Targeted Airborne Geophysical Technologies for Geological Investigation

The general principles for developing such standard technologies are described and illustrated, with subdivision into geological mapping, predictive exploration (for various minerals), engineering geology, hydrogeology, geoenvironmental investigations, and other categories. Predictive exploration technologies are used to demonstrate how an operational sequence is constructed by defining a succession of intermediate exploration targets, their prerequisites, and their indicators (specific exploration targets). Consideration is given to accounting for the landscape and geological characteristics of the environment and selecting optimal observation systems for different exploration phases or stages. The system of targeted logical and semantic data interpretation is briefly described. It is concluded that replacing substantial volumes of ground geological and geophysical work with faster and more informative airborne geophysical technologies is appropriate, feasible, and effective.

The current development of airborne geophysical methods—in terms of both the number of information types acquired and the technical and metrological parameters of each survey channel—together with rapid coverage of large areas, relatively low costs compared with ground methods, and the ability to integrate aerial and satellite photographic data in unified GIS or other computer technologies, makes it possible to change the technological framework for applying airborne geophysics to practical geological investigation tasks. Instead of a “standard suite” of airborne methods preceding regional investigations and geological map compilation, with results often merely appended to geological reports, modern airborne geophysics enables many regions of the country to adopt integrated geological and airborne geophysical mapping, producing geological maps from high-quality integrated aerial survey data.

Instead of traditional “direct exploration” for deposits through local geophysical anomalies, with occasional unsystematic attempts to identify other predictive factors, modern airborne geophysical methods enable the development and use of targeted airborne geophysical predictive exploration technologies designed to efficiently discover specific economic deposit types in specific landscape and geological settings.

These circumstances enable the development of standard targeted airborne geophysical technologies for geological investigation, with every parameter directed towards solving specific practical tasks as cost-effectively as possible. In each case, the technology’s orientation towards the prescribed objective must be ensured by:

• targeted selection of the required work phases or stages, airborne geophysical methods and configurations, observation accuracy, and survey scales and altitudes for different stages;
• targeted methods of information processing and presentation, and a set of necessary supplementary data;
• a targeted workflow for general and predictive interpretation of the acquired information. According to the main types of geological investigation task, these technologies are subdivided into geological mapping, predictive exploration, engineering geology, hydrogeology, geoenvironmental investigations, and others, and are further classified by specific task (exploration for particular deposit types in particular landscape and geological settings, identification of specific environmental monitoring targets, etc.).

Let us consider and illustrate the general principles for developing targeted airborne geophysical predictive exploration technologies using exploration tasks as an example.

1. Exploration targets

As a first approximation, the target of any exploration programme is a mineral deposit that can be profitably exploited under specific geological and economic conditions. Closer examination of geological exploration technology (5), however, reveals that the investigation targets comprise a fairly large set of geological phenomena important to exploration, whose successive identification collectively defines the work technology itself.

In the figure, I denotes a target identified at early stages of diamond exploration: a “mineragenic zone.” Within a diamond-bearing province or subprovince, it is a linear or nearly linear geological structure within which all known kimberlite fields are located. Thus, in diamond exploration over sufficiently large territories, a diamond-bearing mineragenic zone constitutes an independent exploration target. After identifying an area prospective for a diamond-bearing mineragenic zone, the next exploration task is to delineate areas within it prospective for kimberlite fields (label II in the figure). Within these areas, exploration then seeks local sites prospective for clusters of kimberlite bodies (label III), and within those, detailed sites and anomalies prospective for the kimberlite pipes themselves (label IV).

Finally, the task is to select, from all discovered kimberlite bodies, those pipes whose diamond grade and quality provide the minimum acceptable profitability under the specific economic conditions of deposit exploitation (label V).

In this example, a minimally profitable deposit is the ultimate (1) exploration target, while the mineragenic zone, field, cluster, and pipe are intermediate exploration targets. The complete set of targets—in this case, diamond-bearing province, subprovince, diamond-bearing mineragenic zone, kimberlite field, pipe cluster, and pipe—forms a hierarchical series of intermediate exploration targets.

