GIS-BASED STATISTICAL TECHNIQUES FOR MODELLING FOR MINERAL
EXPLORATION
Alok Porwal
International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The
Netherlands
This research examines the application of geographic information systems in predictive modelling for
base-metal exploration. Both genetic and empirical methods are used for identification of exploration
criteria. Various established and new mathematical techniques, both data-driven and knowledge-driven,
are tested in the search for the optimal model.
Introduction
Formation and localization of a mineral deposit is function of a number of temporally and/or spatially
related earth processes, which leave behind their signatures in form of various geologic features associated
with the deposit. Mineral deposits are therefore often characterized by their responses in the form of subtle
anomalies in various data sets. One of the major problems in area selection for mineral exploration is
definition of the relationship between the geologic characteristics of mineral deposits and their geologic
environments. Most professional exploration geologists use heuristic, experience- and intuition-based
methods for understanding this relationship and making decisions regarding area selection. In recent years
integration, superimposition and analysis of spatial data in a GIS, which facilitates a more objective
interpretation, have complemented these subjective methods. The simplistic use of GIS, however,
essentially reveals only empirical relationships. For an objective and quantitative definition of
relationships, it is necessary to construct statistical models, which use various geologic features as
independent variables (input parameters) to predict the probability of occurrence of a mineral deposit. This
research aims at developing generalized mathematical model(s) for demarcating potential areas with
conformable, sediment-hosted base-metal deposits using statistical methods in a GIS environment. The
test area is the south-central part of the Precambrian Aravalli metallogenic province, western India. A prerequisite for achieving the main research objectives is to develop tectonic and metallogenetic models
Aravalli metallogenic province.
Research work in the reporting year
The fuzzy logic and the weights-of-evidence predictive models for base-metal exploration in the southcentral part of Aravalli province, which were generated the previous year, were selectively verified in the
field. Evidences of base-metal mineralisation were found in the predicted high-favourability area in the
western part of the province.
The main focus of attention in the reporting year was on the tectonic and metallogenetic modelling of the
Aravalli metallogenic province. Input thematic layers including aeromagnetics, Bouguer gravity,
topography and remotely-sensed spectral data were incorporated in the previously-established GIS of the
Aravalli province. The objectives were three fold: (1) to demarcate various tectonic domains within the
province on the basis of magnetic anomaly characteristics; (2) to model the deep-crustal structure of the
province using the Bouguer gravity data; and (3) to examine the possible role of the plate tectonic
processes in the tectonic and metallogenetic evolution of the belt. Whilst the geological data were
processed within the GIS, the aeromagnetic, Bouguer gravity, topographical and remote sensing data were
processed outside the GIS using specialised software systems, and the processed maps were imported back
into the GIS.
Interpretation of the total magnetic intensity field data
Experimentation was performed with several filtering techniques in order to enhance magnetic data for
optimal information extraction for a qualitative tectonic interpretation. These included upwardcontinuation, reduction-to-pole, vertical derivatives and a combination of reduction-to-pole and upwardcontinuation. The filtering was performed using the Fast Fourier Transformation (FFT) technique, in
which the gridded spatial data were transformed to wave numbers by Fourier analysis, operated upon by
appropriate filters, and transformed back into the spatial domain. In addition, the shaded-relief and 3-D
analytical signals of the total magnetic intensity field were calculated, both for the unfiltered data and the
upward-continued data.
0
Gravity
(mGals)
-30
-60
=Observed,
-90
=Calculated
Phulad Thrust
-4
Jahazpur Thrust
I
IA
III
II
IV
V
IIA
VI
VIII
VIIIA
VII
IX
16
VIA
Depth (km)
X
XI
36
M
O
O
H
XII
56
0
50
100
150
Distance (km)
Figure 1: The observed and computed Bouguer anomaly across the central part of Aravalli province. The causative tectonic domains along
with the densities in gm/cc (in parentheses) are: I-Malani Igneous Suite (2.62), IIA-Phulad Ophiolite Suite (2.9), II-Delhi Belt (2.72), IIISandmata Complex (2.76), IV-Mangalwar Complex (2.75), V-Bhilwara Belt (2.72), VI-Hindoli Belt (2.65), VIA-low density body below
Hindoli Belt (2.85), VII-Berach Granite (2.62), VIII-Vindhyan Belt (2.56), VIIIA-high density body below Vindhyan Belt (2.9), IX-Upper
Crust (2.7), X-high density body within Lower Crust (3.04), XI-Lower Crust (2.9) and XII-Mantle (3.3)
The shaded relief image of the magnetic field shows anomaly patterns within Aravalli province that
mainly reflect the composition and structural disposition of the metasedimentary components and the
three-dimensional spatial distribution of the basic metavolcanic and basic/ultramafic intrusive
components. As the structural disposition of the metasediments and the emplacement of the igneous rocks
are tectonically controlled, the anomaly patterns can be assumed to be directly related to the tectonic
processes, and hence were used to divide the belt in different tectonic domains. These tectonic domains
are separated by regional-scale magnetic lineaments that have close spatial relationship with the basic and
ultramafic igneous bodies. These lineaments have strong expressions in the topographic and remote
sensing data, and many of them can be spatially correlated with the first-order stratigraphic unconformities
in the province.
Bouguer gravity modelling
A 2½D forward gravity modelling procedure was used to model the observed Bouguer gravity values
along two transects through the central and southern parts of Aravalli province. The modelling was
constrained by deep seismic reflection data. The regional-scale magnetic lineaments were modelled as
boundaries between different tectonic domains in the crustal sections. The shapes and densities of
different tectonic domains were adjusted within reasonable limits so as to minimize RMS error between
the computed and the observed gravity fields. Figure 1 shows the resulting crustal density model across
the central part of the province, and the computed and the observed fields. The model indicates that the
Phulad Ophiolite Suite (IIA) is a distinct tectonostratigraphic domain and should be separated from the
Delhi belt. This belt, which has an island arc-type lithogeochemistry, hosts significant VMS-type deposits.
Similarly, the Bhilwara belt (V), which comprises isolated and linear metasedimentary sequences within
the basement complex, appears to have evolved in an intracratonic rift system or pull-apart basins, and as
such, should be separated from the surrounding basement rocks. This belt, which hosts the most
significant SEDEX-type base-metal deposits in India, has been generally believed to be conformable with
the surrounding basement rocks. The spatial configuration of the Bhilwara belt with respect to the crustalscale Jahazpur Thrust (lower surface of VI) has been clearly imaged on deep seismic reflection data as
westerly dipping surfaces that extend to the Moho. This indicates that the Jahazpur Thrust might have
served as conduit for ore bearing fluids. The model also suggests the presence of two Proterozoic
collisional boundaries, viz., the Phulad Thrust and the Jahazpur Thrust.
Research plan for 2002
Predictive modelling based on artificial neural networks will be attempted. A 3D GIS will be created for
the Pur-Banera-Rajpura-Dariba-Bhinder belt and new techniques will be explored for 3D predictive
modelling for base metals in this belt.