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volcano video data characterized and classified using computer vision and machine learning algorithms

Geoscience Frontiers 2020年第5期11卷 1789-1803页

作者： Alex J.C.Witsil Jeffrey B.Johnson Department of Geosciences Boise State UniversityBoiseIDUSA

Video cameras are common at volcano observatories,but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual *** cameras to serve as effective monitoring tools,video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable *** this study,we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video *** were collected at Villarrica volcano,Chile from two visible band cameras located^17 km from the vent that recorded at 0.1 and 30 frames per second between February and April *** these two months,Villarrica exhibited a diverse range of eruptive activity,including a paroxysmal eruption on 3 *** to and after the eruption,activity included nighttime incandescence,dark and light emissions,inactivity,and periods of cloud *** quantify the color and spatial extent of plume emissions using a blob detection algorithm,whose outputs are fed into a trained artificial neural network that categorizes the observable activity into five *** shifts from primarily nighttime incandescence to ash emissions following the 3 March paroxysm,which likely relates to the reemergence of the buried lava *** periods exhibiting plume emissions are further analyzed using a row and column projection algorithm that identifies plume onsets and calculates apparent plume horizontal and vertical rise *** onsets are episodic,occurring with an average period of^50 s and suggests a puffing style of degassing,which is commonly observed at ***,the lack of clear acoustic transients in the accompanying infrasound record suggests puffing may be controlled by atmospheric effects rather than a degassing regime at the *** presented here offer a generalized toolset for volcano monito

关键词： volcano monitoring Plume exit velocity volcano webcam Image processing Machine learning Villarrica

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Subcritical dyke propagation in a host rock with temperature-dependent viscoelastic properties

Subcritical dyke propagation in a host rock with temperature...

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中国科学院地质与地球物理研究所第11届（2011年度）学术年会

作者： Zuan Chen~1 and Z.-H.Jin~2 1 Key Laboratory of the Study of Earth’s Deep Interior,Institute of Geology and Geophysics,Chinese Academy of Sciences,Beijing 100029,China. 2 Department of Mechanical Engineering,University of Maine Orono,ME 04469,USA

SUMMARY In this paper,we examine the effects of temperature-dependent viscoelastic properties of the host rock on the suberitical growth of a dyke from a magma chamber.A theoretical relationship between the velocity of subcritical dyke growth and dyke length is established using a perturbation solution of stress intensity factor at the dyke tip and a viscoelastic crack growth theory in which the temperature-dependent creep properties are taken into account. The temperature field around the dyke is calculated using an analytic *** numerical results for a dyke subcritically propagating from a magma chamber indicate that while the general dyke growth characteristics are similar to those with constant creep properties,the subcritical dyke growth velocity is increased by an order of magnitude by considering the temperature dependence of the creep ***,the subcritical growth duration before the dyke reaches the unstable growth state is significantly shortened.

关键词： Mechanic s theory and modelling Magma migration and fragmentation volcano monitoring

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