines with class probability output networks , 2015, Neural Networks.

[21]  Moshe Leshno,et al.  The Effect of Training Data Set Size and the Complexity of the Separation Function on Neural Network Classification Capability: The Two-Group Case , 1997 .

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  W. R. Peltier,et al.  The inverse problem for mantle viscosity , 1998 .

[24]  G. Fotopoulos,et al.  Geological Mapping Using Machine Learning Algorithms , 2016 .

[25]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[26]  Robert Koprowski,et al.  Machine learning, medical diagnosis, and biomedical engineering research - commentary , 2014, BioMedical Engineering OnLine.

[27]  J. Mitrovica,et al.  A new inference of mantle viscosity based upon joint inversion of convection and glacial isostatic adjustment data , 2004 .

[28]  Tzay-Chyn Shin,et al.  Quick and reliable determination of magnitude for seismic early warning , 1998, Bulletin of the Seismological Society of America.

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  Brendan J. Meade,et al.  Enabling large‐scale viscoelastic calculations via neural network acceleration , 2017, 1701.08884.

[31]  Sang-Heon Shim,et al.  Effects of the Fe3 + spin transition on the properties of aluminous perovskite—New insights for lower-mantle seismic heterogeneities , 2011 .

[32]  Mathukumalli Vidyasagar,et al.  Machine learning methods in the computational biology of cancer , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[33]  Guillaume Fiquet,et al.  Iron Partitioning in Earth's Mantle: Toward a Deep Lower Mantle Discontinuity , 2003, Science.

[34]  Renata M. Wentzcovitch,et al.  Spin crossover in ferropericlase and velocity heterogeneities in the lower mantle , 2014, Proceedings of the National Academy of Sciences.

[35]  McSween Hy,et al.  Evidence for Life in a Martian Meteorite , 1997 .

[36]  P. Tackley,et al.  Using pattern recognition to infer parameters governing mantle convection , 2016 .

[37]  Felix V. Kaminsky,et al.  The Earth's Lower Mantle: Composition and Structure , 2017 .

[38]  N. Tosi,et al.  Mantle dynamics with pressure- and temperature-dependent thermal expansivity and conductivity , 2013 .

[39]  Wei Leng,et al.  Iron-spin transition controls structure and stability of LLSVPs in the lower mantle , 2015 .

[40]  David A. Yuen,et al.  Spin transition-induced anomalies in the lower mantle: Implications for mid-mantle partial layering , 2017 .

[41]  Barbara Romanowicz,et al.  The three‐dimensional shear velocity structure of the mantle from the inversion of body, surface and higher‐mode waveforms , 2000 .

[42]  M. H. Shahnas,et al.  Mid-mantle heterogeneities and iron spin transition in the lower mantle: Implications for mid-mantle slab stagnation , 2017 .

[43]  C. Lintott,et al.  Galaxy Zoo: reproducing galaxy morphologies via machine learning★ , 2009, 0908.2033.

[44]  Kerry Gallagher,et al.  Thermochemical interpretation of 1-D seismic data for the lower mantle: The significance of nonadiabatic thermal gradients and compositional heterogeneity , 2009 .

[45]  Harmen Bijwaard,et al.  Tethyan subducted slabs under India , 1999 .

[46]  Alexander G. Gray,et al.  Introduction to astroML: Machine learning for astrophysics , 2012, 2012 Conference on Intelligent Data Understanding.

[47]  Paul S. Dysart,et al.  Bathymetric surface modeling : A machine learning approach , 1996 .

[48]  W. R. Peltier,et al.  Deepest mantle viscosity: Constraints from Earth rotation anomalies , 2010 .

[49]  Renata M. Wentzcovitch,et al.  The high‐pressure electronic spin transition in iron: Potential impacts upon mantle mixing , 2011 .

[50]  F. Yin,et al.  Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis. , 2007, Medical physics.

[51]  Rami Qahwaji,et al.  Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares , 2009 .

[52]  J. Elliott,et al.  Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. , 2017 .

[53]  W. R. Peltier,et al.  The impacts of mantle phase transitions and the iron spin crossover in ferropericlase on convective mixing—is the evidence for compositional convection definitive? New results from a Yin‐Yang overset grid‐based control volume model , 2015 .

[54]  Warren T. Wood,et al.  A global prediction of seafloor sediment porosity using machine learning , 2015 .