processing and interpretation 2.1 Introduction
2.10 Data interpretation – qualitative analysis
2.10.1 Spatial analysis of 2D data
The spatial analysis of a 2D geophysical dataset presented in pixel form is similar to the analysis applied to any other kind of image and is based on various characteris- tics, the most fundamental being texture and tone. Tone is the relative colour or brightness of a region and in geo- physical terms is usually equated to amplitude. Areas with similar tonal characteristics are best identified in pseudocolour displays (see Section 2.8.2). Texture is the pattern created by short-wavelength variations in tone. The nature of a particular texture can be difficult to quantify, but qualitative descriptions such as ‘rippled’ and ‘granular’ adequately describe textural characteristics for geophysical purposes. Textural variations are best seen
Location
Variation
Variation
Variation
Location Location
Location Location Location
Linear a) b) Local anomaly Regional Observed True residual
Polynomial (order 2) Wavelength filter
Location Estimated regional Estimated regional Estimated regional Estimated residual Estimated residual Estimated residual c)
Figure 2.39Some common consequences of incorrectly estimating the regional response. (a) Synthetic example of observed data comprising a local anomaly (residual) superimposed on a linear regional response. (b)
Estimated regionalfields. (c) The incorrect estimated
residual anomalies resulting from the various regional responses shown in (b). See text for details. Based on a
diagram in Leaman (1994).
in grey-scale displays, especially when displayed as shaded relief (see Section 2.8.2.3).
Texture and tone are the usual criteria for delineating (mapping) regions with similar characteristics in an image. This may involve working with a suite of images empha- sising these characteristics in complementary ways. The most useful are the colour-draped displays, like that shown
inFig. 2.36and described inSection 2.8.2.4, because they
combine textural and tonal information very effectively. Regions with consistent textural and/or tonal characteris- tics are interpreted in terms of their shape, pattern, size (scale) and (geological) context (association) (Fig. 2.40).
2.10.1.1 Shape and pattern
Shapes and patterns of the responses are very important aspects of the interpretation of 2D geophysical data. The inclined lithological layering common in many geological environments is translated into responses that are parallel and extend over a significant area, whether stratigraphic or igneous in origin (seeFigs. 3.28and4.26). Features that are more equidimensional are often indicative of intrusions (sedimentary or igneous). Examples of some other geo- logical patterns common in geophysical data include den- dritic drainage networks (see Figs. 3.75and4.24),‘veeing’ caused by dipping layers intersecting valleys or by plunging folds (seeFigs. 3.28and4.26), linears and dykes (Fig. 2.27),
shear zones (Figs. 3.27, 3.66a, 3.74a, 3.75 and 4.26) and steeply dipping stratigraphy), fold interference patterns, and chaotic patterns in the interior of diapirs.
2.10.1.2 Size
When interpreting geological features, size must be used with caution because many common geological entities are self-similar over a large size range. A well-known example is folding, where there may be microscopic folds in fabrics which are located within parasitic folds on the limbs of larger folds etc. A good example where size is significant is astroblemes, whose characteristics change with size, from small‘simple’ craters to larger ‘complex’ craters to massive ‘multi-ringed basins’. An example of the less rigorous application of size applies to intrusions such as kimberlites, which tend to be comparatively small. 2.10.1.3 Context
Context is ultimately the most important clue to the geo- logical significance of a pattern of geophysical responses. Some examples include the following:
• A response that cross-cuts the dominant anomaly pat- tern in a region is consistent with an intrusion (Figs. 3.74
and 4.26); if it is linear and extensive it is almost certainly a dyke (e.g. Schwarz et al. (1987), (Figs. 3.66a
and3.74).
• The boundary between two regions with different anom- aly patterns may be an unconformity (Fig. 4.26). • A linear feature associated with the lateral displacement
or truncation of other responses may be a fault or shear zone (Figs. 3.74and4.26).
• Concentric anomaly patterns may be due to zoned intru- sive complexes and/or contact aureoles (Hart, 2007; Schwarz,1991).
• Distinctly curvilinear features are likely to be faults or dykes associated with volcanic centres, plutons, diapirs or impact structures.
• Sets of linear responses at a consistent angle to each other are probably associated with jointing or conjugate faulting.
• Anomalous response(s) at a site geologically favourable for mineralisation may be a potential target. What con- stitutes a favourable site depends upon deposit type and the exploration model. It could be a zone of structural or stratigraphic complexity, the margin of an intrusion etc. • Integrating the responses from different geophysical data types also provides context. For example, coincidence
Tone Texture
Shape
Pattern Context
Size
Figure 2.40 Some basic elements of image analysis. Based on
between aeromagnetic and airborne electromagnetic anomalies was very successfully used as a target selection criterion for nickel sulphide mineralisation in the Can- adian Shield in the 1950s (Dowsett,1967).
When the principal object is anomaly detection rather than geological mapping, context can be the only means of ranking the anomalies as exploration targets. However, discriminating a target anomaly from unwanted target- like anomalies is a fundamental challenge for the inter- preter. Often geophysical data are unable to differentiate between responses from mineralised environments and geologically similar, but unmineralised, settings.
2.10.1.4 Human perceptions of spatial data
The interpretation process relies on the human mind pro- cessing information collected by the human eye to perceive patterns in spatial data, and then interpreting these pat- terns in terms of the geology. Despite the expression ‘seeing is believing’, this is far from an infallible process. This aspect of being human is poorly understood and almost completely unexplored from a geophysical context, so only a few general observations are possible. Import- antly, we emphasise and demonstrate the real possibility of making erroneous interpretations of patterns in a dataset.
