Autoritario

Interpretation begins with a qualitative analysis of the geophysical data. If the aim is simply to identify and target individual anomalies, i.e. regions with anomalous responses, then this is, in principle, a straightforward task provided the data are appropriately processed and pre- sented (seeSections 2.7and2.8). If the aim is to create a pseudo-geological map, then this is usually a more demanding task, not least because it requires both geo- logical and geophysical expertise. For this reason, our description of interpretation is focused more towards the requirements of geological mapping. We leave the issues of interpreting depth and pseudo-depth sections to our descriptions of the individual geophysical methods. Also, we focus on the interpretation of data displayed in the near ubiquitous form of pixel images.

When a target is identified or when difficulties are encountered in making a pseudo-geological map, the observed data can be analysed with the aid of the a) b) c) 2 Kilometres 0

Figure 2.36 Various composite displays of the same aeromagnetic dataset. The pseudocolour emphasises amplitudes whilst the textural information is contained in the grey-scale shaded relief component as follows: (a) grey-scale shaded relief and (b) wet-look grey-scale

shaded relief. (c) High-passfiltered data in pseudocolour with

grey-scale shaded relief. All images are illuminated from the north. Data are aeromagnetic data from the vicinity of the Mount Polley alkalic Cu–Au deposit, located in British Columbia, Canada. Pit outlines shown by the solid lines. Data are reproduced with the permission of the Minister of Public Works and Government Services Canada, 2006, and courtesy of Natural Resources Canada, Geological Survey of Canada.

computed response of a model, i.e. a (simplified) numerical representation of the subsurface geology. This is a form of quantitative analysis. Obtaining a match between the model’s geophysical response and actual observations places constraints on the geology of the subsurface, e.g. the depth of the source of an anomaly, the dip of a stratigraphic contact etc. In most cases, a match is not a definitive result, since more than one model can be made tofit the data. In other words, there is no unique arrange- ment of the geological elements that explain the observed geophysical data, so the interpretation is ambiguous. This fundamental and important aspect of geophysics is known as non-uniqueness, and is discussed in more detail in

Section 2.11.4.

Before discussing the general aspects of interpretation, we consider a number of issues affecting interpretation and describe the important matter of accounting for long- wavelength variations, i.e. the regional response.

2.9.1

Interpretation fundamentals

Aside from the difficulties in reconciling the geological and geophysical perspectives of the geological environment (see

Section 1.1), an interpretation must consider the following

aspects of geophysical data and geophysical responses: • Petrophysics is the link between the geophysical

response and the geology. It is a complex subject. Changes in lithology, mineralogy, alteration, fabrics, weathering and structure can cause great variability in physical properties, so the available data may not be representative of the rocks in the ground. Also, some physical properties are commonly anisotropic, notably electrical conductivity, magnetic susceptibility and seis- mic velocity, often being different when measured paral- lel and perpendicular to planar fabrics such as bedding or metamorphic foliations. This will affect the geophys- ical response.

• The scale of resolvable features needs to be understood: in particular, the diminishing resolution of features with depth and the concept of footprints, i.e. the volume of the subsurface contributing to an individual measure- ment (seeSection 2.6.2).

• Since a geophysical map contains responses from fea- tures at a range of depths, the interpreter needs to think in terms of three dimensions. For example, the fact that one anomaly apparently cuts across another does not mean the sources of the anomalies are actually in

contact. They may be located at different depths. Shallow and deep responses need to be recognised, a subject we describe later for each geophysical method.

• There is a strong possibility of responses originating in the near-surface cover, regolith, permafrost etc., and that bedrock responses may be distorted and/or attenuated by the effects of the cover.

