Electrical Engineering and Computer Sciences Department
As our problems become too complex to rely only on one discipline and as we find ourselves at the midst of information explosion multi-disciplinary analysis methods and data mining approaches in the Earth Sciences field become more of a necessity than professional curiosity. To tackle difficult problems ahead of us, we need to bring down the walls we have built around traditional disciplines and embark on true muti-disciplinary solutions. Our data, methodologies and workflow will have to cut across different disciplines. As a result, today's "integration" which is based on integration of results will have to give way to a new form of integration, that is, discipline integration. In addition, to solve our complex problems we need to go beyond standard mathematical techniques. Instead, we need to complement the conventional analysis methods with a number of emerging methodologies and soft computing techniques such as Expert Systems, Artificial Intelligence, Neural Network, Fuzzy Logic, Genetic Algorithm, Probabilistic Reasoning, and Parallel Processing techniques. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, and partial truth. Soft Computing is also tractable, robust, efficient and inexpensive. Soft computing aims to exploit such a tolerance for solving practical problems. While some individual methodologies have gained much popularity during the past years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation. Future research should focus on the integration of data and disciplinary knowledge for improving our understanding of reservoir data and reducing our prediction uncertainty.
Broadly, Earth Sciences subsumes but is not limited to the following areas;
3. Geophysics (Seismology and electromagnetic)
4. Borehole wireline log evaluation
6. Reservoir Engineering
7. Mineral Prospecting
8. Environment Risk Assessment
9. Nuclear Waste Storage Risk Assessment
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Fuzzy Set: 1965 … Fuzzy Logic: 1973 … BISC: 1990 … Human-Machine Perception: 2000 - …