Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Glynn, Chagnon; Konstantinou, K. I.

doi:10.1038/srep37733

Cited by 21 publications

(39 citation statements)

References 36 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests a relationship between the permutation entropy and the spectral content. Such behavior was also evidenced in Glynn and Konstantinou (2016) when the authors linked low values of permutation entropy with the lack of energy at high frequencies. Figure 4c and d are related to the average dominant polarization degree of the events.…”

Section: Characterization Of Long-duration Eventsmentioning

confidence: 72%

“…After several tests, we found that different values of n and τ modify the magnitude of the h, but not its temporal evolution (see Additional file 3). We set n = 5 and τ = 1 , as suggested also by Glynn and Konstantinou (2016), because this combination provides high variability between stochastic and deterministic behaviors, being suitable for tracking permutation entropy trends.…”

Section: Permutation Entropymentioning

confidence: 99%

See 1 more Smart Citation

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Melchor

Almendros

Carniel

et al. 2020

Earth Planets Space

View full text Add to dashboard Cite

Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the long-term dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the SNR is low.

show abstract

Section: Characterization Of Long-duration Eventsmentioning

confidence: 72%

Section: Permutation Entropymentioning

confidence: 99%

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Melchor

Almendros

Carniel

et al. 2020

Earth Planets Space

View full text Add to dashboard Cite

show abstract

“… SEISMIC. Seismic data have also been successfully analysed using PE [ 41 ], and these time series are a very promising field of research using PE. The data included in this paper was drawn from the Seismic data database, US Geological Survey Earthquake Hazards Program [ 42 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

View full text Add to dashboard Cite

Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

show abstract

“…Seismology and economics are emergent fields of PE utilisation. The work in [ 10 ] used noise randomness assessed by PE to quantify its changes as a possible precursor of volcanic activity. An example of PE used in economics is [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities

Cuesta–Frau

2020

Entropy

View full text Add to dashboard Cite

Despite its widely tested and proven usefulness, there is still room for improvement in the basic permutation entropy (PE) algorithm, as several subsequent studies have demonstrated in recent years. Some of these new methods try to address the well-known PE weaknesses, such as its focus only on ordinal and not on amplitude information, and the possible detrimental impact of equal values found in subsequences. Other new methods address less specific weaknesses, such as the PE results’ dependence on input parameter values, a common problem found in many entropy calculation methods. The lack of discriminating power among classes in some cases is also a generic problem when entropy measures are used for data series classification. This last problem is the one specifically addressed in the present study. Toward that purpose, the classification performance of the standard PE method was first assessed by conducting several time series classification tests over a varied and diverse set of data. Then, this performance was reassessed using a new Shannon Entropy normalisation scheme proposed in this paper: divide the relative frequencies in PE by the number of different ordinal patterns actually found in the time series, instead of by the theoretically expected number. According to the classification accuracy obtained, this last approach exhibited a higher class discriminating power. It was capable of finding significant differences in six out of seven experimental datasets—whereas the standard PE method only did in four—and it also had better classification accuracy. It can be concluded that using the additional information provided by the number of forbidden/found patterns, it is possible to achieve a higher discriminating power than using the classical PE normalisation method. The resulting algorithm is also very similar to that of PE and very easy to implement.

show abstract

Reduction of randomness in seismic noise as a short-term precursor to a volcanic eruption

Cited by 21 publications

References 36 publications

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities

Contact Info

Product

Resources

About