scholarly journals Scaling characteristics of ULF geomagnetic fields at the Guam seismoactive area and their dynamics in relation to the earthquake

2001 ◽  
Vol 1 (3) ◽  
pp. 119-126 ◽  
Author(s):  
N. Smirnova ◽  
M. Hayakawa ◽  
K. Gotoh ◽  
D. Volobuev
Keyword(s):  
Time Series ◽  
Flicker Noise ◽  
Fractal Dimensions ◽  
Large Earthquake ◽  
Local Times ◽  
Noise Structure ◽  
Gradual Evolution ◽  
Self Organized Criticality ◽  
Geomagnetic Fields ◽  
Spectrum Slope

Abstract. The long-term evolution of scaling (fractal) characteristics of the ULF geomagnetic fields in the seismoactive region of the Guam Island is studied in relation to the strong (Ms = 8.0) nearby earthquake of 8 August 1993. The selected period covers 10 months before and 10 months after the earthquake. The FFT procedure, Burlaga-Klein approach and Higuchi method, have been applied to calculate the scaling exponents and fractal dimensions of the ULF time series. It is found that the spectrum of ULF emissions exhibits, on average, a power law behaviour S(f ) α f -b , which is a fingerprint of the typical fractal (self-affine) time series. The spectrum slope b fluctuates quasi-periodically during the course of time in a range of b = 2.5–0.7, which corresponds to the fractional Brownian motion with both persistent and antipersistent behaviour. An tendency is also found for the spectrum slope to decrease gradually when approaching the earthquake date. Such a tendency manifests itself at all local times, showing a gradual evolution of the structure of the ULF noise to a typical flicker noise structure in proximity to the large earthquake event. We suggest considering such a peculiarity as an earthquake precursory signature. One more effect related to the earthquake is revealed: the longest quasi-period, which is 27 days, disappeared from the variations of the ULF emission spectrum slope during the earthquake, and it reappeared three months after the event. Physical interpretation of the peculiarities revealed has been done on the basis of the SOC (self-organized criticality) concept.

scholarly journals Fractal analysis of the ULF geomagnetic data obtained at Izu Peninsula, Japan in relation to the nearby earthquake swarm of June–August 2000

2003 ◽  
Vol 3 (3/4) ◽  
pp. 229-236 ◽  
Author(s):  
K. Gotoh ◽  
M. Hayakawa ◽  
N. Smirnova
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Active Region ◽  
Fractal Analysis ◽  
Flicker Noise ◽  
Earthquake Swarm ◽  
Fractal Dimensions ◽  
Izu Peninsula ◽  
Geomagnetic Data ◽  
Fractal Characteristics

Abstract. In our recent papers we applied fractal methods to extract the earthquake precursory signatures from scaling characteristics of the ULF geomagnetic data, obtained in a seismic active region of Guam Island during the large earthquake of 8 August 1993. We found specific dynamics of their fractal characteristics (spectral exponents and fractal dimensions) before the earthquake: appearance of the flicker-noise signatures and increase of the time series fractal dimension. Here we analyze ULF geomagnetic data obtained in a seismic active region of Izu Peninsula, Japan during a swarm of the strong nearby earthquakes of June–August 2000 and compare the results obtained in both regions. We apply the same methodology of data processing using the FFT procedure, Higuchi method and Burlaga-Klein approach to calculate the spectral exponents and fractal dimensions of the ULF time series. We found the common features and specific peculiarities in the behavior of fractal characteristics of the ULF time series before Izu and Guam earthquakes. As a common feature, we obtained the same increase of the ULF time series fractal dimension before the earthquakes, and as specific peculiarity – this increase appears to be sharp for Izu earthquake in comparison with gradual increase of the ULF time series fractal dimension for Guam earthquake. The results obtained in both regions are discussed on the basis of the SOC (self-organized criticality) concept taking into account the differences in the depths of the earthquake focuses. On the basis of the peculiarities revealed, we advance methodology for extraction of the earthquake precursory signatures. As an adjacent step, we suggest the combined analysis of the ULF time series in the parametric space polarization ratio – fractal dimension. We reason also upon the advantage of the multifractal approach with respect to the mono-fractal analysis for study of the earthquake preparation dynamics.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

scholarly journals Self-Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Andrey Dmitriev ◽  
Victor Dmitriev ◽  
Stepan Balybin
Keyword(s):  
Time Series ◽  
Critical State ◽  
Empirical Investigation ◽  
Phenomenological Theory ◽  
Self Organization ◽  
Protest Movements ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Stochastic Sources ◽  
Adiabatic Mode

Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

scholarly journals Identification of earthquake precursors in the hydrogeochemical and geoacoustic data for the Kamchatka peninsula by flicker-noise spectroscopy

