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 analysis.For came...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 analysis.For 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 activity.In this study,we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations.Data 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 2015.Over these two months,Villarrica exhibited a diverse range of eruptive activity,including a paroxysmal eruption on 3 March.Prior to and after the eruption,activity included nighttime incandescence,dark and light emissions,inactivity,and periods of cloud cover.We 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 classes.Activity shifts from primarily nighttime incandescence to ash emissions following the 3 March paroxysm,which likely relates to the reemergence of the buried lava lake.Time 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 velocities.Plume onsets are episodic,occurring with an average period of^50 s and suggests a puffing style of degassing,which is commonly observed at Villarrica.However,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 vent.Methods presented here offer a generalized toolset for volcano monitors to classify and track emission statistics at a variety of volcanoes to better monitor periods of unrest and ultimately forecast major eruptions.展开更多
The Tianchi volcano in the Changbai Mountains is located on the boundary between China and North Korea.There are many times of eruptions of the Tianchi volcano during the Holocene.One of its large eruptions occurred a...The Tianchi volcano in the Changbai Mountains is located on the boundary between China and North Korea.There are many times of eruptions of the Tianchi volcano during the Holocene.One of its large eruptions occurred around 1000 years ago dated by ^(14)C method and historical records.Composition of products of the largest Tianchi volcanic eruption studied is characterized by comendi-tic Plinian fallout and unwelded ignimbrite,which are mainly distributed in China and North Korea.Caldera is about 4.4 km long and 3.4 km wide,which had filled with water(e.g.Tianchi Lake).The Tianchi volcanic cone is about 2700 m high above sea level.The Tianchi Lake is located on the summit of the volcanic cone,that is also highest peak of the Changbai Mountains in northeastern China.This study analyzed Cl,F,S and H_(2)O concentrations of melt inclusions in the phenocryst min-erals(anorthoclase and quartz)and co-existing matrix glasses using the electron microprobe and estimated environmental effect of Tianchi volcanic gases.The authors proposed a new method to evaluate future eruption of active volcano and estimate potential volcanic hazards based on contents of volatile emissions.Using this method,we made a perspective of future volcanic hazard in this region.展开更多
基金partially supported by National Science Foundation grant EAR-0838562 and EAR1830976。
文摘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 analysis.For 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 activity.In this study,we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations.Data 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 2015.Over these two months,Villarrica exhibited a diverse range of eruptive activity,including a paroxysmal eruption on 3 March.Prior to and after the eruption,activity included nighttime incandescence,dark and light emissions,inactivity,and periods of cloud cover.We 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 classes.Activity shifts from primarily nighttime incandescence to ash emissions following the 3 March paroxysm,which likely relates to the reemergence of the buried lava lake.Time 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 velocities.Plume onsets are episodic,occurring with an average period of^50 s and suggests a puffing style of degassing,which is commonly observed at Villarrica.However,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 vent.Methods presented here offer a generalized toolset for volcano monitors to classify and track emission statistics at a variety of volcanoes to better monitor periods of unrest and ultimately forecast major eruptions.
基金supported by the National Natural Science Foundation of China(Grant No.40372045)the Chinese Academy of Sciences(Grant No.KZCX3-SW-145)the Chinese Academy of Sciences(Grant No.KZCX3-SW-145).
文摘The Tianchi volcano in the Changbai Mountains is located on the boundary between China and North Korea.There are many times of eruptions of the Tianchi volcano during the Holocene.One of its large eruptions occurred around 1000 years ago dated by ^(14)C method and historical records.Composition of products of the largest Tianchi volcanic eruption studied is characterized by comendi-tic Plinian fallout and unwelded ignimbrite,which are mainly distributed in China and North Korea.Caldera is about 4.4 km long and 3.4 km wide,which had filled with water(e.g.Tianchi Lake).The Tianchi volcanic cone is about 2700 m high above sea level.The Tianchi Lake is located on the summit of the volcanic cone,that is also highest peak of the Changbai Mountains in northeastern China.This study analyzed Cl,F,S and H_(2)O concentrations of melt inclusions in the phenocryst min-erals(anorthoclase and quartz)and co-existing matrix glasses using the electron microprobe and estimated environmental effect of Tianchi volcanic gases.The authors proposed a new method to evaluate future eruption of active volcano and estimate potential volcanic hazards based on contents of volatile emissions.Using this method,we made a perspective of future volcanic hazard in this region.
