Geochemical Anomaly and Mineral Prospectivity Mapping in GIS (eBook)

E.J.M. Carranza (Autor)

eBook Download: EPUB

2008 | 1. Auflage
368 Seiten
Elsevier Science (Verlag)
978-0-08-093031-2 (ISBN)

Infos

The book documents and explains, in three parts, geochemical anomaly and mineral prospectivity mapping by using a geographic information system (GIS). Part I reviews and couples the concepts of (a) mapping geochemical anomalies and mineral prospectivity and (b) spatial data models, management and operations in a GIS. Part II demonstrates GIS-aided and GIS-based techniques for analysis of robust thresholds in mapping of geochemical anomalies. Part III explains GIS-aided and GIS-based techniques for spatial data analysis and geo-information sybthesis for conceptual and predictive modeling of mineral prospectivity. Because methods of geochemical anomaly mapping and mineral potential mapping are highly specialized yet diverse, the book explains only methods in which GIS plays an important role. The book avoids using language and functional organization of particular commercial GIS software, but explains, where necessary, GIS functionality and spatial data structures appropriate to problems in geochemical anomaly mapping and mineral potential mapping. Because GIS-based methods of spatial data analysis and spatial data integration are quantitative, which can be complicated to non-numerate readers, the book simplifies explanations of mathematical concepts and their applications so that the methods demonstrated would be useful to professional geoscientists, to mineral explorationists and to research students in fields that involve analysis and integration of maps or spatial datasets. The book provides adequate illustrations for more thorough explanation of the various concepts.

*explains GIS functionality and spatial datastructures appropriate regardless of particular GIS software is in use
*simplifies explanation of mathematical concepts and application
*illustrated for more thorough explanation of concepts

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Geochemical Anomaly and Mineral Prospectivity Mapping in GIS documents and explains, in three parts, geochemical anomaly and mineral prospectivity mapping by using a geographic information system (GIS). Part I reviews and couples the concepts of (a) mapping geochemical anomalies and mineral prospectivity and (b) spatial data models, management and operations in a GIS. Part II demonstrates GIS-aided and GIS-based techniques for analysis of robust thresholds in mapping of geochemical anomalies. Part III explains GIS-aided and GIS-based techniques for spatial data analysis and geo-information sybthesis for conceptual and predictive modeling of mineral prospectivity. Because methods of geochemical anomaly mapping and mineral potential mapping are highly specialized yet diverse, the book explains only methods in which GIS plays an important role. The book avoids using language and functional organization of particular commercial GIS software, but explains, where necessary, GIS functionality and spatial data structures appropriate to problems in geochemical anomaly mapping and mineral potential mapping. Because GIS-based methods of spatial data analysis and spatial data integration are quantitative, which can be complicated to non-numerate readers, the book simplifies explanations of mathematical concepts and their applications so that the methods demonstrated would be useful to professional geoscientists, to mineral explorationists and to research students in fields that involve analysis and integration of maps or spatial datasets. The book provides adequate illustrations for more thorough explanation of the various concepts. - Explains GIS functionality and spatial data structures appropriate regardless of the particular GIS software in use- Simplifies explanation of mathematical concepts and application- Illustrated for more thorough explanation of concepts

Inhalt

Front Cover 1
Geochemical Anomaly and Mineral Prospectivity Mapping in GIS 4
Copyright Page 5
CONTENTS 10
Editor’s Foreword 6
Preface 8
PART I MODELS IN MINERAL EXPLORATION AND GIS 14
Chapter 1 Predictive Modeling of Mineral Exploration Targets 16
Introduction 16
What is Predictive Modeling? 17
Predictive Modeling of Significant Geochemical Anomalies 22
Predictive Modeling of Mineral Prospectivity 25
Predictive Modeling with a GIS 31
Summary 34
Chapter 2 Spatial Data Models, Management and Operations 36
Introduction 36
Models of Spatial Data 36
Management of Spatial Data 42
Operations on Spatial Data 46
Summary 60
PART II GEOCHEMICAL ANOMALY MAPPING 62
Chapter 3 Exploratory Analysis of Geochemical Anomalies 64
Introduction 64
Exploratory Data Analysis 66
Applications of GIS in EDA 75
Case study 76
Summary 96
Chapter 4 Fractal Analysis of Geochemical Anomalies 98
Introduction 98
Geochemical Landscapes as Fractals 99
The Concentration-Area Method for Threshold Recognition 105
Application of GIS in the Concentration-Area Fractal Method 107
Case Study 111
Conclusions 126
Chapter 5 Catchment Basin Analysis of Stream Sediment Anomalies 128
Introduction 128
Estimation of Local Uni-element Background due to Lithology 130
Dilution Correction of Uni-element Residuals 133
Analysis of Anomalous Multi-element Signatures 135
Application of GIS in Catchment Basin Analysis 138
Case Study 141
Discussion and Conclusions 155
PART III MINERAL PROSPECTIVITY MAPPING 158
Chapter 6 Analysis of Geologic Controls on Mineral Occurrence 160
Introduction 160
Spatial Distribution of Mineral Deposits 161
Spatial Association of Mineral Deposits and Geologic Features 175
Conclusions 199
Chapter 7 Knowledge-Driven Modeling of Mineral Prospectivity 202
Introduction 202
General Purpose Applications of GIS 204
Modeling with Binary Evidential Maps 207
Modeling with Multi-class Evidential Maps 218
Wildcat Modeling of Mineral Prospectivity 249
Conclusions 259
Chapter 8 Data-Driven Modeling of Mineral Prospectivity 262
Introduction 262
Selection of Suitable Unit Cell Size for Modeling 266
Selection of Coherent Deposit-type Locations for Modeling 273
Cross-validation of Data-driven Models of Prospectivity 283
Evidential Belief Modeling of Mineral Prospectivity 286
Discriminant Analysis of Mineral Prospectivity 306
Discussion and Conclusions 321
References 324
Online Sources 350
Author Index 352
Subject Index 360

