Data recording the spatial locations of gold deposits and associated geological features in the Murchison area of Western Australia. Extracted from a large scale (1:500,000) study of the Murchison area by the Geological Survey of Western Australia (Watkins and Hickman, 1990). The features recorded are
the locations of gold deposits;
the locations of geological faults;
the region that contains greenstone bedrock.
The study region is contained in a \(330\times 400\) kilometre rectangle. At this scale, gold deposits are points, i.e. their spatial extent is negligible. Gold deposits in this region occur only in greenstone bedrock. Geological faults can be observed reliably only within the same region. However, some faults have been extrapolated (by geological ``interpretation'') outside the greenstone boundary from information observed in the greenstone region.
Deposit locations were extracted from the Minedex database (Geological Survey of Western Australia, n.d.) and include deposits of all sizes. The fault geometry and greenstone boundaries were mapped and collated by Watkins and Hickman (1990).
These data were analysed by Foxall and Baddeley (2002) and Brown et al (2002); see also Groves et al (2000), Knox-Robinson and Groves (1997), Baddeley, Rubak and Turner (2015) and Baddeley (2019). The main aim is to predict the intensity of the point pattern of gold deposits from the more easily observable fault pattern.
data(murchison)
murchison
is a list with the following entries:
a point pattern (object of class "ppp"
)
representing the point pattern of gold deposits.
See ppp.object
for details of the format.
a line segment pattern (object of class "psp"
)
representing the geological faults.
See psp.object
for details of the format.
the greenstone bedrock region.
An object of class "owin"
. Consists of multiple
irregular polygons with holes.
All coordinates are given in metres.
Baddeley, A. (2018) A statistical commentary on mineral prospectivity analysis. In Daya Sagar, B.S., Cheng, Q. and Agterberg, F.P. (eds.) Handbook of Mathematical Geosciences: Fifty Years of IAMG. International Association for Mathematical Geosciences. Chapter 2, pages 25--65.
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.
Brown, W.M., Gedeon, T.D., Baddeley, A.J. and Groves, D.I. (2002) Bivariate J-function and other graphical statistical methods help select the best predictor variables as inputs for a neural network method of mineral prospectivity mapping. In U. Bayer, H. Burger and W. Skala (eds.) IAMG 2002: 8th Annual Conference of the International Association for Mathematical Geology, Volume 1, 2002. International Association of Mathematical Geology. Pages 257--268.
Foxall, R. and Baddeley, A. (2002) Nonparametric measures of association between a spatial point process and a random set, with geological applications. Applied Statistics 51, 165--182.
Geological Survey of Western Australia (n.d.) MINEDEX database of Mines and Mineral Deposits. https://www.dmp.wa.gov.au/Mines-and-mineral-deposits-1502.aspx.
Groves, D.I., Goldfarb, R.J., Knox-Robinson, C.M., Ojala, J., Gardoll, S, Yun, G.Y. and Holyland, P. (2000) Late-kinematic timing of orogenic gold deposits and significance for computer-based exploration techniques with emphasis on the Yilgarn Block, Western Australia. Ore Geology Reviews, 17, 1--38.
Knox-Robinson, C.M. and Groves, D.I. (1997) Gold prospectivity mapping using a geographic information system (GIS), with examples from the Yilgarn Block of Western Australia. Chronique de la Recherche Miniere 529, 127--138.
Watkins, K.P. and Hickman, A.H. (1990)
Geological evolution and mineralization of the Murchison Province,
Western Australia.
Bulletin 137, Geological Survey of Western Australia. 267 pages.
Published by Department of Mines, Western Australia, 1990.
Available online from Department of Industry and Resources,
State Government of Western Australia, www.doir.wa.gov.au
if(require(spatstat.geom)) {
if(interactive()) {
data(murchison)
plot(murchison$greenstone, main="Murchison data", col="lightgreen")
plot(murchison$gold, add=TRUE, pch="+",col="blue")
plot(murchison$faults, add=TRUE, col="red")
}
## rescale to kilometres
Mur <- solapply(murchison, rescale, s=1000, unitname="km")
}
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