Wavelet analysis of the seismograms for tsunami warning

Chamoli, Ashutosh; Rani, V. Swaroopa; Srivastava, Kirti; Srinagesh, D.; Dimri, V. P.

doi:10.5194/npg-17-569-2010

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…The authors also calculated the wavelet packet variance and the wavelet correlation coefficients. Chamoli et al (2010) analysed the tsunami-generating and non-tsunamigenerating earthquakes recorded at different stations to identify the tsunami-genesis of an earthquake. The authors examined the frequency of the seismogram components in the time domain using wavelet transform.…”

Section: Datamentioning

confidence: 99%

Wavelet decomposition in the Earth's gravity field investigation

Bogusz¹

2013

Acta Geodynamica Et Geomaterialia

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This paper presents the results of the application of wavelet decomposition to processing data from the GGP sites (The Global Geodynamics Project). The GGP is an international project within which the Earth's gravity field changes are recorded with high accuracy at a number of stations worldwide using superconducting gravimeters. Data with a 5-second sampling interval from Wettzell and Bad Homburg were used for the research. The wavelet transform enables the investigation of the temporal changes of the oscillation amplitudes or the decomposition of the time series for the analysis of the required frequencies. The wavelet decomposition was performed using the regular orthogonal symmetric Meyer wavelet. The research concerned data from an earthquake period recorded at various locations and a quiet period when the gravimeters worked without any disturbances. The decomposition was followed by the Fast Fourier Transform for signal frequency components and then by correlation analyses of corresponding frequency components (for periods from 10 to 60 000 seconds) for all sensor combinations, for the quiet and the earthquake periods separately. Frequency components defining long term changes for all sensor combinations, as well as combinations between two sensors at the same site for the quiet days are characterised by high correlation coefficients. For the time of the earthquake, the Wettzell site data proved strong correlation for all frequency components, while the Bad Homburg site data showed an unexpected decrease of correlation for the majority of frequency components. The authors also showed that wavelet decomposition can be a good method of data interpolation, especially from the time of earthquakes. Moreover, it is a very useful tool for filtering the data and removing the noises.

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Section: Datamentioning

confidence: 99%

Wavelet decomposition in the Earth's gravity field investigation

Bogusz¹

2013

Acta Geodynamica Et Geomaterialia

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“…Wavelet methods have shown significant contributions in different fields: gravity and magnetic source characterization and source edge detection [3,[16][17][18], climatic signals [19], tsunami warning [20,21], and different time series analyses [22,23]. Shannon entropy is a measure of information of any distribution and has been found to be helpful for tsunami warnings [24,25], climatology and hydrology [26,27], and earthquake analysis [28,29] in past case studies.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data

Dwivedi

Chamoli

Rana

2023

Entropy

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Subsurface source boundary identification is a major step in the interpretation of potential field anomalies in geophysical exploration. We investigated the behavior of wavelet space entropy over the boundaries of 2D potential field source edges. We tested the robustness of the method for complex source geometries with distinct source parameters of prismatic bodies. We further validated the behavior with two datasets by delineating the edges of (i) the magnetic anomalies due to the popular Bishop model and (ii) the gravity anomalies of the Delhi fold belt region, India. The results showed prominent signatures for the geological boundaries. Our findings indicate sharp changes in the wavelet space entropy values corresponding to the source edges. The effectiveness of wavelet space entropy was compared with the established edge detection techniques. The findings can help with a variety of geophysical source characterization problems.

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“…The energy distribution is shifted at the boundary by half of the step-width using Walsh low pass sequenecy filtering method. Recently, several researchers have suggested that wavelet based algorithm could be an appropriate choice to handle the nonstationary part of the geophysical signal (Morlet et al 1982;Mallat 1989;Wickerhauser 1994;Kumar and Foufoula-Georgiou 1997;Oppenheim et al 1999;Boggess and Narcowich 2001;Frantziskonis and Denis 2003;Soliman et al 2003;Misti et al 2007;Chamoli et al 2007;Pan et al 2008;Chamoli et al 2010;Adamowski and Chan 2011;Chandrashekhar and Rao 2012;Perez-Munoz et al 2013). Researchers have invariably used wavelet transform to solve geophysical characterization problem.…”

Section: Introductionmentioning

confidence: 99%

Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

2016

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Identification of sharp and discontinuous lithological boundaries from well log signal stemming from heterogeneous subsurface structures assumes a special significance in geo-exploration studies. Well log data acquired from various geological settings generally display nonstationary/nonlinear characteristics with varying wavelengths and frequencies. Modelling of such complex well-log signals using the conventional signal processing techniques either fails to catch-up abrupt boundaries or at the best, do not provide precise information on insidious lithological discontinuities. In this paper, we have proposed a new wavelet transform-based algorithm to model the abrupt discontinuous changes from well log data by taking care of nonstationary characteristics of the signal. Prior to applying the algorithm on the geophysical well data, we analyzed the distribution of wavelet coefficients using synthetic signal generated by the first order nonstationary auto-regressive model and then applied the method on actual well log dataset obtained from the KTB bore hole, Germany. Besides identifying the formation of layered boundaries, the underlying method also maps some additional formation boundaries, which were hitherto undetected at the KTB site. The results match well with known geological lithostratigraphy and will be useful for constraining the future model of KTB bore hole data.

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Wavelet analysis of the seismograms for tsunami warning

Cited by 16 publications

References 28 publications

Wavelet decomposition in the Earth's gravity field investigation

Wavelet decomposition in the Earth's gravity field investigation

Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data

Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

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