In light of the above, extending approach (6), an intermediate exploration target may be defined as a single, locally distinct, genetically unified, and conditionally homogeneous geological formation with specific mineral resource significance (metallogenic or mineragenic taxon), formed through the regular geological evolution of an area and expressed in components of the surrounding landscape and geological environment. It corresponds to a particular scale of variability in the distribution of a mineral in the Earth’s crust, concentrates all economically valuable reserves of that mineral at that scale, and is accordingly identified at a particular stage of the geological exploration process.

The identified intermediate targets allow exploration practice to employ two work schemes or various combinations of them. One is the staged investigation technology described above. The other is a shortened workflow that, under favourable conditions—strong diagnostic contrasts of exploration targets and a favourable landscape and geological environment—allows individual stages to be omitted. It is important to note that the shortened workflow is less reliable and, as a rule, is eventually replaced by a staged approach in any territory.

The right-hand part of the figure, III, schematically illustrates the technological features of identifying this intermediate target. Label III.а denotes geological phenomena reflecting the regularities of geological development in a specific territory that determined the location of the corresponding member of the hierarchical target series at a particular site (exploration prerequisites). Label III.b denotes geological phenomena reflecting specific properties of the exploration target expressed in the structure of its surrounding landscape and geological environment (exploration indicators). Thus, exploration prerequisites and indicators, as crucial factors in locating an intermediate target, themselves become exploration targets in turn. They may be called specific exploration targets.

Exploration prerequisites used in practice are highly diverse. They are geological regularities manifested at different compositional levels of an area’s structure: geochemical, mineralogical, petrographic (or lithological), formational, and tectonic (labels “1”, “2”, … “n” in the figure). The manifestations of exploration indicators—geological phenomena directly or indirectly indicating the presence of an intermediate exploration target, or more precisely the fact of its formation—are likewise diverse (labels “1”, “2”, … “k”). They are expressed in the same individual geological fields or static geological systems [3].

An important property of static geological systems is the material expression of regularities inherent in higher-level systems (for example, formational or tectonic systems) within relatively lower-level systems (for example, mineralogical or geochemical systems). Thus, a transition from a terrigenous to a carbonate formation is clearly reflected in changes in rocks, rock-forming and accessory minerals, and the concentrations of virtually all chemical elements. Consequently, virtually all exploration prerequisites and indicators can be clearly expressed in the simplest compositional geological fields: geochemical, mineralogical, and petrographic.

By its nature, variability in geophysical fields reflects, to differing degrees, variability in simple geological fields and, through them, exploration prerequisites and indicators with varying contrast.

Figure 2 gives an example of identifying tectonic prerequisites for bauxite occurrence from airborne geophysical surveys at 1:25000 over approximately 3000 km² . The essence of these prerequisites lies in the association of the zone of the most

The upper part of the figure (“а”) shows a map of the regional magnetic field component with a contour interval of 5 nT. Analysis of the map allows a boundary between major blocks to be traced from top to bottom through the middle of the area. Figure 2b shows the outcrop traces on the erosion surface of a Lower Carboniferous horizon mapped through analysis of the local magnetic field component owing to its elevated magnetization.

Mean intensities of this local anomaly were calculated in a window of 40–50 flight-line intersections, followed by statistical assessment of the significance of the differences. Intensities rounded to integers are shown in circles in nT. Spatial analysis of this parameter clearly shows a difference between the left (5 nT) and right (3 nT) parts of the figure. The boundary between these parts coincides with sufficient accuracy with the boundary between blocks identified from the regional field component.

Without discussing here the details of the compositional interpretation of the causes of spatial variability in the local component, it can be concluded that a statistically significant difference in the thickness variability of the constituent rocks was recorded for the stratigraphic interval mentioned above. The coincidence of the boundary of changing values with the boundary of the identified blocks indicates the evident influence of the latter on the sedimentation regime. Figure 3с shows a schematic palaeotectonic model of the area constructed from the airborne geophysical data discussed.

The boundary zone between the blocks was identified as prospective for the highest-quality bauxite ores.

Unlike the numerous examples in the literature with clear, strongly contrasting expressions of particular exploration prerequisites and indicators, this example illustrates a case in which identifying an exploration factor required a fairly complex sequence of operations. Yet it demonstrates the potential to greatly increase the number of predictive factors identified from airborne geophysical data when surveys use modern technical and metrological capabilities and an appropriate targeted analysis and interpretation workflow. In this case, the boundary between blocks served as a specific exploration target.