• Distortions caused by topography may be present. We stress the importance of integrating topographic data into the interpretation of all types of geophysical data; because for most geophysical methods, the shape of the land surface affects the geophysical measurements to produce terrain-induced responses. Nevertheless, it is important to be aware that a correlation between geo- physical responses and topography is not necessarily indicative of artefacts in the dataset, since both the topography and the geophysical responses are controlled by the local geology. More likely to be artefacts in the geophysical data are responses not directly correspond- ing with topographic features, but occurring adjacent to them. For example, a ‘moat’ surrounding a hill or a linear anomaly adjacent to a ridge should be treated with scepticism. Even then the responses may be reflecting the local geology: a hill due to an intrusion may have a contact aureole, the cliff may be indicative of a fault etc. • A map or image of the survey stations, or the survey lines and the terrain clearance for airborne surveys, should always be available for the interpretation. Inte- grating these with the survey data and its various trans- forms can reveal correlations with responses, possibly indicating artefacts in the data caused by the station distribution itself, or by variations in survey height. Features occurring in areas of sparse data or oriented parallel to the survey lines should be carefully analysed to ensure they are not survey artefacts.

• The survey line direction and spacing have effects on the resolution of particular geological features.

• Geophysical responses may have a complicated form which is not indicative of their source geometry and may be offset from their source location or extend beyond the edges of their source.

• Artefacts caused by data processing and display may be present. Levelling errors may introduce artefacts trending parallel to survey lines. Gridding may introduce beading and bulls-eyes (see Section 2.7.2.3). Apparent ‘cross-cutting relationships’ are not necessarily indicative of relative ages as gridding may result in datasets where higher-amplitude anomalies appear to cross-cut

low-amplitude ones. Shaded relief also creates spurious apparent cross-cutting relationships (Fig. 2.34).

• Information about infrastructure that might produce artefacts in the geophysical data, e.g. powerlines, pipelines, railways, buildings or open pits, needs to be available. This can usually be obtained from cadastral maps and aerial photography. Responses from these features reflect their form: powerlines create linear arte- facts, buildings localised responses etc.

• When several kinds of geophysical data are being inter- preted together, which is generally a good strategy, it is very important to consider the geological controls on their individual responses. For example, a geological feature may have a wide gravity response, but only a particular region of the feature may contain minerals that produce a magnetic response, electrically conductive minerals may be confined to other parts of the body, and radioactivity would only be observed where radioactive minerals are exposed at the surface. Often there is partial correspondence between gravity and magnetic datasets, both primarily reflecting bedrock lithological variations. However, there is no requirement for their responses to correspond exactly with, say, the distributions of radio- elements in the near-surface as derived from radiometric data, which are much more likely to correlate with spectral remote sensing data. Sometimes the different depths ‘probed’ by the different geophysical methods cause lateral offsets between the responses of the differ- ent data types if the source is dipping. For example, electrical measurements may respond well to the con- ductive (weathered) near-surface geology, whilst gravity and magnetic data may contain responses from the unweathered deeper regions.

To create an interpretation which accounts for the above requires knowledge that is method specific, together with a good understanding of the nature of geophysical responses, and also how the data were acquired and processed.

2.9.2

Removing the regional response

The measured variations in the geophysical parameter of interest almost invariably consist of a series of superim- posed responses from geologic features of different dimen- sions and/or depths in the vicinity of the measurement. A common manifestation of this is the interference (see

Appendix 2) between an anomaly of interest and the

longer-wavelength variations associated with deeper and/

or larger features of less interest, i.e. the background or regional response. The regional response is a form of geological environmental noise (see Section 2.4). The target anomaly and the regional field can be separated in a process known as regional removal (Fig. 2.37). The variation remaining after the regional has been removed is known as the residual response. This is the shorter- wavelength variation correlating with the shallower geol- ogy, and is usually the response of interest. InFig. 2.37it is the response originating from the mineralised environ- ment. Removing the regional response is important for quantitative analysis (seeSection 2.11) and can also greatly facilitate qualitative interpretation (see Section 2.10), as shown inSection 3.11.1.

