2006
DOI: 10.1111/j.1365-246x.2006.03023.x
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Characterization of seismic waveforms and classification of seismic events using chirplet atomic decomposition. Example from the Lacq gas field (Western Pyrenees, France)

Abstract: S U M M A R YIn the present paper, we present a generalization of the wavelet transform, known as chirplet transform, specially designed to quantify the morphological attributes of individual seismic sections (packets) constituting the seismic waveforms. The proposed transform relies on an atomic decomposition of individual seismograms based on local multiscale chirps (swept frequency wave packets) of various shape and duration. We developed an algorithm that provides an optimal representation of the waveform … Show more

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Cited by 28 publications
(35 citation statements)
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“…Joswig, 1990;Dowla et al, 1990). On the other hand, unsupervised pattern recognition may be used to generate an initial understanding of the unknown data properties without utilizing existing class or event labels as done for supervised learning (Bardainne et al, 2006;Köhler et al, 2009). Clustering is a well-known unsupervised learning method which describes the task to find a meaningful grouping of unlabeled data into respective categories (e.g.…”
Section: Methodsmentioning
confidence: 99%
“…Joswig, 1990;Dowla et al, 1990). On the other hand, unsupervised pattern recognition may be used to generate an initial understanding of the unknown data properties without utilizing existing class or event labels as done for supervised learning (Bardainne et al, 2006;Köhler et al, 2009). Clustering is a well-known unsupervised learning method which describes the task to find a meaningful grouping of unlabeled data into respective categories (e.g.…”
Section: Methodsmentioning
confidence: 99%
“…The signal was constructed using elementary chirplet wave packets. Such chirplet packets were proposed by Bardainne et al [2006] to decompose seismograms. Details of the construction are given in Appendix A.…”
Section: Emd Decomposition Of Synthetic Seismic Datamentioning
confidence: 99%
“…5 is based on the work of Bardainne et al [2006] and is constructed as follows: The parameter values used are shown in …”
Section: Noise Corruption Of Empirical Mode Decomposition 395mentioning
confidence: 99%
“…In order to avoid the limitations of well known clustering algorithms such as the hierarchical methods, the k -means, the fuzzy means, and the Expectation Maximization algorithm (Everitt et al, 2001, Bardaine et al, 2006, Becker et al, 2006, we propose a fast, sequential, graph based algorithm that exploits the structure of the similarity graph and produces a single cluster in each iteration. The proposed algorithm (Pikoulis et al, 2006) emphasizes on the quality of the produced clusters by introducing a suitable measure to evaluate the participation of each object to a cluster and by expressing the overall quality of the cluster as a function of the participations of the individuals that comprise it.…”
Section: Identification Of Event Clusters and Time Differencesmentioning
confidence: 99%