scholarly journals The Depth of Pressure Source and Magma Supply Volume for Merapi Eruption during 2009-2011 Using the Combination of Yokoyama and Mogi Model

2019 ◽  
Author(s):  
Andi Anshariyah ◽  
Abdul Basid
Keyword(s):  
Pressure Source ◽  
Magma Supply

scholarly journals Numerical Modeling Of Pressure Source Of Sinabung Volcano Based On GPS Data In 2011-2012 Using Particle Swarm Optimization (PS0)

2020 ◽  
Vol 3 (2) ◽  
pp. 30
Author(s):  
Ratih Kumalasari ◽  
Wahyu Srigutomo ◽  
Irwan Meilano ◽  
Hendra Gunawan
Keyword(s):  
Numerical Modeling ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Gps Data ◽  
Pressure Source ◽  
Swarm Optimization ◽  
Magma Supply ◽  
Shallow Magma Chamber ◽  
And Inversion ◽  
To Receive

Mogi Model with particle swarm optimization (PSO) scheme have been applied to the local GPS data of Sinabung Volcano during 2011 to 2012 to receive subsurface parameters as pressure sources in terms of misfit and inversion model parameter. The size of displacement was inverted by PSO. From the inversion concluded that the position pressure source showing shallow magma pockets at a depth between ±1.3 km volume change around +0.95x106 m3 . It indicates the presence of a huge magma supply and continuous into shallow magma chamber up to the surface of Sinabung Volcano.

scholarly journals Potential Eruption and Current Activity of Anak Krakatau Volcano, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012021
Author(s):  
Kristianto ◽  
N Indrastuti ◽  
A Basuki ◽  
H D Purnamasari ◽  
S Adi ◽  
...  
Keyword(s):  
Volcanic Activity ◽  
Early Warning System ◽  
Warning System ◽  
Pressure Source ◽  
Volcanic System ◽  
Near Surface ◽  
Surface Emission ◽  
Magma Supply ◽  
Harmonic Tremors ◽  
Krakatau Volcano

Abstract Anak Krakatau Volcano is located in the Sunda Strait known for its paroxysmal eruption in 1883. During the January - November 2019 period, seismicity was dominated by types of quakes which indicated the occurrence of magma supply (VA and VB), near-surface volcanic activity (LF, Hybrid, Harmonic Tremors), and volcanic activity above the volcanic surface (eruptions, emission, and continuous tremors). In the period December 2019 - July 2020, there was an increase in the types of quakes near the surface (LF, Hybrid) and the types of quakes on the surface (emission and continuous tremors). Volcanic deformation monitors changes in tilt over the 2019-2020 period associated with pressure releases before, during and after the eruption. The results of GPS data modeling, the shallow pressure source is at a depth of 0.22 km below sea level. Volcanic activity until July 2020 was dominated by activity near and above the volcanic surface associated with the growth of lava domes. The volcanic system of Anak Krakatau is currently an open system, with the potential for eruptions. Strengthening the early warning system for the eruption of Anak Krakatau is important in mitigating efforts and understanding its eruption potential

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Research on the Pressure-Measuring Uncertainty of Standard Internal Crusher Gauge

2013 ◽  
Vol 300-301 ◽  
pp. 874-881
Author(s):  
Chuan Rong Zhao ◽  
De Ren Kong ◽  
Fang Wang ◽  
Li Xia Yang ◽  
Li Ping Li
Keyword(s):  
Theoretical Foundation ◽  
Evaluation Model ◽  
Computing Methods ◽  
Selection Methods ◽  
Pressure Source ◽  
True Value ◽  
Individual Character ◽  
Copper Cylinder ◽  
The Individual ◽  
Random Fluctuations

This article introduces the function, the method of selection and related criterion of standard internal crusher gauge, and systematically analyzes three factors that affect the measuring uncertainty of standard internal crusher gauge, including: inconsistency of pressure’s true value from pressure source, the uncertainty imported by standard copper-cylinder and the random fluctuations of the individual character of pressure measuring gauge. According to usage characteristics and selection methods of the standard internal crusher gauge, discusses computing methods of components of the measuring uncertainty and establishes evaluation model for measuring uncertainty of standard internal crusher gauge. The model can quantitatively calculate through experimental data of selection, which lay a theoretical foundation for the control of the pressure measuring uncertainty of standard internal crusher gauge.

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