As with any consideration of signal and noise, the vari- ations of interest depend on the interpreter’s requirements of the data. Consider the hypothetical example of a massive nickel sulphide deposit located at the contact between mafic and ultramafic rocks in a greenstone belt, e.g. the komatiitic peridotite-hosted deposits that occur in the Kambalda area in Western Australia (Fig. 2.38). In the early stages of exploration, prospective contacts may be the target being sought. In this case, the contact response is a residual (signal) superimposed on the regional (noise) response of, say, the base of the greenstone succession. When the contact has been located and exploration focuses along it, responses from nickel sulphide mineralisation become the target. The contact response is now the regional (noise), and the response of the mineralisation is the residual (signal).

Ideally, the responses due to the deeper and/or larger geological features are computed (modelled) and sub- tracted from the data. However, accurately defining the regional response requires detailed knowledge of the local geology, which depends on the geological interpretation of the area (an example of the geophysical paradox, cf.

Section 1.3). Non-uniqueness (seeSection 2.11.4) also con-

tributes to the problem because interference between adja- cent local anomalies may produce a longer-wavelength composite response which could be mistaken for part of the regional response. Also, the local and the regional responses must both be properly defined in the original data series, i.e. the sampling interval needs to satisfy the conditions of the sampling theorem for the higher- frequency local response (Section 2.6.1), and the data series must extend far enough in distance to define the longer- wavelength regional response. Unfortunately, then, the regional variation cannot be accurately calculated or

modelled so it cannot be properly removed but, neverthe- less, the requirement for removal remains. There exists an extensive literature on regional removal, often referred to as ‘regional–residual separation’, and various techniques are used. Hearst and Morris (2001) show how subjective regional removal can give rise to conflicting interpretations of gravity data from the well-known Sudbury structure in Ontario, Canada.

The two most common approaches to removing the regional response involve either using a smooth mathemat- ical function to approximate the regional or filtering the data to remove the longer-wavelength variations (see Frequency/wavelength inSection 2.7.4.4). Neither approach is universally applicable, notwithstanding the fact that determining whether a particular approach is ‘correct’ is a purely subjective decision. It is often the case that the process needs to be repeated with different interpolation/ filter parameters until an ‘acceptable’ residual response is obtained. Ultimately, the task for the interpreter then is to find a residual response, from an infinite number of possi- bilities, which is the most geologically plausible. The target anomaly will inevitably be distorted in some way.

We describe here some aspects of regional removal, emphasising the practicalities of the process. At this stage it is worth saying that avoiding the problem altogether may, in some situations, be the best approach. Sometimes it may be better to account for all scales of variation in the observed data, even if this necessitates making gross assumptions about the deep geology of the area, rather than remove some arbitrary regional component. At least, in this case, the assumptions about the local geology are known to the interpreter. On the other hand, not to remove some form of the regional variation can severely limit the interpretation of important local responses.

2.9.2.1 Interpolation methods

Surface and curvefitting techniques involve interpolating the broader regional response into areas dominated by the

10 20 Gravity (gu) 30 0 40 50 10 20 0 a) Mineralisation Gossan Surface

Quartoze sedimentary rocks Graphitic, chloritic sedimentary rocks

Observed Regional Residual Metres 0 200 Metres 0 200 0 10 TMI (nT) 20 –10 30 40 10 20 0 Observed Regional b) Marcasite mineralisation Residual Surface SP (mV) c) SP (mV) Metres 0 25 Ore intersection Regional Observed Residual 200 400 600 800 0 0 200 400 600

Figure 2.37 Examples of regional removal. (a) Gravity data and geology from the Murray Brook massive Cu–Pb–Zn sulphide deposit, New Brunswick, Canada. Redrawn, with permission, from

Thomas (1999). (b) Magnetic data across a Mississippi Valley-type

Pb–Zn deposit, Pine Point area, Northwest Territories, Canada.