2011 ◽  
Vol 11 (2) ◽  
pp. 541-548 ◽  
Author(s):  
G. V. Ryabinin ◽  
Yu. S. Polyakov ◽  
V. A. Gavrilov ◽  
S. F. Timashev
Keyword(s):  
Statistical Physics ◽  
Systems Approach ◽  
Flicker Noise ◽  
Large Earthquake ◽  
Kamchatka Peninsula ◽  
Time Frame ◽  
Underground Water ◽  
Ion Concentration ◽  
Seismic Zone ◽  
Noise Spectroscopy

Abstract. A phenomenological systems approach for identifying potential precursors in multiple signals of different types for the same local seismically active region is proposed based on the assumption that a large earthquake may be preceded by a system reconfiguration (preparation) on different time and space scales. A nonstationarity factor introduced within the framework of flicker-noise spectroscopy, a statistical physics approach to the analysis of time series, is used as the dimensionless criterion for detecting qualitative (precursory) changes within relatively short time intervals in arbitrary signals. Nonstationarity factors for chlorine-ion concentration variations in the underground water of two boreholes on the Kamchatka peninsula and geacoustic emissions in a deep borehole within the same seismic zone are studied together in the time frame around a large earthquake on 8 October 2001. It is shown that nonstationarity factor spikes (potential precursors) take place in the interval from 70 to 50 days before the earthquake for the hydrogeochemical data and at 29 and 6 days in advance for the geoacoustic data.

scholarly journals Testing RNN-LSTM Forecasting with Simulated Astronomical Lightcurves

2019 ◽  
Author(s):  
Nachiketa Chakraborty
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Flicker Noise ◽  
Temporal Structure ◽  
Batch Size ◽  
Electromagnetic Spectrum ◽  
X Rays ◽  
Optimal Test ◽  
Noise Type ◽  
Lstm Network

With an explosion of data in the near future, from observatories spanning from radio to gamma-rays, we have entered the era of time domain astronomy. Historically, this field has been limited to modeling the temporal structure with time-series simulations limited to energy ranges blessed with excellent statistics as in X-rays. In addition to ever increasing volumes and variety of astronomical lightcurves, there's a plethora of different types of transients detected not only across the electromagnetic spectrum, but indeed across multiple messengers like counterparts for neutrino and gravitational wave sources. As a result, precise, fast forecasting and modeling the lightcurves or time-series will play a crucial role in both understanding the physical processes as well as coordinating multiwavelength and multimessenger campaigns. In this regard, deep learning algorithms such as recurrent neural networks (RNNs) should prove extremely powerful for forecasting as it has in several other domains. Here we test the performance of a very successful class of RNNs, the Long Short Term Memory (LSTM) algorithms with simulated lightcurves. We focus on univariate forecasting of types of lightcurves typically found in active galactic nuclei (AGN) observations. Specifically, we explore the sensitivity of training and test losses to key parameters of the LSTM network and data characteristics namely gaps and complexity measured in terms of number of Fourier components. We find that typically, the performances of LSTMs are better for pink or flicker noise type sources. The key parameters on which performance is dependent are batch size for LSTM and the gap percentage of the lightcurves. While a batch size of $10-30$ seems optimal, the most optimal test and train losses are under $10 \%$ of missing data for both periodic and random gaps in pink noise. The performance is far worse for red noise. This compromises detectability of transients. The performance gets monotonically worse for data complexity measured in terms of number of Fourier components which is especially relevant in the context of complicated quasi-periodic signals buried under noise. Thus, we show that time-series simulations are excellent guides for use of RNN-LSTMs in forecasting.

PLAYER'S POSITIONAL DEPENDENCE OF FRACTAL BEHAVIORS IN A SOCCER GAME

Fractals ◽  
2006 ◽  
Vol 14 (01) ◽  
pp. 71-76 ◽  
Author(s):  
SANGRAK KIM
Keyword(s):  
Time Series ◽  
Video Processing ◽  
Fractal Dimensions ◽  
Two Dimensional ◽  
Severe Attack ◽  
Soccer Game ◽  
Relative Dominance ◽  
Processing Techniques

This paper describes fractal behaviors in a soccer game according to the player's position. It is quite important for us to characterize the fractal motion behaviors of the objects during the game. We obtained two-dimensional coordinates of the objects using standard video processing techniques from a computer soccer game. We calculated values of regularization dimensions of the time series to characterize their fractal behaviors. To see positional dependence, we averaged individual player's values over the same position in the same team. When a team is one-sidedly experiencing a severe attack, its defenders have higher fractal dimensions than those of the opponent's corresponding players. We propose a new measure of relative dominance in attack against the opponent team.

scholarly journals Fractal analysis for the ULF data during the 1993 Guam earthquake to study prefracture criticality

2006 ◽  
Vol 13 (4) ◽  
pp. 409-412 ◽  
Author(s):  
Y. Ida ◽  
M. Hayakawa
Keyword(s):  
Fractal Analysis ◽  
Main Shock ◽  
Epicentral Distance ◽  
Sense Of Self ◽  
Low Frequency ◽  
Large Earthquake ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Electromagnetic Signature

