Synchronization of Geophysical Field Fluctuations

Lyubushin, A. A.

doi:10.1016/b978-0-12-813138-1.00006-7

Cited by 21 publications

(17 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is not designed to outline spectral information, identify weak synchronous signals, or automatically identify correct time shifts or choose the correct scaling. More sophisticated methods such as (Lyubushin, 2018b) address all of these concerns. However, even those readers with the resources to confidently apply one of the many vector time series methods available to yield results they are satisfied with may find the γ method useful as a diagnostic.…”

Section: Discussionmentioning

confidence: 99%

Feature identification in time series data sets

Shaw

Stastna

Coutino

et al. 2019

Heliyon

View full text Add to dashboard Cite

We present a computationally inexpensive, flexible feature identification method which uses a comparison of time series to identify a rank-ordered set of features in geophysically-sourced data sets. Many physical phenomena perturb multiple physical variables nearly simultaneously, and so features are identified as time periods in which there are local maxima of absolute deviation in all time series. Unlike other available methods, this method allows the analyst to tune the method using their knowledge of the physical context. The method is applied to a data set from a moored array of instruments deployed in the coastal environment of Monterey Bay, California, and a data set from sensors placed within the submerged Yax Chen Cave System in Tulum, Quintana Roo, Mexico. These example data sets demonstrate that the method allows for the automated identification of features which are worthy of further study.

show abstract

Section: Discussionmentioning

confidence: 99%

Feature identification in time series data sets

Shaw

Stastna

Coutino

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…The minimum entropy (1) was proposed in [ 11 ]. In works [ 3 , 5 , 6 , 11 , 12 , 13 ], it was used to analyze the predictive properties of seismic noise. Entropy (1) has common features with the multiscale entropy considered in [ 14 , 15 ].…”

Section: Description Of Seismic Noise Propertiesmentioning

confidence: 99%

“…The natural time approach [ 17 ] has its own toolboxes using multifractals and multiscale entropy for the analysis of seismicity [ 18 , 29 , 30 , 31 ]. The multifractal property

of seismic noise was used for the purposes of predicting earthquakes and assessing seismic hazard in [ 1 , 2 , 3 , 4 , 5 , 13 , 32 ]. The singularity spectrum support width

is used to study the behavior of various nonlinear systems.…”

Section: Description Of Seismic Noise Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Low-Frequency Seismic Noise Properties in the Japanese Islands

Lyubushin

2021

Entropy

Self Cite

View full text Add to dashboard Cite

The records of seismic noise in Japan for the period of 1997–2020, which includes the Tohoku seismic catastrophe on March 11, 2011, are considered. The following properties of noise are analyzed: The wavelet-based Donoho–Johnston index, the singularity spectrum support width, and the entropy of the wavelet coefficients. The question of whether precursors of strong earthquakes can be formulated on their basis is investigated. Attention is paid to the time interval after the Tohoku mega-earthquake to the trends in the mean properties of low-frequency seismic noise, which reflect the constant simplification of the statistical structure of seismic vibrations. Estimates of two-dimensional probability densities of extreme values are presented, which highlight the places in which extreme values of seismic noise properties are most often realized. The estimates of the probability densities of extreme values coincide with each other and have a maximum in the region: 30°N ≤ Lat ≤ 34°N, 136°E ≤ Lon ≤ 140°E. The main conclusions of the conducted studies are that the preparation of a strong earthquake is accompanied by a simplification of the structure of seismic noise. It is shown that bursts of coherence between the time series of the day length and the noise properties within annual time window precede bursts of released seismic energy. The value of the lag in the release of seismic energy relative to bursts of coherence is about 1.5 years, which can be used to declare a time interval of high seismic hazard after reaching the peak of coherence.

show abstract

“…Если скорость определяется законом Ниже для оценки мультифрактальных характеристик сигналов использовался метод, основанный на анализе флуктуаций после устранения масштабнозависимых трендов [Kantelhardt et al, 2002]. В работах [Lyubushin, 2010;2012;2013;2014;2018] мультифрактальные параметры ∆a и a * были использованы при оценке сейсмической опасности по свойствам сейсмического шума в Японии (см. также [Любушин, 2009; 2012; 2014]).…”

Section: авторегрессионная мера нестационарности сигнала Runclassified

Analysis of long-term observations of the groundwater level In an aseismic region

2019

View full text Add to dashboard Cite

The results of the analysis of continuous precise time series of atmospheric pressure and groundwater level fluctuations in a well drilled to a depth of 400 m in the territory of Moscow are presented. The observations are remarkable in terms of their duration of more than 22 years (from February 2, 1993 to April 4, 2015) and by the sampling interval of 10 min. These long observations are suitable for exploring the stationarity of the properties of hydrogeological time series in a seismically quiet region, which is important from the methodological standpoint for interpreting the similar observations in seismically active regions aimed at earthquake prediction. Factor and cluster analysis applied to the sequence of multivariate vectors ofthe statistical properties of groundwater level time series in the successive 10-day windows after adaptive compensation for atmospheric pressure effects distinguish five different statistically significant states of the time series with the transitions between them. An attempt to geophysically interpret the revealed states is made. Two significant periods – 46 and 275 days – are established by spectral analysis of the sequence of the transitions times between the clusters.

show abstract

Synchronization of Geophysical Field Fluctuations

Cited by 21 publications

References 50 publications

Feature identification in time series data sets

Feature identification in time series data sets

Low-Frequency Seismic Noise Properties in the Japanese Islands

Analysis of long-term observations of the groundwater level In an aseismic region

Contact Info

Product

Resources

About