The Tau Model for Data Redundancy and Information Combination in Earth Sciences: Theory and Application |
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| Authors: | Sunderrajan Krishnan |
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| Institution: | (1) Centre for Action, Research and Education for Water (CAREWATER), Elecon premises, V. V. Nagar, Anand district, Gujarat, 388120, India |
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| Abstract: | Many decision-making processes in the Earth sciences require the combination of multiple data originating from diverse sources.
These data are often indirect and uncertain, and their combination would call for a probabilistic approach. These data are
also partially redundant with each other or with all others taken jointly. This overlap in information arises due to a variety
of reasons—because the data arises from the same geology, because they originate from the same location or the same measurement
device, etc. The proposed tau model combines partially redundant data, each taking the form of a prior probability for the
event being assessed to occur given that single datum. The parameters of that tau model measure the additional contribution
brought by any single datum over that of all previously considered data; they are data sequence-dependent and also data value-dependent.
Data redundancy depends on the sequence in which the data is considered and also on the data values themselves. However, for
a given sequence, averaging the tau model parameters over all possible data values leads to exact analytical expressions and
corresponding approximations and inference avenues. Information on multiple-point connectivity of permeability arrives from
core data, well-test data and seismic data which are defined over varying supports with complex redundancy between these information
sources. In order to compute these tau weights for determining connectivity, one needs a model of data redundancy, here expressed
as a vectorial training image (Ti) constructed using a prior conceptual knowledge of geology and the physics of data measurement.
From such a vectorial Ti, the tau weights can be computed exactly. Neglecting data redundancy leads to an over-compounding
of individual data information and the possible risk of making extreme decisions. |
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| Keywords: | Information redundancy Data integration Tau model |
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