Redrawn, with permission, from Lajoie and Klein (1979). (c)

Downhole self-potential data from a drillhole intersecting the Joma pyrite deposit, Trøndelag, Norway. Redrawn, with permission, from

Skianis and Papadopoulos (1993).

shorter-wavelength variations. A smooth mathematical function is used to describe the regional variation, a curve for 1D data and a surface for 2D data, and the computed curve/surface is subtracted from the observed data. Often the regional variation over a small area can be adequately represented by a straight line (1D) or a sloping plane (2D). In its simplest form, the interpolation can be done by manually estimating the form of the regional; the process is given the rather grand name of graphical. The basic idea is to extrapolate thefield from areas of the data perceived to be free of shorter-wavelength responses into the area con- taining the local response of interest. The great advantage of the graphical approach is that the effects of the known geological features can be factored in, for example the

effect of the contact in Fig. 2.38. The disadvantage is that the method is not objective. By including a geological interpretation a bias is introduced; if the interpretation is erroneous there is less chance of adequately removing the regional variation.

The most common analytical approach involvesfitting a polynomial function to the data using the method of least- squares (Davis, 1986). The polynomial may be of any order, but the smoothness of the regional response means that the curved surface is not complex and, therefore, should be adequately defined by a low-order function. Different types of polynomial and fitting methods have also been proposed; see examples in Beltrão et al. (1991). The method is equally applicable to 1D or 2D datasets.

Fitting a smooth surface to the entire dataset using least- squares cannot achieve its goal unless the short-wavelength variations are randomly distributed about the regional, i.e. the short-wavelength features have a zero mean, which will rarely be the case. The problem is illustrated inFig. 2.39. The curve approximating the regional is deflected by the local response. At best this produces an incorrect base level for the residual, or worse, this deflection will be broader than the local anomaly, resulting in a residual whose amp- litude is too small and which is flanked by side-lobes of opposite polarity (Agarwal and Sivaji,1992). Various solu- tions to the problem have been suggested. These range from only using data points perceived to be‘regional’ when fitting the curve, through to an iterative approach where residuals are analysed and used as a basis to modify subse- quentfittings of the curve and so on. Another problem is deciding the order of the polynomial. If it is too high there is loss of detail in the residual, and if too low the regional component severely distorts the residual. In Fig. 2.39the straight line (linear) is correct, but the second-order polynomial produces a regional that is too complex (i.e. curved).

2.9.2.2 Wavelength filtering methods

Wavelength-based regional removal involves applying a filter to the data to remove the long-wavelength variations in order to reveal the shorter-wavelength residual response (see Frequency/wavelength in Section 2.7.4.4). Fourier transforms (seeAppendix 2) can be computed to identify the spatial frequencies of the various responses in data. Both the local and regional responses usually extend over a range of frequencies/wavelengths and there is often over- lap, so it is impossible to separate the two responses com- pletely. The wavelength filter in Fig. 2.39 produces a

Gravity

Gravity

Nickel sulphides

Mafic–ultramafic contact

Base of greenstones Nickel sulphides only

Nickel sulphides + mafic–ultramafic contact + base of greenstones a) b) c) Nickel sulphides + mafic–ultramafic contact Nickel sulphides Ultramafics Mafics Base of greenstones

Figure 2.38 A model showing how the various components of the geology contribute to the overall geophysical response; represented with gravity data. A hypothetical massive nickel sulphide body occurs at a mafic–ultramafic contact in a greenstone belt; see geological section (c). (a) The different gravity responses of the mineralisation with various component responses of the surrounding geology included. (b) The three component gravity responses producing the resultant measured response.

residual with the correct base level and showing the local anomaly with approximately the correct overall shape, but the responses are distorted owing to the overlap in their frequency content and the imperfect nature of the filter. Cowan and Cowan (1993) describe various wavelength- basedfiltering methods for obtaining the residual compon- ent of aeromagnetic data.

2.10

Data interpretation – qualitative