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dc.contributor.authorDiako Hariri Naghadehen_US
dc.contributor.authorChristopher Keith Morleyen_US
dc.date.accessioned2018-09-05T02:59:11Z-
dc.date.available2018-09-05T02:59:11Z-
dc.date.issued2016-06-01en_US
dc.identifier.issn14209136en_US
dc.identifier.issn00334553en_US
dc.identifier.other2-s2.0-84975316734en_US
dc.identifier.other10.1007/s00024-016-1238-7en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84975316734&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55643-
dc.description.abstract© 2016, Springer International Publishing. Extraction of propagation wavelet phase from seismic data can be conducted using first, second, third and fourth-order statistics. Three new methods are introduced, which are: (1) Combination of different moments, (2) Windowed continuous wavelet transform and (3) Maximum correlation with cosine function. To compare different methods synthetic data with and without noise were chosen. Results show that first, second and third order statistics are not able to preserve wavelet phase. Kurtosis can preserve propagation wavelet phase but signal-to-noise ratio can affect the extracted phase using this method. So for data set with low signal-to-noise ratio, it will be unstable. Using a combination of different moments to extract the phase is more robust than applying kurtosis. The improvement occurs because zero phase wavelets with reverse polarities have equal maximum kurtosis values hence the correct wavelet polarity cannot be identified. Zero-phase wavelets with reverse polarities have minimum and maximum values for a combination of different-moments method. These properties enable the technique to handle a finite data segment and to choose the correct wavelet polarity. Also, the existence of different moments can decrease sensitivity to outliers. A windowed continuous wavelet transform is more sensitive to signal-to-noise ratio than the combination of different-moments method, also if the scale for the wavelet is incorrect it will encounter with more problems to extract phase. When the effects of frequency bandwidth, signal-to-noise ratio and analyzing window length are considered, the results of extracting phase information from data without and with noise demonstrate that combination of different-moments is superior to the other methods introduced here.en_US
dc.subjectEarth and Planetary Sciencesen_US
dc.titleSource Wavelet Phase Extractionen_US
dc.typeJournalen_US
article.title.sourcetitlePure and Applied Geophysicsen_US
article.volume173en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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