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| Titel |
Multiplicative cascade processes and information integration for predictive mapping |
| VerfasserIn |
Q. Cheng |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 19, no. 1 ; Nr. 19, no. 1 (2012-01-11), S.57-68 |
| Datensatznummer |
250014165
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| Publikation (Nr.) |
 copernicus.org/npg-19-57-2012.pdf |
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| Zusammenfassung |
| This paper presents a new model proposed on the basis of
multiplicative cascade process (MCP) theory for integrating spatial
information to be used for mineral resources prediction and environmental
impact assessment. Probability of a spatial point event is defined as the
probability that a small map calculating unit (map unit) randomly selected
from a study area contains one or more points. The probability that such
unit randomly selected from a subarea with known spatial binary map patterns
(evidential layers) contains one or more points is defined as the posterior
point event probability. In this paper, processes of integrating multiple
binary map patterns that divide the study area into smaller areas with
updated posterior probabilities are viewed as multiplicative cascade
processes resulting in a new log-linear model for calculating conditional
probabilities from the multiple evidential input layers. The coefficients
(weights) involved in this model measuring degree of spatial correlation
between point event and the evidential layers are found to be associated
with singularity indices involved in multifractal modeling. It is
demonstrated that the model is simple and easy to be implemented in
comparison with the existing weights of evidence model which is commonly
applied in spatial decision modeling. In addition, the posterior probability
as the end product of a multiplicative cascade process can be used to
describe multifractality and singularity which are useful properties for
characterizing spatial distribution of predicted point events. A case study
of tin mineral potential mapping in the Gejiu mineral district in China is
used to illustrate principles and use of the modeling process. Four binary
layers: formation of limestone, buffer distance for intersections of three
groups of faults, local and regional geochemical anomalies of elements As,
Sn, Cu, Pb, Zn and Cd, were combined for mapping potential areas for
occurrence of tin mineral deposits. |
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