Deformation Monitoring of Volcanic Eruption Using DInSAR Method

2017 ◽  
pp. 715-727
Author(s):  
P. Saranya ◽  
K. Vani
Keyword(s):  
Volcanic Eruption ◽  
Deformation Monitoring

scholarly journals PSInSAR Processing for Volcanic Ground Deformation Monitoring Over Fogo Island

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 3 ◽  
Author(s):  
Arun Babu ◽  
Shashi Kumar
Keyword(s):  
Phase Difference ◽  
Volcanic Eruption ◽  
Ground Deformation ◽  
Natural Disturbance ◽  
Volcanic Field ◽  
Deformation Monitoring ◽  
Line Of Sight ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Fogo Volcano

Persistent Scatterer Interferometry Synthetic Aperture Radar (PSInSAR) has been widely used in the precise measurement of ground deformation due to anthropogenic and natural disturbance of the earth’s surface. The present study has utilized the spaceborne C-band Sentinel-1 data for PSInSAR processing to generate a displacement map due to the volcanic eruption of Pico do Fogo volcano of the Fogo Island. An eruption was recorded in the year 2014–2015 and the Fogo volcano became active on 23 November 2014. It was observed that the intensity of the volcanic eruption during 2014–2015 had approached the intensity of the volcanic eruption of 1951, which was recorded as one of the strongest eruptions on the island. The volcanic eruption continued for 77 days and it stopped on 8 February 2015. To find the mean line-of-sight displacement from PSInSAR processing, a total of seven Single Look Complex (SLC) products of Sentinel-1 data in the interferometric mode were used. The SLC product of the SAR data that was acquired before the start of the volcanic eruption was chosen as the master image and all the remaining six slave images were precisely coregistered. The selection of Persistent Scatterers (PSs) is the most important step in PSInSAR processing. The initial set of PSs was identified by amplitude stability index and phase analysis was performed to estimate the phase stability of each resolution cell. After PS identification, 3D phase unwrapping was performed. The unwrapping step involved the low-pass filtering of the complex phase difference and time series in the frequency domain using a Gaussian window. The phase difference between each filtered data point was then calculated. The unwrapped phase of the interferogram was used to generate a displacement map for the volcanic field. The PSInSAR-based line-of-sight displacement was measured in the range of −34 mm to +35 mm and the standard deviation of the displacement ranged from +2 mm to +30 mm.

Hubungan Pengetahuan Tentang Manajemen Bencana Dengan Prevention Masyarakat Dalam Menghadapi Bencana Gunung Meletus Pada Kepala Keluarga Di Rt 06/Rw 01 Dusun Puncu Desa Puncu Kecamatan Puncu-Kediri

1970 ◽  
Vol 5 (2) ◽  
pp. 01
Author(s):  
Didit Damayanti ◽  
Pria Wahyu R.G ◽  
Muhanni’ah Muhanni’ah
Keyword(s):  
Disaster Management ◽  
Volcanic Eruption ◽  
Sampling Technique ◽  
P Value ◽  
Cross Sectional ◽  
Head Of Household ◽  
Correlational Research ◽  
Level Of Knowledge ◽  
Independent Variable ◽  
Negative Impacts

Introduction: Disaster management is a dynamic, continual, and integrated process as to increase the qualities of the actions which are relevant to the process of observation and analysis of disaster as well as minimalizing the negative impacts, mitigation, readiness, early warning, immediate emergency, rehabilitation and reconstruction. The aim of this research is to analyse theconnection between disaster management and the prevention of community breakdown in order to face a volcanic eruption for every head of household. Method: The design of this research is correlational research with a cross sectional approach. The demographic group that is used for this research is the head of households in Rt 06/Rw 01 dusun Puncu desa Puncu, by using the purposive sampling technique which has been collected from the sampling of the 33 heads of households. Independent variable is the knowledge of disaster management, and the dependent variable is the prevention of community breakdown in the handling of the disaster. The data has been received by using the questionnaire, and the results have been analysed by using spearman rho test. Result:  As according to the statistics test, it is found that p-value= 0,000 on the significant level (α) = 0,05 and r = 0,752. It is concluded that there is a connection between knowledge and the prevention of community breakdown in handling of the volcanic eruption in Rt 06/Rw 01. This research shows that the level of knowledge within the community about disaster management and prevention in handling volcanic eruption has been increasing. Conclution: This is shown by the capability of the community in mitigating the effects of the disaster. It is hoped that the community will further engage in training education and simulation to reduce the negative impacts of a disaster. The location where the participants resideis Kelud Volcano, and it is therefore hoped that the communities are willing to participate in better handling of any disaster by joining the education training and simulation; Kata kunci : Pengetahuan, Manajemen bencana, Prevention.

Volcanic eruption forecasting

Impact ◽  
2018 ◽  
Vol 2018 (6) ◽  
pp. 66-68
Author(s):  
Kostas Konstantinou
Keyword(s):  
Volcanic Eruption ◽  
Eruption Forecasting

LONG-TERM HYDROACOUSTIC AND VIDEO INVESTIGATIONS OF SUBMARINE VOLCANIC ERUPTION DYNAMICS AT NW ROTA-1 AND WEST MATA

2017 ◽  
Author(s):  
Adriana J. Cranston ◽  
◽  
Jackie Caplan-Auerbach ◽  
William W. Chadwick ◽  
Robert P. Dziak ◽  
...  
Keyword(s):  
Volcanic Eruption ◽  
Eruption Dynamics

scholarly journals Real-time automatic uncertainty estimation of coseismic single rectangular fault model using GNSS data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Keitaro Ohno ◽  
Yusaku Ohta ◽  
Satoshi Kawamoto ◽  
Satoshi Abe ◽  
Ryota Hino ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Order Approximation ◽  
Nonlinear Problems ◽  
Large Earthquake ◽  
Fault Model ◽  
Deformation Monitoring ◽  
Model Parameters ◽  
The Sea Of Japan ◽  
Plate Interface

AbstractRapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

Vibration Deformation Monitoring of Offshore Wind Turbines Based on GBIR

2021 ◽  
Vol 20 (3) ◽  
pp. 501-511
Author(s):  
Deming Ma ◽  
Yongsheng Li ◽  
Yanxiong Liu ◽  
Jianwei Cai ◽  
Rui Zhao
Keyword(s):  
Wind Turbines ◽  
Deformation Monitoring ◽  
Offshore Wind ◽  
Offshore Wind Turbines
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