International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
6. CONCLUSION
The subsurface engineering is truly three-dimensional and is
required a real 3D data model. For the purpose of describing
different subsurface objects, many 3D data models and data
structures have been investigated. Our experimental results
demonstrated that the proposed 3D data model based on QTPV
is feasible and efficient in modeling irregular geological objects
and regular subsurface engineering. The following conclusions
can be drawn:
(1) The developed 3D QTPV data model has the ability of
modeling regular and irregular 3D objects. Although it is a
volumetric model, but we can also get the surface model of
the modeled objects by designing a special algorithm. For
example, for the purpose of obtaining a stratigraphy
interface, we should seek all the triangles which have the
same positive-negative attribute while the attribute of
positive side and negative side are different for one triangle.
The QTPV data structure could not only overcome the strict
data restriction, i.e. the captured points should be located on
a regular 3D grid, but also overcome the disadvantages of
TEN, such as huge data volume, complex topological
relationship and modeling algorithm complex.
ro
—
—
UG
—
) The complex geological objects could be described by using
only QTPV data structure while hybrid data structure does
not need. Thus it is convenient for database management.
(4
—
When adding attribute structure on the vertices and
attaching digitized borehole log to the edges of QTPV, we
can get thc inner attribute of geological objects at any
position by using linear or finite element interpolation
methods. Thus the real 3D management of geological
bodies can be achieved.
Our applicable cases are only stratigraphy modeling according
to real borehole captured data and simulated laneway data. The
further works are taking into account for more complex
geological objects and consummate modeling, visualization and
model manipulation methods.
ACKNOWLEDGEMENTS
The research is financially supported by the Hong Kong
Polytechnic University ASD research fund under contract
number of No. 1.34.4222 and the Open Research Fund
Program of LIESMARS under the contract number of No. WKL
(01) 0302.
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