1 / 95

2. Probabilistic Mineral Resource Potential Mapping

This document explores the utilization of Geographic Information Systems (GIS) for mapping the potential of mineral resources through probabilistic methods. It emphasizes the role of Weights-of-Evidence modeling and logistic regression in estimating the probabilities of various types of mineral deposits. Key formulas like Bayes' Rule and logistics are detailed to demonstrate how uncertainties, including those from missing data, affect mineral resource estimates. An example from the Meguma Terrain illustrates the application of these techniques in real-world scenarios.

lilac
Télécharger la présentation

2. Probabilistic Mineral Resource Potential Mapping

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 2. Probabilistic Mineral Resource Potential Mapping • The processing of geo-scientific information for the purpose of estimating probabilities of occurrence for various types of mineral deposits was made easier when Geographic Information Systems became available. Weights-of-Evidence modeling and logistic regression are examples of GIS implementations.

  2. Weights of Evidence (WofE)

  3. BAYES’ RULE P(D on A) = P(D and A)/P(A) P(A on D) = P(A and D)/P(D) P(D on A) = P(A on D) * P(D)/P(A)

  4. ODDS & LOGITS O = P/(1-P); P = O/(1+O); logit = ln O ln O(D on A) = W+(A) + ln O(D) W+(A) = ln {P(A on D)/P(A not on D)}

  5. VARIANCE OF WEIGHT s2 = n-1(A and D) + n-1(A and not D)

  6. Negative Weight & Contrast W-(A) = W+(not A) Contrast: C = W+(A) - W-(A)

  7. PRESENT, ABSENT or MISSING add W+, W- or 0 to prior logit

  8. TWO or MORE LAYERS Add Weight(s) assuming Conditional Independence P(<A and B> on D) = P(A on D) * P(B on D)

  9. UNCERTAINTY DUE TO MISSING DATA P(D) = EX{P(D on X)} = S P(D on Ai) * P(Ai) or S P(D on <Ai and Bk>) * P(<Ai and Bk>) etc.

  10. VARIANCE (MISSING DATA) s2{P(D)} = S {P(D on Ai) - P(D)}2 * P(Ai) or S {P(D on <Ai and Bk>) - P(D)}2 * P(<Ai and Bk>) etc.

  11. TOTAL UNCERTAINTY Var (Posterior Logit) = Var (Prior Logit) + + S Var (Weights) + Var (Missing Data)

  12. Uncertainty inLogits and Probabilities D {Logit (P)} = 1/P(1-P) s(P) ~ P(1-P) s{Logit (P)}

  13. Meguma Terrain Example

  14. Table 1. Number of gold deposits, area in km2, weights, contrast (C) with standard deviations (s). In total: 68 deposits on 2945 km2

  15. Logistic Regression Logit (i) = b0 + xi1b1 + xi2b2 + … + xim bm

  16. Newton-Raphson Iteration b(t+1) = b(t) + {XTV(t)X)}-1XTr(t), t = 1, 2, … r(t) = y(t) - p(t)

  17. Seafloor Example

More Related