scholarly journals Secular crustal deformation in central Japan, based on the wavelet analysis of GPS time-series data

2002 ◽  
Vol 54 (2) ◽  
pp. 133-139 ◽  
Author(s):  
K. Vijay Kumar ◽  
Kaoru Miyashita ◽  
Jianxin Li
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Crustal Deformation ◽  
Time Series Data ◽  
Series Data ◽  
Central Japan ◽  
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.

Slip-deficit estimation with a 3D fault model of the North Anatolian Fault by using InSAR time series

2021 ◽  
Author(s):  
Alison Seidel ◽  
Henriette Sudhaus
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Time Series Data ◽  
Fault Model ◽  
North Anatolian Fault ◽  
Series Data ◽  
The North ◽  
Slip Deficit ◽  
Finite Fault ◽  
Finite Fault Model

<p>Crustal earthquakes are events of sudden stress release throug­h rock failure, for example as a consequence of continuous and long-term stress buildup at tectonic faults that eventually exceeds the strength of rock. Before failure, under increasing stress at a fault, the surrounding crust is slowly deforming. The amount and pattern of crustal deformation carries information about location and potential magnitude of future earthquakes.</p><p>Time series of space-borne interferometric Synthetic Aperture Radar (InSAR) data can be used to precisely measure the surface motion, which corresponds to the crustal deformation, in the radar line-of-sight and across large areas. These observations open the opportunity to study fault loading in terms of location, size of locked parts at faults and their slip deficit. Here we study the North Anatolian Fault (NAF), a major right-lateral strike-slip fault zone of about 1500 km length in the north of Turkey and we create its first large-scale 3D finite-fault model based on InSAR data.</p><p>We use the InSAR time series of data recorded by ESA’s Envisat SAR satellite between 2002 and 2010 (Hussain et al., 2018 and Walters et al., 2014).<!-- Das ist nicht ganz eindeutig formuliert. rigid motion darf nicht auf die InSAR Daten bezogen werden. --> We represent the fault with several vertical, planar fault segments that trace the NAF with reasonable resolution. The medium model is a layered half space with a viscoelastic lower crust and mantle. Several GNSS velocity measurements are used to apply a trend correction and calibrate the InSAR time series data to an Eurasia-fixed-reference frame. We use the plate motion difference of the Anatolian and the Eurasian plates calculated through an Euler pole to set up a back-slip finite-fault model. We then optimize the back-slip as the slip deficit, the width and the depth of the locked fault zone at each segment to achieve a good fit to the measured surface motion.</p><p>We find shallow locking depths and small slip deficits in the eastern and westernmost regions of the NAF, while the central part shows both deeper locking depths and larger slip deficits for the observation period. <!-- So wie es jetzt ist sind es zu viele Wörter, wenn man diesen erklär-Satz rausnehmen würde, würde es gerade so passen. Für die Erdbebenaktivität im Osten hab ich bis jetzt für den Zeitraum auch noch kein entsprechendes Paper gefunden, da suche ich aber noch. -->For both parameters the trends are in an overall agreement to earlier studies. There, InSAR-time series data have been used to calculate slip deficits at the North Anatolian fault with 2D models and/or assuming a homogeneous and purely elastic medium.<!-- Passt vom flow jetzt besser hier hin, denke ich. --> Local modeled differences therefore might be connected to differences in the modeling approaches, but also remain subject to further investigations and discussions.</p><p>Our model provides a very suitable basis for future time-dependent modeling of the slip deficit at the NAF that includes also more recent InSAR time series based on data from the Sentinel-1 radar satellite mission of ESA.</p>

WAVELET ANALYSIS OF TIME-SERIES: COHERENT STRUCTURES, CHAOS AND NOISE

1999 ◽  
Vol 09 (03) ◽  
pp. 455-471 ◽  
Author(s):  
W. J. STASZEWSKI ◽  
K. WORDEN
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Coherent Structures ◽  
Wavelet Transforms ◽  
Time Series Data ◽  
Series Data ◽  
Different Types ◽  
Analyze Time Series Data ◽  
Analyze Time Series ◽  
Analysis Of Time Series

The continuous and orthogonal wavelet transforms are used to analyze time-series data. The analysis involves signal decomposition into scale components using both Grossman–Morlet and Daubechies type wavelets. A number of simulated and experimental data vectors exhibiting different types of coherent structures, chaos and noise is analyzed. The study shows that wavelet analysis provides a unifying framework for the description of many phenomena in time-series.

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
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