2. Landscape and geological conditions of exploration

Existing technical instructions for airborne geophysical surveys do not require dedicated investigation and mapping of landscape and geological exploration conditions for any survey type. However, the expressions of the diverse exploration targets discussed above in physical fields measured at the ground surface or along flight lines vary substantially with the landscape and geological characteristics of the environment. The signal from an exploration target may be completely screened by environmental components or significantly distorted.

In the survey figure. The following landscape types are distinguished:

eluvial-autonomous (I in the figure); transeluvial (II ); eluvial-accumulative (III ); subaqueous (IV); superaqueous (V).

For magnetometry, the transition to high-accuracy observation systems sharply increased the number of local anomalies associated with redistribution of magnetic minerals in Quaternary deposits (curve а in the figure). Magnetic minerals released by bedrock weathering in eluvial-autonomous landscapes very often form enriched streaks in transeluvial landscapes and lenses and aprons in eluvial-accumulative landscapes. Within aquatic landscapes, minerals transported from other parts of the area likewise form streaks and lenses. Where signal contrast is low, anomalies from near-surface concentrations of magnetic minerals are often difficult to distinguish from target anomalies using magnetic field analysis alone.

For electromagnetic prospecting (curve b), the groundwater-table position is an important landscape characteristic. Its shallow occurrence in accumulative landscapes substantially reduces the electrical resistance of bedrock, appearing on geoelectric parameter maps as increased differentiation of the investigated fields.

In most situations studied, landscape and geological heterogeneities in the upper section have their strongest effect on the spectral components of the gamma field (curve с). Factors include downslope and streamwise displacement of natural radioactive element halos and shielding of the gamma field by water and vegetation. In our view, the failure of many practitioners to apply landscape-based interpretation principles to airborne gamma-ray survey data is what currently constrains the development of airborne gamma-ray spectrometry for mapping and identifying low-contrast radiogeochemical halos and anomalies.

On the other hand, the effects of landscape and geological environmental components on aerial survey results described above may appear insurmountable only at first glance. Experience in different parts of Russia shows that most data needed to identify and quantitatively account for landscape and geological heterogeneities in the upper section can be obtained from satellite and aerial photographs, topographic, geological, geomorphological, and Quaternary deposit maps, and similar sources. Successfully suppressing background field variations associated with near-surface characteristics requires dedicated mapping of landscape and geological exploration conditions for each exploration method (5).

3. Work stages and survey scales

Current practice in applying airborne geophysical methods to geological exploration generally uses binary-decimal survey scales: they are always multiples of 10, 100, 1000, etc., and each successive scale is obtained by dividing the preceding one by 2. In our view, these scale conventions are associated with the widespread decimal measurement system (the number of fingers on human hands), and are far from universally optimal for exploration. Exploration targets and the anomalies they produce have specific dimensions that, in most known cases, do not follow a binary-decimal measurement system. This leads either to insufficient exploration-grid density or to excessive expenditure and a prolonged exploration process. Thus, in addition to the overall list of intermediate and specific exploration targets and consideration of landscape and geological influences, investigation effectiveness depends on their spatial dimensions and the contrast of their diagnostic properties (magnetization, electrical conductivity, natural radioactivity, etc.).

Our earlier analysis (5) of the variability of these characteristics among different exploration targets showed that the dimensions and property contrasts of specific targets are most often described by (Fig.

The same figure shows the general form of a truncation function for the target population under a particular exploration workflow. This curve gives the probability of detecting a target (vertical axis) of a particular size or with a particular diagnostic-property contrast (horizontal axis). It is readily apparent that the curve divides the exponential and normal target populations shown into two parts:

• a right-hand part, where truncation-function values are close to 1 (all exploration targets with the corresponding size or property contrast are guaranteed to be detected);
• a left-hand part, where function values are below 1 (some targets are missed, inversely related to size or property contrast). The relationship shown between the truncation function and the specific distribution types is schematic. With a denser grid or a more reliable exploration technology, the upper inflection point of the function moves increasingly closer to the vertical axis. In practically all cases, however, it remains separated from that axis: there will almost always be a group of exploration targets with a detection probability below 1.

