Modeling the multi-level plumbing system of the Changbaishan caldera from geochemical, mineralogical, Sr-Nd isotopic and integrated geophysical data

Yi, Jiawei; Wang, Pujun; Shan, Xuanlong; Ventura, Guido; Wu, Chengzhi; Guo, Jianchun; Liu, Pencheng; Li, Jiahui

doi:10.1016/j.gsf.2021.101171

Cited by 20 publications

(13 citation statements)

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“…The above summarized structural, geophysical, and morphological data coherently indicate that the volcano formed on a NW-SE weakness zone possibly controlled by the Baishan-Jince regional fault system, which extends from North Korea to China. Changbaishan represents one of the most dangerous Eastern Asia volcanoes because of its 'Millennium' eruption (ME), which emitted about 96 ± 19 km 3 of pyroclastics and whose gas emissions had a global impact on climate (Horn and Schmincke, 2000;Iacovino et al, 2016;Yi et al, 2021). The 2002-2006 unrest characterized by deformations, earthquakes including low-frequency events, and changes in volcanic gas geochemistry (Xu et al, 2012).…”

Section: Geological and Geophysical Setting Eruptive History Tectonic...mentioning

confidence: 99%

“…As a result, the knowledge of the complex plumbing system of volcanoes is of primary importance to evaluate the volcano dynamics and assess the hazard. Changbaishan volcano (China/North Korea) (Figure 1) is characterized by a multi-level plumbing system consisting of reservoirs located at different depth and a shallower hydrothermal system (Zhang et al, 2015;Zhang et al, 2018;Yi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we analyze satellite images from the ALOS-2 platform acquired between November 2018 and October 2020 on Changbaishan, which last erupted in 1903 and underwent an unrest episode in [2002][2003][2004][2005][2006]. The volcano is characterized by a summit caldera filled by a lake and it is considered the most dangerous volcano in China and surrounding countries (Russia, North and South Korea) because it was responsible for the largest eruption of the first millennium on Earth, the magnitude M 7.2, 946 CE "Millennium" eruption (Horn and Schmincke, 2000;Iacovino et al, 2016;Yi et al, 2021), whose products have been found in Russia, Japan, China Sea, Korean peninsula, and Greenland (Sun et al, 2014). Between 2002 and, Changbaishan experienced an increase of seismicity, changes in the gas composition, and deformations (uplift) (Xu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Upward Magma Migration Within the Multi-Level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018–2020 SAR Data

et al. 2021

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Changbaishan volcano (China/North Korea border) was responsible for the largest eruption of the first Millennium in 946 CE and is characterized by a multi-level plumbing system. It last erupted in 1903 and presently consists of a cone with summit caldera. An unrest episode occurred between 2002 and 2006, followed by subsidence. Here, we analyze the Changbaishan 2018–2020 deformations by using remote sensing data, detecting an up to 20 mm/yr, NW-SE elongated, Line of Sight movement of its southeastern flank and a −20 mm/yr Line of Sight movement of the southwestern flank. This reveals an unrest occurring during 2018–2020. Modeling results suggest that three active sources are responsible for the observed ground velocities: a deep tabular deflating source, a shallower inflating NW-SE elongated spheroid source, and a NW-SE striking dip-slip fault. The depth and geometry of the inferred sources are consistent with independent petrological and geophysical data. Our results reveal an upward magma migration from 14 to 7.7 km. The modeling of the leveling data of the 2002–2005 uplift and 2009–2011 subsidence depicts sources consistent with the 2018–2020 active system retrieved. The past uplift is interpreted as related to pressurization of the upper portion of the spheroid magma chamber, whereas the subsidence is consistent with the crystallization of its floor, this latter reactivated in 2018–2020. Therefore, Changbaishan is affected by an active magma recharge reactivating a NW-SE trending fault system. Satellite data analysis is a key tool to unravel the magma dynamics at poorly monitored and cross-border volcanoes.

