Statistical Models for Optimizing Mineral Exploration
After the spectacular successes of the 1960's and 1970's, the mineral exploration business is at a crossroads, facing uncertain t:imes in the decades ahead. This situation requires a re-thinking of the philosophy guiding mineral exploration if it is to emulate its recent performance. The m...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
1987, 1987
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| Edition: | 1st ed. 1987 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 3.3. Sequential testing of population commonality
- 3.4. Application of sequential testing procedure to assist the world-wide search for six types of ore deposits
- 3.5. Application of sequential testing to assist continent-wide search programs for six types of ore deposits
- Exercises
- 4 Probabilistic models for the optimal detection of ore deposits by airborne and ground exploration programs
- 4.1. General statement: detection of ore deposits based on Geometric Probability
- 4.2. Detection of randomly oriented ore deposits by airborne surveys on grids of various designs
- 4.3. Detection of randomly oriented ore deposits by ground surveys or vertical drilling on square grids
- 4.4. Detection of oriented ore deposits by airborne and ground surveys and drilling programs. Application of Dynamic Programming
- 4.5. Optimization of airborne and ground field surveys and drilling programs based on detection probability models
- 7 The Expert computerized system for optimizing mineral exploration
- Appendices
- A.1. List of names of ore deposits included in data base
- A.3. SEQPOOL computer programs
- A.4. OPTGRID computer programs
- A.5. FACTOR-TREND computer programs
- A.6. Linear Programming dual simplex computer program
- A.7. CLASSIFICATION computer programs
- A.8. Data lists for exercises
- A.9. Measurement conversion table and statistical tables
- A.10. Answers to selected exercises
- 1 Application of computerized geomathematical models to the optimization of exploration programs
- 1.1. General statement: optimization of the mineral exploration sequence
- 1.2. Types of geomathematical models used for optimizing mineral exploration
- 1.3. Construction of a data base for the optimized search for six types of ore deposits in five continents
- 2 Deterministic, heuristic and univariate stochastic models used for optimizing mineral exploration
- 2.1. Foreword: types of models used in the optimized ore deposit search
- 2.2. Deterministic models
- 2.3. Heuristic models
- 2.4. Univariate stochastic models
- 2.5. Modelling of economic & geometric parameters of ore deposits
- 2.6. Modelling of occurrence parameters of ore deposits
- Exercises
- 3 Statistical pooling of regions to assist the optimization of world-wide ore search programs
- 3.1. General statement: rationale of statistical pooling of regions
- 3.2. Testing of population commonality
- 4.6. Selection of ore deposit types and regions of search based upon the detectability criterion
- 4.7. Optimal selection of region-ore deposit types based on the discounted payoff criterion
- Exercises
- 5 Application of the General Linear Model and Operations Research models to the optimization of field exploration and development planning
- 5.1. Theoretical background for the GLM
- 5.2. Application of regression analysis to various mineral exploration problems
- 5.3. Application of trend factor and residual trend factor analyses to field exploration programs
- 5.4. Application of Operations Research models to development planning
- Exercises
- 6 Multivariate Bayesian classification models: Application to the optimal selection of prospecting areas and exploration targets
- 6.1. General statement
- 6.2. Optimal selection of prospecting areas and exploration targets based on control data
- 6.3. Optimal selection of exploration targets without control data
- Exercises