These data support an important practical conclusion: any population of exploration targets always contains a few with relatively extreme anomalous dimensions or contrasts. Such targets may be termed indicator targets of a metallogenic taxon. Clearly, where an area’s ore-bearing potential has not been demonstrated, exploration should seek precisely these indicator targets, using a relatively sparse observation system. Only after such targets have been discovered should exploration proceed over part of the original area with denser grids or a broader suite of methods.

Statistical analysis of Yakutian kimberlite fields identified specific parameters of indicator targets for kimberlite fields and pipe clusters within them, in terms of dimensions, magnetic moment, and mineralogical productivity. These parameters proved highly stable throughout the province or varied according to fairly simple patterns. Data were also obtained on the variability of dimensions and diagnostic characteristics of many intermediate and specific exploration targets. Together, these data enable a transition from binary-decimal exploration grids to dedicated, targeted sequential (staged) observation systems adapted both to the parameters of the entire target population and to the effects of components of the surrounding landscape and geological environment.

4. Data interpretation, prediction, and assessment of result reliability

The widespread use of GIS technologies in geological and geophysical data analysis and interpretation has also led to widespread adoption in mineral deposit prediction of a specific methodological approach that may provisionally be termed image-based (from “image”).

This involves widespread conversion of geological and geophysical information into colour images, overlaying different datasets as colour combinations, and visually searching for patterns. In doing so, distinctions between the natures of geological and geophysical fields are often blurred. Without disputing the potential effectiveness of this approach, particularly for relatively simple predictive exploration tasks, it is important to consider the principles of developing computer-based prediction technologies using a logical and semantic approach.

In this approach, each metrically reliable geological phenomenon expressed in some form in measured or described geological and geophysical fields must first receive a semantic classification in geological or landscape and geological terms. Logical relationships are established between these phenomena, such as “above–below,” “younger–older,” and “bedrock–landscape.” The result is maps of the geological and landscape-geological structure of the area.

The next stage of processing and interpretation is landscape-geological reduction of the measured fields. Through an iterative scheme for solving forward and inverse problems, all metrically reliable elements that have received satisfactory geological interpretation are removed from the observed field. Removing evident contributions sharply reduces previously unaccounted-for “geological variations in background fields,” enabling low-contrast geophysical anomalies to be identified and mapped. These, in turn, can also be interpreted geologically and incorporated into geological and landscape logical-semantic models.

The final interpretation step is the transition from geological and landscape-geological models to purpose-specific logical-semantic models. Here, all geological phenomena described during interpretation and unexplained elements of geophysical fields are classified into the following predictive exploration categories:

· prerequisites for predicting one or more specified intermediate exploration targets;

· direct or indirect indicators of one or more specified intermediate exploration targets;

· phenomena that complicate identification of specific exploration targets (statistical, physical, or dynamic screening);

· phenomena with no predictive value that do not affect the extraction of information useful for achieving the objective. A predictive map is then compiled for the work area, spatially depicting the recorded prerequisites and indicators of intermediate targets: a predictive map for the corresponding work phase or stage.

For each identified predictive factor, a spatial model is constructed to assess the reliability of its identification; the set of models provides a complete map of exploration coverage reliability for the area.

In the figure

In part of the figure

In the figure

Figure 5с illustrates the results of airborne gamma-ray spectrometric prospecting of the area for the intermediate target mentioned above.

Interestingly, comparison of Figure 2с (bauxite exploration prerequisites) with Figure 5с (exploration indicators) readily reveals the spatial coincidence of predictive factors for the bauxite-bearing zone. Their coincidence therefore identifies an area prospective for the intermediate target mentioned above, allowing work within it to proceed to the next member of the hierarchical intermediate-target series: a bauxite-bearing deposit.

Conclusion

In our view, these data indicate that all necessary prerequisites—theoretical, technical and metrological, geological interpretation, interpretation software and hardware, and others—now exist for transferring substantial, costly volumes of geological investigation in general, and mineral exploration in particular, from ground methods to airborne geophysical execution. The principal route is the development and use of standard targeted airborne geophysical technologies for geological investigation. In addition to the advantages already described—speed, relatively low cost, environmental compatibility, and others—there is the rapid resolution of the overwhelming majority of geological investigation tasks over areas of any size, substantially shortening the investment period for specific tasks and territories.

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