Textauszug

Handbook of Exploration and Environmental Geochemistry 11, Vol. 11, Number Suppl (C), 2009

ISSN: 1874-2734

doi: 10.1016/S1874-2734(09)70006-3

Chapter 2: Spatial Data Models, Management and Operations

Introduction

Geochemical and other types of data sets for target generation in mineral exploration are spatial (or geographically-referenced) data that come from either primary or secondary sources and are stored in either digital or non-digital (analogue) formats. The diversity in storage formats of such data sets calls for proper data management in order to achieve efficiency in modeling of geochemical anomalies and prospective zones via various forms of spatial data analysis. Target generation in mineral exploration thus requires a computerised system such as a GIS so that the pieces of spatial geo-information of interest are mapped as discrete spatial entities or geo-objects (i.e., with perceivable boundaries, sizes and shapes). In a GIS, geo-objects are represented either as vector or raster spatial data models. The range of operations for spatial data analysis supported by a GIS depends on (a) geometric model of geo-objects (point, line or polygon), (b) spatial data models (vector or raster), (c) type of attribute data (quantitative or qualitative), (d) objectives of analysis and (e) GIS software package used. The last factor is least but must be considered important because many GIS software packages that are available at present support certain types of spatial data analyses using either only vector or only raster spatial data models.

This chapter explains briefly the concepts of spatial data models, especially which model is appropriate for representation of certain types of geoscience spatial data in a GIS, and the concepts for capturing and organising spatial data in a GIS database. The various types of GIS operations for spatial data analysis are also discussed briefly, because these will be the topics in the succeeding chapters.

Models of Spatial Data

The definition of model in this context is different from the definitions given in (Chapter 1). In the present context, a data model refers to (a) the schema or ways of organising data about real-world systems or (b) the symbolic representation of relationships between geo-objects and their data attributes.

Geo-objects

Many types of geological features with distinct boundaries, such as lithologic units, are clearly geo-objects. There are several types of geological features with no distinct boundaries, such as geochemical anomalies, which require modeling of pertinent spatial data to represent them as geo-objects. Modeling, therefore, involves various forms of analysis to partition or discretise pertinent spatial data in order to represent certain geological features of interest as geo-objects. For example, a threshold for background element concentrations must be determined in order to map geochemical anomalies.

The geometry of geo-objects can be represented based on their spatial dimensions. Point geo-objects are without length or area and thus 0-dimensional (0-D). Geochemical sample locations, although strictly not dimensionless, are usually depicted as points because they are usually too small to be represented in most map scales. Linear geo-objects (e.g., faults) are one-dimensional (1-D) and only have length as spatial measure. Polygonal geo-objects (e.g., geochemical anomalies) are two-dimensional (2-D) and have area and perimeter as spatial measures. Some geo-objects (e.g., geochemical landscape) require so-called 2.5-dimensional (2.5-D) representation, because they cannot be strictly described in two or three dimensions. Geo-objects characterised by their volume (e.g., orebody) require three-dimensional (3-D) representation. In addition, many geo-objects require fractal modeling to describe their geometry (Mandelbrot, 1983; (Chapter 4). A fractal geo-object is one which can be fragmented into various parts, and each part has a similar geometry as the whole geo-object.

