# Integrated Processing and Imaging of Exploration Data: An Application of Fuzzy Logic

## Abstract

Recently, Earth observation tools and geological exploration techniques have been developing rapidly, alongside significant advancements in computer technology and data processing capabilities. These developments encourage integrated multi-sensor, and multi-target observation of the Earth System environment, resulting in massive volumes of Earth science and geological exploration data. A number of new approaches for handling such large volumes of data have been proposed, including those based on classical Bayesian statistics and probability theory. However, fuzzy logic approximation of the spatial information (Earth science and geological exploration data) has been quick and effective for efficient and accurate analysis of complex geological exploration data and associated Earth system information. For more complex spatial reasoning, a combined single-stage fuzzy neural network approach has also been successfully applied, with future prospects of cascaded multi-stage fuzzy neural network systems. This chapter reviews some of the recent developments in the theoretical aspects of fuzzy logic and neural network models in Earth System science and geological application problems.

## Keywords

Membership Function Fuzzy Logic Geographic Information System Exploration Data Fuzzy Neural Network## Preview

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## References

- An, P., 1992, Spatial reasoning and integration techniques for geophysical and geological exploration data: Unpublished Ph.D. thesis, Univ. of Manitoba, Canada.Google Scholar
- An, P., Moon, W. M., and Rencz, A., 1991, Integration of geological, geophysical and remote sensing data using fuzzy logic: J. Can. Soc. Expl. Geophys.,
**27**, 1–11.Google Scholar - An, P., Moon, W. M., and Bonham-Carter, G. F., 1994, Uncertainty management of exploration data using the belief function. Nonrenewable Resources,
**3**, 60–71.CrossRefGoogle Scholar - Benediktsson, J. A., Ingimundarson, J. I., and Sveinsson, J. R., 1997, Classification and feature extraction of hyperdimensional data using LOOC covariance estimation: IGARSS’97 Proceedings, B10. 03.Google Scholar
- Bonham-Carter, G. F., 1994, Geographic information systems for geoscientists: Pergamon Press.Google Scholar
- Burrough, P. A., 1986, Principles of geographic information systems for land resources assessment: Clarendon Press.Google Scholar
- Duan, J. C., and Chung, F. L., 2001, Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning, IEEE Trans. Fuzzy Systems,
**9**, 293–306.Google Scholar - Goguen, J. A., 1967, L-fuzzy sets: J. Math. A. A.,
**18**, 145–174.CrossRefGoogle Scholar - Goguen, J. A., 1969, The logic of inexact concepts: Synthese,
**19**, 325–373.CrossRefGoogle Scholar - Hauptmann, W., and Heesche, K., 1995, A neural net topology for bidirectional fuzzy-neuro transformation: Proc. IEEE Internat. Conf. on Fuzzy Systems, vol. I II, 1511–1518.Google Scholar
- Ishibuchi, H., Morisawa, T., and Nakashima, T., 1996, Voting schemes for fuzzy rule-based classification systems: Proc. IEEE Internat. Conf. on Fuzzy Systems, vol. I, 614–620.Google Scholar
- Iyanaga, S., and Kawada, Y., Eds., 1980, Encyclopedic dictionary of mathematics: MIT Press, 1167–1170.Google Scholar
- Jiang, W. W., Moon, W. M., and Feng, L.N., 1997, Data fusion research for mineral exploration data from Hwanggang-ri region, Korea: Technical Report # 17, Dept. of Earth Sciences, University of Manitoba, Winnipeg, Canada, 1–62.Google Scholar
