Dynamical Patterns in Seismology

Chélidzé, T.; Matcharashvili, Teimuraz

doi:10.1007/978-3-319-07155-8_10

Cited by 14 publications

(10 citation statements)

References 84 publications

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“…It has been discovered recently that teleseismic surface waves of remote strong earthquakes can synchronize a new class of tremors—non-volcanic tremors (NTV) [ 64 ]. The similar effect of synchronization was observed in laboratory conditions: periodically forced stick–slip (with a forcing period T ) also generated weak acoustic emission signals at multiples of T (Figure 10.8 and Figure 10.9 in [ 63 ]).…”

Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...supporting

confidence: 64%

“…The effect of strong electromagnetic pulses. From 1975 to 1996 near Bishkek polygon (Central Asia), experiments were carried out to study the effect of strong electromagnetic pulses generated by the MHD with some regular time intervals on the seismic regime of the region [ 63 ] (pp. 324–327).…”

Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...mentioning

confidence: 99%

“…The Earth/oceanic tides and seismicity. The conclusions of related publications are very different: in [ 62 , 63 , 64 , 65 , 66 ] a significant correlation between tides and seismicity was established, though [ 67 ] rejects the possibility of such an effect, arguing that the tectonic forces needed for EQ generation are many orders larger than tidal stress.…”

Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...mentioning

confidence: 99%

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Complexity in Geophysical Time Series of Strain/Fracture at Laboratory and Large Dam Scales: Review

Chélidzé

Matcharashvili

Mepharidze

et al. 2023

Entropy

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One of the interesting directions of complexity theory is the investigation of the synchronization of mechanical behavior of large-scale systems by weak forcing, which is one of manifestations of nonlinearity/complexity of a system. The effect of periodic weak mechanical or electromagnetic forcing leading to synchronization was studied on the laboratory load–spring system as well as on a big dam’s strain data. Due to synchronization, the phase space structure of the forced system strongly depends on the weak forcing intensity–determinism show itself in the recurrence of definite states of the forced system. The nonlinear dynamics of tilts/strains/seismicity near grand dams reflect both the complexity of the mentioned time series, connected with the natural agents (regional and local geodynamics), which were presented even before dam erection, as well as the effects of the water level (WL) variation in the reservoir, which is a quasi-periodic forcing superimposed on the natural geodynamic background. Both these effects are documented by the almost half-century of observations at the large Enguri Dam. The obtained data on the dynamics of strain/seismicity near a large dam can be used for the assessment of the possible risks, connected with the abrupt change of routine dynamics of construction.

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Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...supporting

confidence: 64%

Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...mentioning

confidence: 99%

Section: The Effect Of Weak Forcing Of Different Origin On the Dynami...mentioning

confidence: 99%

See 1 more Smart Citation

Complexity in Geophysical Time Series of Strain/Fracture at Laboratory and Large Dam Scales: Review

Chélidzé

Matcharashvili

Mepharidze

et al. 2023

Entropy

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“…The stick-slip process, especially under weak periodic forcing, reveals a quite regular pattern in the time domain, if even in the natural stick-slip process, the distribution of waiting times T w centers on the most probable value, the distribution became much more narrow under weak periodic forcing (Chelidze and Matcharashvili, 2015). Predicting not a statistical distribution of events but the time and amplitude of the next event is much more difficult.…”

Section: Eq Forecast In the Laboratory And In Numerical Modelsmentioning

confidence: 99%

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

et al. 2022

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In this article, we considered the problem of M≥3 earthquake (EQ) forecasting (hindcasting) using a machine learning (ML) approach, using experimental (training) time series on monitoring water-level variations in deep wells as well as geomagnetic and tidal time series in Georgia (Caucasus). For such magnitudes’, the number of “seismic” to “aseismic” days in Georgia is approximately 1:5 and the dataset is close to the balanced one. However, the problem of forecast is practically important for stronger events—say, events of M≥3.5—which means that the learning dataset of Georgia became more imbalanced: the ratio of seismic to aseismic days for in Georgia reaches the values of the order of 1:20 and more. In this case, some accepted ML classification measures, such as accuracy leads to wrong predictions due to a large number of true negative cases. As a result, the minority class, here—seismically active periods—is ignored at all. We applied specific measures to avoid the imbalance effect and exclude the overfitting possibility. After regularization (balancing) of the training data, we build the confusion matrix and performed receiver operating classification in order to forecast the next day probability of M≥3.5 earthquake occurrence. We found that the Matthews’ correlation coefficient (MCC) is the measure, which gives good results even if the negative and positive classes are of very different sizes. Application of MCC to observed geophysical data gives a good forecast of the next day M≥3.5 seismic event probability of the order of 0.8. After randomization of EQ dates in the training dataset, the Matthews’ coefficient efficiency decreases to 0.17.

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“…Quantification of RP allows to interpret various dynamical regimes emerging in time series in terms of signal complexity and reveal new features of these time series. In particular, RPs were applied to biological data analysis [Hirata et al, 2016;Acharya et al, 2011;Acharya et al, 2013], seismic activity analysis [Lin et al, 2015;Chelidze and Matcharashvili, 2015], chemistry [Alves et al, 2017;Facchini et al, 2009], and were especially demanded in climate research [Deng et al, 2017;Feng and Dijkstra, 2017;García-Olivares and Herrero, 2013;Panagoulia and Vlahogianni, 2018].…”

Section: Introductionmentioning

confidence: 99%

Recurrence plot structures reflect motor-related EEG pattern

Pitsik

Frolov

2019

CAP

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Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

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Dynamical Patterns in Seismology

Cited by 14 publications

References 84 publications

Complexity in Geophysical Time Series of Strain/Fracture at Laboratory and Large Dam Scales: Review

Complexity in Geophysical Time Series of Strain/Fracture at Laboratory and Large Dam Scales: Review

Earthquake Forecast as a Machine Learning Problem for Imbalanced Datasets: Example of Georgia, Caucasus

Recurrence plot structures reflect motor-related EEG pattern

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