The geometry of geo-objects can be defined according to either amount of sampling data or certain criteria (Raper, 1989). If the geometry of certain geo-objects is defined by amount of sampling data, then they are called sampling-limited geo-objects. Examples of sampling-limited geo-objects are porphyry stocks, quartz veins, lithologic contacts, etc., because they cannot be sampled or mapped completely if they are only partially exposed. If the geometry of certain geo-objects is defined by certain criteria in order to delimit their spatial extents, then they are called definition-limited geo-objects. The best example of a definition-limited geo-object is an orebody, the spatial extents of which are defined by cut-off grade at prevailing economic conditions. Significant geochemical anomalies and prospective zones are both definition-limited geo-objects, although they are also both sampling-limited geo-objects.

Vector Model

In a vector model, geo-objects are represented as components of a graph. That means the geometric elements of point, linear and polygonal geo-objects are interpreted in 2-D space as in a map (Fig. 2-1). Point geo-objects are nodes defined by their graph of map (x,y) coordinates. Linear geo-objects are defined by arcs with start-nodes and end-nodes or by a series of arcs inter-connected at nodes called vertices. Polygonal geo-objects are defined by inter-connected arcs that form a closed loop.

Fig. 2-1 Vector model of geo-objects.

The so-called spaghetti model is the simplest type of vector model (Fig. 2-2), which represents geo-objects in spatially less structured forms. That means, intersections between linear geo-objects are not recorded, whereas boundaries between polygonal geo-objects are represented separately. The latter is usually not without error and could result in so-called false or sliver polygons. The spaghetti vector model leads to inefficient data storage and is not amenable to true GIS functions (e.g., neighbourhood operations; see further below). This type of vector model has, nonetheless, the advantage that geo-objects can be readily scaled, transformed to other map projections and displayed using inexpensive systems for visualisation.

Fig. 2-2 Spaghetti model versus topological model of vector data.

The topological model offers vector representation of geo-objects in a spatially-structured form (Fig. 2-2) Topology is concerned with spatial relationships between geo-objects in terms of containment, connectivity, adjacency or proximity. In a topological model, linear geo-objects (including boundaries of polygonal geo-objects) are recorded in node-arc structures such that nodes represent intersections between linear geo-objects and form polyline segments or arcs and then arcs form polygons. Boundaries of polygonal geo-objects are not recorded separately. The model results in efficient storage of attributes of geo-objects and, more importantly, in explicit definition of spatial relationships between nodes, arcs and polygons. That means geo-objects on either left or right of another geo-object are explicitly defined. The topological model is amenable to true GIS functions (e.g., neighbourhood operations; see further below), not only because spatial relationships between geo-objects are defined but also because such spatial relationships between geo-objects are independent of map scale or measurement scales and are preserved even under transformations to various map projections (Fig. 2-3). A disadvantage of the topological model is that defining spatial relationships between geo-objects during spatial data capture and map editing can be time-consuming.

Fig. 2-3 Topological relationships between geo-objects remain unchanged under transformation.

Because a vector model represents geo-objects in 2-D space, it is not an appropriate model for surface variables such as topographic elevations, element concentrations of surficial materials, geophysical properties, etc. Although data for surface variables can be stored as a series of multi-valued points or a series of isoline contours in a vector model, a vector model does not adequately represent nor readily support calculation of surface characteristics (e.g., slope). Data of surface variables require 2.5-D representation such as tessellations of polygonal planar patches called triangulated irregular networks (TIN), which are usually treated as a vector model.

A TIN is constructed by connecting points of data (with x,y coordinates and z-values) to form a continuous network of triangles (Fig. 2-4). Note that a TIN can also be generated from points derived from isoline contours. There are various triangulation methods, but the most favoured is the Delaunay triangulation technique, which is a dual product of Thiessen or Voronoi or Dirichlet tessellations of polygons. The triangular facets defined represent planes with similar surface characteristics such as slope and aspect. A TIN model is adequate to represent geometry and topology of a surface, is efficient in data storage and can be locally manipulated to represent surface complexity by using breaklines (e.g., terrain discontinuities such as rivers or ridges on topographic surfaces). It is a significant alternative to surface representations based on regular grids.

Fig. 2-4 A triangulated irregular network (TIN) by Delaunay triangulation. Triangles are defined by three points forming circumcircles not...

Produkt-Details

• Erscheint lt. Verlag: 26.11.2008
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geologie
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geophysik
• Themenwelt: Naturwissenschaften ► Physik / Astronomie
• Themenwelt: Technik ► Bergbau
• Themenwelt: Technik ► Umwelttechnik / Biotechnologie
• Themenwelt: Wirtschaft
• ISBN-10: 0-08-093031-X / 008093031X
• ISBN-13: 978-0-08-093031-2 / 9780080930312