- Klement, E. P., 1980, Fuzzy measures by classical measures,
*in*Wang, P. P., and Chang, S. K., Eds., Fuzzy sets: 25–33.CrossRefGoogle Scholar - Mathai, A. M., and Rathie, P. N., 1975, Information theory and statistics: John Wiley and Sons, 1–137.Google Scholar
- Moon, W. M., 1990, Integration of multiple sets of geophysical information using evidential belief function: IEEE Trans. Geosci. Remote Sensing,
**28**, 272–278.Google Scholar - Moon, W. M., 1993, Mathematical basis for geophysical information representation and integration: Can. J. Remote Sensing,
**19**, 63–67.Google Scholar - Moon, W. M., 1995, Information representation and integration of multiple sets of spatial geoscience data: IGARSS’95 Proceedings.Google Scholar
- Moon, W. M., and Jiang, W. W., 1995, Integrated exploration of base metal deposits in East Java, Indonesia: Technical Report #14, The University of Manitoba, Canada.Google Scholar
- Moon, W. M., Jiang, W. W., Yun, S. G., and So, C. S., 2001, Data fusion modeling of the Hwang-Gang-Ri mineralization zone: in preparation.Google Scholar
- Pawlak, Z., 1996, Why rough sets ?: Proc. IEEE Int. Conf on Fuzzy Systems, vol.
**2**, 738–743.Google Scholar - Pedrycz, W, Bezdek, J. C., Hathway, R. J., and Rogers, G. W., 1998, Two non-parametric models for fusing heterogeneous fuzzy data, IEEE Trans. Fuzzy Systems, 6, 411–424.Google Scholar
- Rudas, I.J., and Kaynak, M. Okay, 1998, Entropy-based operations on fuzzy sets, IEEE Trans. Fuzzy systems,
**6**, 33–40.Google Scholar - Shannon, C. E., 1948, A mathematical theory of communication: Bell System Tech. J.,
**27**, 379–423, 623–656.Google Scholar - Takagi, H., and Hayashi, I., 1991, NN-driven fuzzy reasoning: J. Approx. Reasoning, 5, 191–212.Google Scholar
- Wang, P. P., and Chang, S. K., Eds., 1980, Fuzzy sets: Plenum Press.Google Scholar
- Yager, R. R., 1992, Implementing fuzzy logic controllers using a neural network framework: Fuzzy Sets and Systems,
**48**, 53–64.CrossRefGoogle Scholar - Yao, C. C., and Kuo, Y. H., 1996, A fuzzy neural network model with three-layered structure: Proc. IEEE Internat. Conf on Fuzzy Systems, Vol. III, 1503–1510.Google Scholar
- Zadeh, L. A., 1965, Fuzzy sets: Information and Control,
**8**, 338–353.CrossRefGoogle Scholar - Zadeh, L. A., 1968, Probability measures of fuzzy events: J. Math. Anal. Appl.,
**23**, 421–427.Google Scholar - Zadeh, L. A., 1987a, Fuzzy sets,
*in*Yager, R. R., Ovichinikov, S., Tong, R. M., and Nguyon, H. T., Eds., Fuzzy sets and applications: Selected papers by L.A. Zadeh: John Wiley, 29–44.Google Scholar - Zadeh, L. A., 1987b, Fuzzy sets as a basis for a theory of possibility,
*in*Yager, R. R., Ovichinikov, S., Tong, R. M., and Nguyon, H. T., Eds., Fuzzy sets and applications: Selected papers by L.A. Zadeh: John Wiley, 193–218.Google Scholar - Zadeh, L. A., 1987c, A theory of approximate reasoning,
*in*Yager, R. R., Ovichinikov, S., Tong, R. M., and Nguyon, H. T., Eds., Fuzzy sets and applications: Selected papers by L.A. Zadeh: John Wiley, 368–412.Google Scholar - Zadeh, L.A., 2001, Recognition technology and fuzzy logic, IEEE Trans. Fuzzy Systems,
**9**, 3–4.Google Scholar - Zimmermann, H J, 1984, Fuzzy set theory — and its applications: Kluwer-Nijhoff Publishing.Google Scholar
- Zimmermann, H J, and Zysno, P., 1980, Latent connectives in human decision making: Fuzzy Sets and Systems,
**4**, 37–51.CrossRefGoogle Scholar