Crustal strain field in central japan, based on the wavelet analyzed GPS time-series data

2003 ◽  
Vol 8 (2) ◽  
pp. 731-735
Author(s):  
Li Jian-xin ◽  
K. Miyashita ◽  
P. Ivan ◽  
H. Torimoto
Keyword(s):  
Time Series ◽  
Strain Field ◽  
Time Series Data ◽  
Series Data ◽  
Central Japan ◽  
Crustal Strain ◽  
Gps Time Series

scholarly journals Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

2021 ◽  
Vol 11 (24) ◽  
pp. 11852
Author(s):  
Astri Novianty ◽  
Irwan Meilano ◽  
Carmadi Machbub ◽  
Sri Widiyantoro ◽  
Susilo Susilo
Keyword(s):  
Time Series ◽  
Genetic Algorithms ◽  
Early Warning ◽  
Time Series Data ◽  
Source Parameters ◽  
Large Earthquake ◽  
Series Data ◽  
Inversion Method ◽  
Permanent Displacement ◽  
Gps Time Series

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.

scholarly journals Analysis of Tectonic Plate Velocity Variations in the Sunda Strait Based on GPS Time-series Data

2021 ◽  
Vol 873 (1) ◽  
pp. 012084
Author(s):  
Y Dhira ◽  
I Meilano ◽  
D W Dudy
Keyword(s):  
Time Series ◽  
Subduction Zone ◽  
Time Series Data ◽  
Series Data ◽  
Time Interval ◽  
Tectonic Plate ◽  
Tectonic Plates ◽  
Plate Velocity ◽  
Before And After ◽  
Gps Time Series

Abstract Indonesia is an earthquake-prone country located in the junction of four tectonic plates, namely the Indo-Australian, Eurasian, Philippine, and Pacific. The convergent boundary between tectonic plates is also called a subduction zone that can produce great earthquakes in the future. One of the subduction zones in Indonesia is the Sunda Strait subduction zone which predicted can release a M7.8 earthquake. Previous research stated that there is a change in tectonic plate velocity after an earthquake ruptured. It is likely that this could happen in the Sunda Strait area which has experienced several large earthquakes. In this study, we conducted research to find out the information on the tectonic plate velocity changes in the Sunda Strait. We used Global Positioning System (GPS) time-series data provided by Indonesia Geospatial Information Agency (BIG). The time series data is used to calculate the earthquake displacement, the changes in GPS velocity of before and after earthquake, and the changes in velocity of each time interval. Our results show that the horizontal displacement due to the earthquake at all GPS stations ranged from 3.34 mm to 7.36 mm in the north-south direction and -27.45 mm to 0.18 mm in the east-west direction. Furthermore, the result of the changes in GPS velocity before and after an earthquake ranged from 2.25 mm/year to 12.60 mm/year and 1.80 mm/year to 13.35 mm/year. The pattern of change in velocity is likely due to post-seismic deformation from the 2012 Indian Ocean earthquake, the 2016 Sumatra earthquake, and also other tectonic factors.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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