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| Titel |
New approaches for automated data processing of annually laminated sediments |
| VerfasserIn |
I. Rupf, G. Radons |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 11, no. 5/6 ; Nr. 11, no. 5/6 (2004-11-24), S.599-607 |
| Datensatznummer |
250008990
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| Publikation (Nr.) |
 copernicus.org/npg-11-599-2004.pdf |
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| Zusammenfassung |
| Laminated sediments, like evaporites and biogenic lake sediments, provide
high-resolution paleo-climate records. Yet detection and counting of laminae
causes still problems because sedimentary structures are often disturbed. In
the past laminated rocks often were analysed manually - a tedious and
subjective work. The present study describes four automated approaches for lamina detection
based on 1d grey-scale vectors. Best results are obtained with a newly
developed algorithm, called Adaptive Template Method (ATM) in combination
with the Hilbert transform. ATM improves the signal to noise ratio of the
grey-value signal. Its basic idea is to extract first a characteristic
waveform, the template, which describes the typical grey-value variation
transverse to the laminae. This is a kind of "template learning" process,
which in practice is done by an appropriate averaging method. This template
is in a second step used for processing the whole sample. One calculates the
overlap of the template with the actual signal, the grey-value variation
along the core, as function of position in core direction. This method
generates a new signal with maxima at positions, where the template
optimally matches the original signal. The new time-series is called
AT-transform. It is smoother than the initial data sequence. High frequency
noise and local trend effects are suppressed. Afterwards, the AT-transform
can be analysed with the Hilbert transformation for extracting phase
information. The data processing methods are tested both on artificial data sequences and
on a seasonally laminated sedimentary record of the Oligocene Baruth Maar
(Germany). Although ATM is no panacea for highly disturbed signals, our
comparison with other approaches shows that it provides the best results.
The combination of ATM and the Hilbert transform allows to detect clearly
long-term oscillations in the sedimentation patterns and thus cycles in
climatic variations. |
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