Shared Chronologies: developing tools to improve age–depth modeling by
incorporating common event layers among several sedimentary records
- Rodrigo Vega,
- Daniel Melnick
Daniel Melnick
Instituto de Ciencias de la Tierra, Universidad Austral de Chile
Author ProfileAbstract
Sedimentary records provide an invaluable background for understanding
of complex phenomena that vary within multiple spatio--temporal scales,
such as climate and the seismic cycle. Understanding the latter in
southern Chile has yielded motivation to develop new tools to deal with
such records, in order to build a comprehensive peleoseismic catalog
from them. The region inherited an extensive chain of lakes from the
pleistocene glaciations, and a strong tephrochronological framework has
been developed during the last two decades. Lake deposits have been
extensively studied and shown to contain an incredibly sensitive
paleoseismic record in the form of lacustrine turbidites. The task is
thus to build the best possible chronology making use of all available
data. Age--depth modeling is now routinely done by means of bayesian
techniques, by using a sedimentation model as prior information and a
set of age determinations as data. This approach provides the best
results for any single record, but not necessarily for a set of records
taken together. This is the goal of the shared chronologies approach, to
build the tools for estimating the best chronologies for a set of
sedimentary records given some chronological data for each and a set of
shared events or stratigraphic markers. We use for this purpose the fact
that two or more of such layers should yield age differences close to
zero, within the general age uncertainty. This fact is incorporated to
the model as prior information, along with the sedimentation model. The
idea is clearly usable in a wide range of contexts, and for this reason
we would like to share the implementation in a very early stage of
development in order to incorporate feedback into design decisions that
could affect extensibility and modularity, and to forge collaboration.
This contribution shares an early experiment against a simulated data
set, as well as the current R implementation and future plans.