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Section: Geological and Geophysical Setting Eruptive History Tectonic...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Upward Magma Migration Within the Multi-Level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018–2020 SAR Data

et al. 2021

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“…Therefore, the rhyolitic GST-314 tephra was not from a local eruption of the LVF. Changbaishan volcano, on the border between China and North Korea, ~120 km to the west, experienced frequently felsic volcanic eruptions during the Quaternary period (Chen et al, 2016;Liu et al, 2015;Sun et al, 2014Sun et al, , 2017Sun et al, , 2018Yi et al, 2021;Zhang et al, 2018). However, Changbaishan tephras predominantly with trachytic and rhyolitic glass compositions exhibit higher potassium content than GST-314, which excludes it as the source (Fig.…”

Section: Depth (Cm)mentioning

confidence: 99%

~5.9 cal ka bp Towada‐Chuseri tephra from Towada volcano: a mid‐Holocene marker layer from Japan to northeast China

Sun

Plunkett

Zhu

et al. 2021

J Quaternary Science

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Far‐travelled ash layers from explosive volcanic eruptions can provide invaluable marker horizons for dating and correlating regional to global sedimentary archives. Here, we present a new cryptotephra associated with the ~5.9 cal ka bp Towada‐Chuseri eruption (To‐Cu) in a peat sediment record from northeast China. This tephra exhibits a rhyolitic glass composition that can be distinguished from other widespread tephra layers around the region of Japan and northeast China. Our findings extend the known range of this ash significantly, making it now traceable about 1200 km from its source, Towada volcano, Japan. Notably, this tephra provides an important isochron for synchronising palaeoenvironmental studies during the mid‐Holocene period from the western Pacific, central Japan, Japan Sea and northeast China.

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“…The sports preference, hobbies, and personality of college students can be obtained by questionnaire. The situation in physical education class is recorded by physical education teachers and physical education committee members in class and recorded in the database of teaching management platform after class [18]. The training situation after physical education class is collected by college students by entering the "college physical education independent monitoring training data system" (hereinafter referred to as the automatic control data system).…”

Section: Data Collection and Preprocessing Of Physical Training Inmentioning

confidence: 99%

Discrete Dynamic Modeling Analysis of Data Mining Algorithm under the Background of Big Data in the Strategic Goal of Sustainable Development of College Physical Training

Li¹,

Liu²

2022

Wireless Communications and Mobile Computing

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With the popularization of intelligent devices and the rapid development of information technology, many valuable data become easier to obtain. After obtaining massive data, the problem of how to make more efficient and scientific use of these data is in front of us. Data mining is a deeper processing of data. It is aimed at mining the internal associations hidden in complex data and making the data give full play to its maximum value. At present, college physical training is also in urgent need of more scientific and modern upgrading and reform. Data mining is introduced into college physical training so that physical training data is no longer just a simple collection, query, and storage. This paper uses and introduces the basic content of Apriori algorithm. The actual college sports training data are collected for preprocessing, and the discrete dynamic model is established by using Apriori algorithm. Using this model, the actual data are deeply analyzed. Using the strong correlation subintegration results, the physical training of college students is optimized and reformed. The scores of college students’ physical quality are improved, and the goal of sustainable development of college physical training is achieved. Based on the data of 126 students in four classes of a major in 2020, this paper mines the frequent subsets with strong correlation in the data and finds out the relationship between the correlation and trust between subsets. In the 70-day experiment, the scores of college students’ comprehensive physical quality have been significantly improved, which proves that the discrete dynamic model established by Apriori algorithm data mining has a significant role in college physical training. The questionnaire survey results show that 83% of the college students are very satisfied with the discrete dynamic model-assisted physical training, and 16% of the college students are basically satisfied with the system. Apriori algorithm has significant advantages in dealing with big data. Single Apriori algorithm also has some defects. The efficiency of data mining, data security, and the accuracy of model analysis results need to be tested in practice.

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Modeling the multi-level plumbing system of the Changbaishan caldera from geochemical, mineralogical, Sr-Nd isotopic and integrated geophysical data

Cited by 20 publications

References 63 publications

Upward Magma Migration Within the Multi-Level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018–2020 SAR Data

Upward Magma Migration Within the Multi-Level Plumbing System of the Changbaishan Volcano (China/North Korea) Revealed by the Modeling of 2018–2020 SAR Data

~5.9 cal ka bp Towada‐Chuseri tephra from Towada volcano: a mid‐Holocene marker layer from Japan to northeast China

Discrete Dynamic Modeling Analysis of Data Mining Algorithm under the Background of Big Data in the Strategic Goal of Sustainable Development of College Physical Training

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