HR: 0800h
AN: NG41A-0424    [Abstracts]
TI: Identification of the Time Base in Environmental Archives
AU: * De Ridder, F
EM: federid@pop.vub.ac.be
AF: Vrije Universiteit Brussel Department of electricity and instrumentation Team B: System identification, Pleinlaan 2 , Brussels, 1050 Belgium
AU: De brauwere, A
EM: adebrauw@vub.ac.be
AF: Vrije Universiteit Brussel Department of analytical and environmental chemistry, Pleinlaan 2, 1050, Brussels Belgium
AU: Pintelon, R
EM: Rik.Pintelon@vub.ac.be
AF: Vrije Universiteit Brussel Department of electricity and instrumentation Team B: System identification, Pleinlaan 2 , Brussels, 1050 Belgium
AU: Schoukens, J
EM: johan.schoukens@vub.ac.be
AF: Vrije Universiteit Brussel Department of electricity and instrumentation Team B: System identification, Pleinlaan 2 , Brussels, 1050 Belgium
AU: Dehairs, F
EM: Frank.Dehairs@vub.ac.be
AF: Vrije Universiteit Brussel Department of analytical and environmental chemistry, Pleinlaan 2, 1050, Brussels Belgium
AB: One of the major problems with data-processing of proxy records (e.g. stable oxygen or carbon isotopes, or trace elements in shells, sponges, corals, sediment cores, etc.) is the dating of individual observations. All these proxy records are measured as function of a distance, while generally the time series are desired. Due to variations and differences in accretion rate, each record has its unique distance series, which cannot be compared with other records or models. Therefore, distance series are transformed into time series. However, this is only possible if additional information about the accretion rate is available. Unfortunately, this is mostly not the case and thus additional assumptions about the accretion rate are necessary. The most popular method to overcome this problem is the so-called anchor, tie or control point method, where the user assumes that the date of several observations is known. Next, the others are dated by a (linear) interpolation technique. Such methods are often used, especially when growth bands are available. We have proposed an alternative. Therefore, a parametric model for the signal is used, e.g. a periodic signal model. In addition, a new concept is introduced: the time base distortion. Therefore, we started from a previously estimated time base (if this is unknown, we initialize the time base assuming a constant accretion rate). Next, we allow this base to be distorted due to nonlinear accretion rates or hiatuses. This time base distortion can be identified in the frequency domain. When the accretion rate differs from the proposed one the spectral peaks, caused by the periodic component, are broadened and/or side peaks appear. From the latter, the distortion of the initial time base can be decoded, employing a phase demodulation. In order to refine this approach, an automated model selection procedure is employed to estimate how much variation in the time base and in the signal model is significant. The model selection procedure used is an adaptation of Akaike's information criterion, which can now handle small data sets. This results in a refined time base, where each individual observation is dated and where the stochastic disturbances are minimized. Several real world examples are processed to illustrate this methodology. First both methods are compared on the Mg-signal measured in a Saxidomus giganteus (shell) from Kenya. Here, this method was able to identify two hiatuses. Next, the oxygen stable isotope record measured in a coral is discussed. Here growth bands are present. These were used to date the record, using the anchor point method. Because several peaks appeared in its spectrum, the interpretation was not straightforward. The time base distortion approach is used to refine the initially estimated time base and finally one clear peak with a period of 14.4 years remained. This example shows that this method can be used to refine other time bases and that it can be used even when no annual periodicity is present in the signal. Finally, the temperature reconstruction derived from the Vostok ice core record is processed and discussed.
DE: 3344 Paleoclimatology
DE: 3200 MATHEMATICAL GEOPHYSICS (New field)
DE: 3210 Modeling
DE: 3260 Inverse theory
DE: 1035 Geochronology
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting