Estimation of arrival times from seismic waves: a manifold-based approach*

Taylor, Kye; Procopio, Michael J.; Young, Christopher J.; Meyer, François G.

doi:10.1111/j.1365-246x.2011.04947.x

Cited by 23 publications

(15 citation statements)

References 70 publications

(90 reference statements)

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“…Hafez et al [18] proposed a method based on maximal-overlap discrete wavelet transform to determine P wave arrival time. In addition, other methods such as shortand long-term average rate (STA/LTA) and improved energy ratio [19,20], wave polarization [21,22], artificial neural network [23][24][25], wavelet-based methods [1,14,18,26], spectrogram-based methods [27,28], autoregressive techniques [29], local-maxima distribution [30], higher order statistics [31,32], and manifold-based approach [33] have also been proposed for determining the arrival time of these waves. Aboamer et al [46] proposed a linear modelbased estimation method for blood pressure and cardiac output for normal and paranoid cases.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Kırbaş

Peker

2016

Neural Comput & Applic

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Determining P and S wave arrival times while minimizing noise is a major problem in seismic signal analysis. Precise determination of earthquake onset arrival timing, determination of earthquake magnitude, and calculation of other parameters that can be used to make more accurate seismic maps are possible with the detection of these waves. Experts try to determine these waves by manual analysis. But this process is time-consuming and painful. In this study, a new method that enables the determination of P and S wave arrival times in noisy recordings is recommended. This method is based on the hybrid usage of empirical mode decomposition and Teager-Kaiser energy operator algorithms. The results show that the proposed system gives effective results in the automatic detection of P and S wave arrival times. Promisingly, the recommended system might serve as a novel and powerful candidate for the effective detection of P and S wave arrival time.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Kırbaş

Peker

2016

Neural Comput & Applic

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“…Signal A is a chirp, signal B is a row of the image Lenna (shown in Fig. 2-D), and signal C is a seismogram [41].…”

Section: Examples Of Signals and Imagesmentioning

confidence: 99%

“…These metrics can be used to efficiently organize patches in a manner that reveals the local behavior of the associated signal or image. In our previous work [36,41], we have noticed that metrics based on a diffusion, or a random walk concentrate patches that contain rapid changes in the signal or image data. These patches contain changes associated with singularities (edges), rapid changes in frequency (textures, oscillations), or energetic transients contained in the underlying function.…”

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confidence: 99%

A Random Walk on Image Patches

Taylor¹,

Meyer²

2012

SIAM J. Imaging Sci.

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Abstract. In this paper we address the problem of understanding the success of algorithms that organize patches according to graph-based metrics. Algorithms that analyze patches extracted from images or time series have led to state-of-the art techniques for classification, denoising, and the study of nonlinear dynamics. The main contribution of this work is to provide a theoretical explanation for the above experimental observations. Our approach relies on a detailed analysis of the commute time metric on prototypical graph models that epitomize the geometry observed in general patch graphs. We prove that a parametrization of the graph based on commute times shrinks the mutual distances between patches that correspond to rapid local changes in the signal, while the distances between patches that correspond to slow local changes expand. In effect, our results explain why the parametrization of the set of patches based on the eigenfunctions of the Laplacian can concentrate patches that correspond to rapid local changes, which would otherwise be shattered in the space of patches. While our results are based on a large sample analysis, numerical experimentations on synthetic and real data indicate that the results hold for datasets that are very small in practice.

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“…The fusion framework is based on a recent work by [28], [29]. The study extends Diffusion Maps (DM) [10], which has been successfully applied for phase classification [24], for estimation of arrival times [30] and for events discrimination [31]. Other constructions for fusing kernels were proposed in [32], [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

Lindenbaum

Bregman²,

Rabin

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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The problem of learning from seismic recordings has been studied for years. There is a growing interest of developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability have a reliable identification of manmade explosions. The availability of multiple high dimensional observations has increased the use of machine learning techniques in a variety of fields. In this work, we propose to use a kernel-fusion based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and in Jordan. The method achieves promising results in classification of event type as well as in estimating the location of the event. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.

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Estimation of arrival times from seismic waves: a manifold-based approach*

Cited by 23 publications

References 70 publications

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

RETRACTED ARTICLE: Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

A Random Walk on Image Patches

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

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