Model Fusion under Probabilistic and Interval Uncertainty, with Application to Earth Sciences

One of the most important studies of the earth sciences is that of the Earth's
interior structure. There are many sources of data for Earth tomography models:
first-arrival passive seismic data (from the actual earthquakes),
first-arrival active seismic data (from the seismic experiments),
gravity data, and surface waves. Currently, each of these datasets is
processed separately, resulting in several different Earth models that have
specific coverage areas, different spatial resolutions and varying degrees of
accuracy. These models often provide complimentary geophysical information on
earth structure (P and S wave velocity structure).

Combining the information
derived from each requires a joint inversion approach.
Designing such joint inversion techniques presents an important theoretical and
practical challenge. While such joint inversion methods are being developed, as
a first step, we propose a practical solution: to fuse the Earth
models coming from different datasets. Since these Earth models have different
areas of coverage, model fusion is especially important because some of the
resulting models provide better accuracy and/or spatial resolution in some
spatial areas and in some depths while other models provide a better accuracy
and/or spatial resolution in other areas or depths.

The models used in this paper contain measurements that have not only different
accuracy and coverage, but also different spatial resolution. We describe how to fuse such
models under interval and probabilistic uncertainty.

The resulting techniques can be used in other situations when we need to merge models of different accuracy and spatial resolution.

Jan 1 2010 (All day)
conference paper
interval uncertainty
data fusion
model fusion
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