Plutonic complexes are the result of multiple emplacements of different plutonic bodies, whose composition can be differentiated through mechanisms such as fractional crystallization, mixing or assimilation. Tectonics can further influence the final rock type distribution, largely modifying the original primary contacts. Therefore, rock type distribution within a plutonic complex can be described as the result of the interplay of deterministic (i.e. driven by a specific gradient) and/or a stochastic (controlled by a probabilistic rule) processes. For these reasons, we developed a new GIS-based automated package tools (IG-Mapper - Fiannacca et al., 2017) for the interpolation of geochemical parameters of plutonic rocks based on the alternative choice of a deterministic (i.e. Inverse Distance Weighted - IDW) or a stochastic (i.e. Kriging) interpolator. IDW is based on the multivariate analysis of selected mapped variables whose influence decreases with the distance between its sampled locations. The most important parameter to consider using the IDW is the “Power”, which allows checking the meaningfulness of the known points with respect to the interpolated values, based on their relative distance. Kriging is a non-deterministic algorithm based on the calculation of an autocorrelation function among sampling points for a specifically selected parameter, which has to be considered to vary with continuity in the region of interest. Kriging is also a best linear unbiased estimator (BLUE) based on a regional variable (RV), which tends to be correlated at specific scales and decreases with the increase of the distance calculating at the same time the magnitude of the reliability of the interpolation. IG-Mapper produces also lithological maps based on TAS (Middlemost, 1985, 1994), R1-R2 (De La Roche et al., 1980), Q-ANOR (Streckeisen and Le Maitre, 1979) and Ab-An-Or (Barker, 1979) classification diagrams. The obtained lithological maps provide a reconstruction of the field relationships between different lithological units with an estimate of geostatistical reliability achievable through the construction of interpolation checking maps. These last maps provide an estimate of the geostatistical interpolability for one specific parameter at a time, calculating an empirical index (Stochastic Interpolation Index), able to highlight the randomness of a specific value distribution.

Deterministic vs. Stochastic interpolation methods in geochemical mapping of plutonic complexes: Automated mapping via IG-Mapper of the Squillace pluton (Serre Massif, Southern Italy)

Ortolano G.
;
Fiannacca P.;Pagano M.;Visalli R.;Cirrincione R.
2018

Abstract

Plutonic complexes are the result of multiple emplacements of different plutonic bodies, whose composition can be differentiated through mechanisms such as fractional crystallization, mixing or assimilation. Tectonics can further influence the final rock type distribution, largely modifying the original primary contacts. Therefore, rock type distribution within a plutonic complex can be described as the result of the interplay of deterministic (i.e. driven by a specific gradient) and/or a stochastic (controlled by a probabilistic rule) processes. For these reasons, we developed a new GIS-based automated package tools (IG-Mapper - Fiannacca et al., 2017) for the interpolation of geochemical parameters of plutonic rocks based on the alternative choice of a deterministic (i.e. Inverse Distance Weighted - IDW) or a stochastic (i.e. Kriging) interpolator. IDW is based on the multivariate analysis of selected mapped variables whose influence decreases with the distance between its sampled locations. The most important parameter to consider using the IDW is the “Power”, which allows checking the meaningfulness of the known points with respect to the interpolated values, based on their relative distance. Kriging is a non-deterministic algorithm based on the calculation of an autocorrelation function among sampling points for a specifically selected parameter, which has to be considered to vary with continuity in the region of interest. Kriging is also a best linear unbiased estimator (BLUE) based on a regional variable (RV), which tends to be correlated at specific scales and decreases with the increase of the distance calculating at the same time the magnitude of the reliability of the interpolation. IG-Mapper produces also lithological maps based on TAS (Middlemost, 1985, 1994), R1-R2 (De La Roche et al., 1980), Q-ANOR (Streckeisen and Le Maitre, 1979) and Ab-An-Or (Barker, 1979) classification diagrams. The obtained lithological maps provide a reconstruction of the field relationships between different lithological units with an estimate of geostatistical reliability achievable through the construction of interpolation checking maps. These last maps provide an estimate of the geostatistical interpolability for one specific parameter at a time, calculating an empirical index (Stochastic Interpolation Index), able to highlight the randomness of a specific value distribution.
Kriging/IDW interpolation, Python, Lithological maps
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/348531
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