Abstract. An extremely large earthquake (with magnitude of 8.2) happened on 8 August 1993 near the Guam island, and ultra-low-frequency (ULF) (frequency less than 1 Hz) electromagnetic fields were measured by 3-axis induction magnetometers at an observing station (with the epicentral distance of 65 km) with sampling frequency of 1 Hz. In order to study electromagnetic signature of prefracture criticality, we have undertaken the fractal (mono-fractal) analysis by means of the Higuchi's method for the ULF data during the 1993 Guam earthquake. Then, it is found that the fractal dimension exhibits five maxima 99, 75, 52, 21, and 9–4 days before the earthquake main shock, which suggests the ULF electromagnetic signature of nonlinear evolution (in the sense of self-organized criticality) taking place in the lithosphere just before the 1993 large Guam earthquake. That is, there take place step-like changes in the lithosphere during the long-term of the order of several months before the main shock.

Fractal dimensions for rainfall time series

1999 ◽  
Vol 48 (4-6) ◽  
pp. 437-446 ◽  
Author(s):  
M.C. Breslin ◽  
J.A. Belward
Keyword(s):  
Time Series ◽  
Fractal Dimensions ◽  
Rainfall Time Series

Systematic worldwide statistical correlation of physical and chemical atmospheric parameters before large earthquakes in the last four decades

2020 ◽  
Author(s):  
Dedalo Marchetti ◽  
Alessandro Piscini ◽  
Angelo De Santis ◽  
Caroline Ganglo ◽  
Gianfranco Cianchini ◽  
...  
Keyword(s):  
Time Series ◽  
Large Earthquake ◽  
Surface Concentration ◽  
Statistical Correlation ◽  
Historical Time ◽  
Atmospheric Parameters ◽  
Swarm Satellites ◽  
Preparation Phase ◽  
A Chain ◽  
Physical And Chemical

<p>Applying a multi-parametric approach, we already investigated the preparatory phase of several medium and large (M6.0 ~ M8.3) earthquakes occurred in the last 6 years in different locations in the World. In some cases, a chain of processes from the lithosphere to atmosphere and ionosphere has been successfully detected (e.g. M7.8 Ecuador 2016: Akhoondzadeh, 2018, ASR, https://doi.org/10.1016/j.asr.2017.07.014; Italian seismic sequence (M6.5) 2016-2017: Marchetti et al., 2019, RSoE, https://doi.org/10.1016/j.rse.2019.04.033; M7.5 Indonesia 2018: Marchetti et al., 2019, JAES, https://doi.org/10.1016/j.jseaes.2019.104097). These analyses underline the importance to study all the “spheres” that surround the Earth as suggested by a Geosystemic approach (De Santis et al., 2019, Entropy, https://doi.org/10.3390/e21040412). To analyse the anomalies that occur in the atmosphere we typically calculate the mean and standard deviation of the “historical time series” of the investigated parameter based on around 40 years of data, and then we superpose the value of the same quantity in the earthquake year. If the value overpasses two standard deviations of the historical time series, we define this day/parameter as anomalous. Applying the same methodology presented in previous works that studied climatological parameters such as skin temperature, total column water vapour, aerosols, and SO<sub>2</sub>, which <sub> </sub>seem to provide anomalies possibly related to the earthquake preparation phase (e.g. Piscini et al., 2017, PAGeoph, https://doi.org/10.1007/s00024-017-1597-8), here we investigate more atmospheric parameters proposed as possible precursors in the Lithosphere Atmosphere Ionosphere Coupling (LAIC) models (Pulinets and Ouzounov, 2011, JAES, https://doi.org/10.1016/j.jseaes.2010.03.005) such as methane and surface concentration of carbon monoxide. Other parameters, such as dimethylsulfide could be useful in other geophysical events, such as the volcano eruptions (Piscini et al. PAGeoph 2019, https://doi.org/10.1007/s00024-019-02147-x).</p><p>In this study, we also apply a Worldwide Statistical Correlation (WSC), as it was successfully applied to Swarm satellites electromagnetic anomalies and earthquakes, providing some statistical evidence for such perturbations in ionosphere before the occurrence of M5.5+ earthquakes (De Santis et al., 2019, Sci. Rep., https://doi.org/10.1038/s41598-019-56599-1).</p><p>The statistical approaches applied to these climatological data, provided by meteorological agencies such as ECMWF and NOAA, provides some interesting concentrations of atmospheric anomalies, preceding from days to several weeks the occurrence of the largest earthquakes from 1980 to 2017.</p><p>The study of several chemical and physical (e.g. aerosol particles) components in the atmosphere, the involved physical processes, the chemical reactions and chemical constraints (such as the elements lifetime and interactions in the atmosphere) can help to distinguish which LAIC model is more reliable to produce the observed anomalies before the occurrence of a large earthquake.</p><p> </p>

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