Multi-channel singular spectrum analysis of underground Rn concentration at Asahi station, Japan: Preliminary report on the variation of underground Rn flux

2021 ◽  
Author(s):  
Katsumi Hattori ◽  
Haruna Kojima ◽  
Kazuhide Nemoto ◽  
Chie Yoshino ◽  
Toshiharu Konishi ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Spectrum Analysis ◽  
Preliminary Report ◽  
Singular Spectrum Analysis ◽  
Atmospheric Electricity ◽  
Heavy Rain ◽  
Ion Concentration ◽  
Weather Factors ◽  
Local Seismicity ◽  
Large Earthquakes

<p>There are many reports on electromagnetic pre-earthquake phenomena such as geomagnetic, ionospheric, and atmospheric anomalous changes. Ionospheric anomaly preceding large earthquakes is one of the most promising phenomena. Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model has been proposed to explain these phenomena. In this study, to evaluate the possibility of chemical channel of LAIC by observation, we have installed sensors for atmospheric electric field, atmospheric ion concentration, atmospheric Rn concentration, underground Rn concentration (GRC), and weather elements at Asahi station, Boso, Japan. Since the atmospheric electricity parameters are very much influenced by weather factors, it is necessary to remove these effects as much as possible. In this aim, we apply the MSSA (Multi-channel Singular Spectral Analysis) to remove these influences from the variation of GRC and estimate the underground Rn flux (GRF). We investigated the correlations (1) between GRF and precipitation and (2) between GRF and the local seismic activity around Asahi station. The preliminary results show that there is a tendency of correlation (1) between GRF and heavy rain and (2) between GRF and local seismicity within an epicenter distance of 50 km from the station.</p>

scholarly journals PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)

2019 ◽  
Vol 12 (2) ◽  
pp. 214
Author(s):  
Herni Utami ◽  
Yunita Wulan Sari ◽  
Subanar Subanar ◽  
Abdurakhman Abdurakhman ◽  
Gunardi Gunardi
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Electricity Demand ◽  
Signal Extraction ◽  
Forecasting Model ◽  
Window Length ◽  
Singular Spectral Analysis ◽  
Singular Spectrum

This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.

scholarly journals Detecting Earthquake-Related Anomalies of a Borehole Strain Network Based on Multi-Channel Singular Spectrum Analysis

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1086
Author(s):  
Zining Yu ◽  
Katsumi Hattori ◽  
Kaiguang Zhu ◽  
Chengquan Chi ◽  
Mengxuan Fan ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Local Seismicity ◽  
Earthquake Energy ◽  
Yunnan Region ◽  
Singular Spectrum ◽  
Complex Fault ◽  
Longmenshan Fault ◽  
Spatio Temporal ◽  
Network Anomalies

To investigate the nonlinear spatio-temporal behavior of earthquakes, a complex network has been built using borehole strain data from the southwestern endpoint of the Longmenshan fault zone, Sichuan-Yunnan region of China, and the topological structural properties of the network have been investigated based on data from 2011–2014. Herein, six observation sites were defined as nodes and their edges as the connections between them. We introduced Multi-channel Singular Spectrum Analysis (MSSA) to analyze periodic oscillations, earthquake-related strain, and noise in multi-site observations, and then defined the edges of the network by calculating the correlations between sites. The results of the daily degree centrality of the borehole strain network indicated that the strain network anomalies were correlatable with local seismicity associate with the earthquake energy in the strain network. Further investigation showed that strain network anomalies were more likely to appear before major earthquakes rather than after them, particularly within 30 days before an event. Anomaly acceleration rates were also found to be related to earthquake energy. This study has revealed the self-organizing pre-earthquake phenomena and verified the construction of borehole networks is a powerful tool for providing information on earthquake precursors and the dynamics of complex fault systems.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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