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2.2. PROSPECTOR

Duda et al. 1978
SRI International

The work behind PROSPECTOR was very much inspired by the encouraging results reported from the MYCIN effort (Shortliffe 76). The medical type of diagnosis that MYCIN was able to demonstrate lent itself very well to that of analyzing rocks and minerals. PROSPECTOR was patterned according to this type of schemata, but pioneered some important new aspects on its own.

PROSPECTOR was built to help geologists in exploring for hard-rock mineral deposits (Duda 78; Gaschnig 81; Duda 81). To achieve that, the developers created a knowledge model along the same lines as that of MYCIN. With the aid of this model, the system should be able to emulate the reasoning process of an experienced exploration geologist in asserting the likelihood that a given prospect area could contain the type of mineral that the geologist would desire. The user of PROSPECTOR would obtain some promising field data and then consult the system for assessing these data. Based on the input given, the system would infer the presence of minerals such as sulfides, uranium, lead, zinc, copper, nickel, and others.

A novel feature of PROSPECTOR at the time was its ability to accept rock data and information on minerals found together with other observations that the user volunteered. In fact, part of the input came from a digitized map. After the first match on this input, the system would possibly request additional information in a standard question-driven manner.

Part of the output could be given in the form of color-coded graphical displays that showed the favorbility of each cell with respect to prospect drilling.

PROSPECTOR also demonstrated capabilities of explaining its conclusions and actions. When prompted for data, the user could enter WHY, thus initiating a process whereby the system produced a simple geological rationale for its query. In order to achieve this, the system would trace its rule network and produce an answer. In this way it reflected the causal structure between the rules themselves.

The system was built up around the backward driven rule schemata that became very popular during the early 1980s (Duda 81). An example rule would typically be of the form:


TABLE 1
A List of Principal Systems on Geology and Mineralogy
 
Year
reported
Name of system Domain knowledge Technology applied Reference
 
1978 PROSPECTOR Minerals, prospecting Production rules, Bayesian inferencing (Gaschnig 81)
1981 DIPMETER ADVISOR Log interpretation Production rules, goal-directed programming, and more (Davis 81)
1982 ELAS Log interpretation Production rules
1982 LITHO Interpretation of oil-well drilling logs Production rules/EMYCIN (Bonnet 83)
1982 PHOENIX Oil well log interpretation/knowledge engineering Models and production rules (Barstow 82)
1982 Amoco/X-ray Mineralogy Production rules, various inferencing techniques (Ennis 82)
1983 ANALOG Petroleum geology Production rules (Hawkins 83)
1984 IKBM Evaluate petrophysical formations Production rules, explicit geological models (ES 84)
1987 MuPETROL Basin classification Production rules, explicit geological models
1987 SPECTRUM Remote sensing/geology Mixed-initiative, agents (Borchardt 87)
1987 META/LOG Log interpretation and resource estimation Hybrid approach, production rules, blackboard (SPECTRUM 87)
1988 EXPERTEST Reservoir and formation modeling Production-rules
1988 XX Identification, modeling, analysis of hydrocarbon plays and prospects Suite of many knowledge modules, production rules, approximate reasoning (Kendall 88)
1990 Contouring Assistant Intelligent front-end and advisor to a complex contouring and gridding system for geological data Object-oriented technology, production rules, hybrid system, interface to numerical models (Sines 96)
1990 Baldwin et al. Log interpretation/dentification of minerals and lithofacies Neural networks/Hyper Cube (Baldwin 90)
1991 GeoX Analysis of hydrocarbon plays, resource estimation Decision support, object-oriented technology, production rules, structured justification (Stabell 90)
1992 I2SAdvisor Remote sensing for geological exploration Planning, decision-support, case-based planning (Bremdal 95)
1992 PLAYMAKER Characterization of hydrocarbon plays Production rules, knowledge discovery techniques
1993 Sismonaute Detecting and interpreting wave fronts in seismic simulations Model-based reasoning (Junker 95)
1993 GeCoS 3D Building 3D models from geoloogical cross-sections Assumption-based truth maintenance, model-based reasoning, constraints (Hamburger 95)
1993 Kemme's E&P Subsurface modelling Business models, object-oriented methods, constraints (Kemme 95)
1993 Urwongse Prediciting hydrocarbon potential DBMS, fuzzy analogs, neural networks (Urwongse 95)
1993 MatchMod Interpretation of X-ray diffraction patterns Genetic algorithms (Schuette 95)
1993 Maceral analysis Automatic analysis of coal macerals Production rules, hybdrids, image analysis (Catalina 94)
1995 Baygun et al. Geological modeling of a mature hydrocarbon reservoir Neural networks, fuzzy classification (Baygun 96)
1995 Hatton et al. Crude oil fingerprinting Self-organizing neural networks (Hatton 96)
1995 Abel et al. Petrographic analysis Case-based reasoning (Abel 96)

IF: There is hornblende pervasively altered to biolite
THEN: There is strong evidence (320,0.001) for potassic zone alteration


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