On the singular values decoupling in the Singular Spectrum Analysis of volcanic tremor at Stromboli

Carniel, Roberto; Barazza, Fausto; Tárraga, Marta; Ortiz, R.

doi:10.5194/nhess-6-903-2006

Cited by 18 publications

(11 citation statements)

References 26 publications

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“…Generally, the first two steps are considered as the decomposition stage and the final two steps the reconstruction stage. 30–32 SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components, and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series.…”

Section: Discussion On the Existence Of Long-period Seismic Waves Frmentioning

confidence: 99%

Extracting ground motion characteristics of distant earthquakes for mitigating displacement-sensitive equipment

Huang

Chen

Loh

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

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Ground motions induced by strong, distant earthquakes may contain extremely long-period seismic waves that have a dominant period of up to 10 s. In general, these long-period seismic waves have small amplitudes and do not endanger the safety of building structures and civil infrastructures. However, they may bring unexpected shutdown of some vibration (displacement)-sensitive equipment (such as the wafer scanners in high-tech fabs), which can cause production loss. In this study, seismic waveforms collected from broadband seismometers distributed in Taiwan were used to investigate the ground motion characteristics of the collected distant earthquakes (with an epicenter distance of over 1000 km). The time-variant dominant frequency was extracted using moving window wavelet packet transform to monitor significant long-period seismic waves from the preevent data of each seismic event. The slope of the Arias intensity and the slope index of the recorded seismic waves were also developed to detect the potential accumulation of vibration energy increasing with respect to time and amplitude. The proposed index was used to detect the features of significant distant earthquakes, and it provides a mechanism to prevent unexpected shutdown of displacementsensitive equipment. Finally, the proposed approach is discussed in relation to the damage severity of high-tech fabs.

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Section: Discussion On the Existence Of Long-period Seismic Waves Frmentioning

confidence: 99%

Extracting ground motion characteristics of distant earthquakes for mitigating displacement-sensitive equipment

Huang

Chen

Loh

et al. 2018

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

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“…Factorization (NMF) [43], Singular Value Decomposition [44], Principal Component Analysis (PCA) [45] and Auto-encoders [46]. Linear Discriminant Analysis (LDA) [47] uses the training samples to estimate the between-class and within-class scatter matrices, and then employs the Fisher criterion to obtain the projection matrix for feature extraction (or feature reduction).…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning in Volcanology: A Review

Carniel¹,

Guzmán²

2021

Updates in Volcanology - Transdisciplinary Nature of Volcano Science

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A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.

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“…In the present work, the ratio of two singular values, quantifying the coupling level between two singular values in RSSA, is utilized as a secondary DSF. The use of this quantity as a possible indicator of instability was first adopted for monitoring volcanic activity (Carniel et al 2006). The principle behind the use of this marker is that structural damage or instability induces a low frequency component in the signal, creating a de-coupling between the two most significant PCs.…”

Section: Damage Detection Using Recursive Eigen Ratio Differencementioning

confidence: 99%

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

Bhowmik

Krishnan

Hazra

et al. 2018

Structural Health Monitoring

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A novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Singular Spectral Analysis (RSSA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed in this paper. The acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its original state to contiguous linear /non-linear-states indicating damage. Most work to date deal with algorithms that require windowing of the gathered data that render them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, that motivates the development of the present framework. The response from a single channel is provided as input to the algorithm in real time. The RSSA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Lower order TVAR models are applied on the transformed responses to improve detectability. Numerical simulations performed on a 5-dof nonlinear system and on an SDOF system modeled using a Duffing oscillator under white noise excitation data, with different levels of nonlinearity simulating the damage scenarios, demonstrate the robustness of the proposed algorithm. The method, further validated on results obtained from experiments performed on a cantilever beam subjected to earthquake excitation; a toy cart experiment model with springs attached to either side; demonstrate the efficacy of the proposed methodology as an appropriate candidate for real time, reference free structural health monitoring.

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On the singular values decoupling in the Singular Spectrum Analysis of volcanic tremor at Stromboli

Cited by 18 publications

References 26 publications

Extracting ground motion characteristics of distant earthquakes for mitigating displacement-sensitive equipment

Extracting ground motion characteristics of distant earthquakes for mitigating displacement-sensitive equipment

Machine Learning in Volcanology: A Review

Real-time unified single- and multi-channel structural damage detection using recursive singular spectrum analysis

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