The reader will be familiar with optical illusions, where the eye/brain system misinterprets one or more character- istics of an object in a picture. The most relevant examples in the context of geophysical image analysis are linear and circular features, since these are fundamental components of the geological environment.Figures 2.41a and bdem- onstrate how obliquely intersecting lines produce illusion- ary offsets and misjudgement of parallelism. The former is a commonly occurring line-geometry in geophysical inter- pretations, with the potential result being incorrect inter- pretations of faulting. The next two examples,Figs. 2.41c
andd, illustrate how the human vision system may per- ceive non-existent lines. The potential exists in geophysical data for the interpretation of spurious linear features.
Figure 2.41e shows the strong human tendency to see
circles, even when there are none present, or even when there are no curved lines in the area. Figure 2.41f shows how abrupt changes in image intensity create the illusion of deviations in straight lines. Although all three arcs in
Fig. 2.41ghave the same radius, they are perceived to be
different, which may cause problems when interpreting data containing curvilinear features. Finally, the two cen- tral squares in Fig. 2.41hare the same shade of grey, but
the different shades of the surrounding areas disguise this fact. The same illusion occurs with coloured regions.
Another common form of optical illusion is the inver- sion of apparent topography associated with the illumin- ation direction in a shaded relief display (see Section
2.8.2.3 and Fig. 2.32). Some forms of data enhancement
specifically rely on the identification of positive features, for example the analytic signal transformation applied to potential field data (seeSection 3.7.4.2). The possibility of making erroneous interpretations is obvious.
2.10.1.5 A cautionary note
Figure 2.42ashows the locations of sources of geophysical
responses, perhaps magnetic sources derived from Euler deconvolution (see Section 3.10.4.1) or anomalous radio- metric responses (see Section 4.5.1). From the various patterns that can be seen inFig. 2.42a, in particular linear and circular alignments, and variations in their clustering, a plausible geological interpretation has been made. Linear alignments were identified first and then regions with similar internal distributions of sources were delineated. The interpretation recognised a faulted and folded layered
a) b)
c) d)
e) f)
g) h)
Figure 2.41 Examples of some optical illusions that can be misleading in the interpretation of geophysical data. See text for explanation.
sequence intruded by various igneous rocks. Depending on the local geology, sites considered favourable for mineral- isation might be the intersection of the major fold and the largest fault in the survey area, or where faults are associ- ated with the intrusive rocks. Although apparently geo- logically plausible, there is an underlying problem: the dataset comprises 500 randomly distributed points. There is a high probability that any distribution of points will show spatial alignments that form linear and curvilinear features, and also clusters, many or even all of which have no significance. Interpreters can easily fall into this trap when other data are not available to guide the
interpretation. In fact, it is very common for humans to mistakenlyfind structure in random datasets, as described by Taleb (2001) in the context of the analysis of stock markets and more generally by Shermer (2011).
How can traps like those described be avoided? Unfor- tunately, there is no panacea, but the following cautionary strategies are generally applicable and can help the inter- preter to avoid common mistakes:
• One reason that it was possible to create geological ‘sense’ from a random pattern was that only one form of presentation of the data was used for the
Irregular pattern of widely spaced sources Absence of sources
Many tightly clustered sources
Sub-circular source patterns, moderate number of sources Sub-circular regions with sources at their edges
Intense concentration of sources – skarn?
Faults (linear boundary between different source patterns) Dykes (linear pattern of sources)
Inferred stratigraphy – layered units
Intrusive units Linears b) a) c) Intrusions at depth Intrusions at depth Intrusive complex Fold hinge
Figure 2.42 A cautionary note. (a) Map of source locations as might be obtained from a geophysical dataset. (b) Linear alignments identified
within the distribution, and (c) a possible geological interpretation of the data. Although apparently plausible, the source distribution is in fact
interpretation. By using both the data in its basic form and its various transformations, such as derivatives, and by making use of different kinds of display, for example grey-scale, pseudocolour etc., more information can be displayed, making for a much more reliable interpretation.
• Although various forms of the data may emphasise par- ticular characteristics, other important characteristics may be obscured; so the interpreter should continually make reference to the most fundamental form of the geophysical data, e.g. the magnetic or gravityfield strength, the calcu- lated electrical conductivity/resistivity, etc., described in a simple form such as a colour-draped display (Fig. 2.36). • As demonstrated byFigs. 2.41and2.42, the human vision
system is not infallible. Many of the potential traps can be addressed in two ways. For reasons not fully understood, the same interpreters will make different interpretations of the same data if presented in a different orientation (Sivarajah et al.,2013). Why this is the case is the subject of current research, but the problem is easily addressed by rotating the data during interpretation.
• The use of several illumination directions in shaded relief is important because of its inherent directional bias, which can lead to incorrect or incomplete identifi- cation of linear features.
• Many optical illusions are a form of incorrect‘mental’ extrapolation or interpolation. This can be overcome by checking the interpretation whilst viewing a small window or subarea of the dataset.
• The need for the interpreter to continually evaluate the geological credibility of features interpreted from the data cannot be over-emphasised. The integration of other types of geophysical data, and geological, geochemical and topo- graphic data, is essential for developing an accurate inter- pretation of the data. The interpreter is more likely to be fooled if they treat their interpretation as‘factual’ without making the essential credibility checks.