TsNIGRI Conference 2021. REGIONAL PREDICTION OF PORPHYRY COPPER MINERALIZATION USING GEOPHYSICAL DATA

REGIONAL PREDICTION OF PORPHYRY COPPER MINERALIZATION USING GEOPHYSICAL DATA
P. S. Babayants, B. A. Kalmykov, Aleksey Leonidovich
JSC GNPP Aerogeofizika

The methodology and results of regional prediction of porphyry copper ore targets at the ore-district and ore-cluster levels, based on integrated gravity and magnetic data analysis, are described.
Keywords: magnetometry, gravimetry, porphyry copper mineralization, integrated
interpretation, prediction.

This study describes a methodology for regional prediction of ore targets at the ore-district and ore-cluster levels based on analysis of integrated gravity and magnetic data from the GGK-1000/3 map set, drawing on experience gained in similar work on porphyry copper mineralization in the Far Eastern region [1]. The investigations formed part of work to compile a predictive geophysical base for the Amur region and Sikhote-Alin to predict porphyry copper and epithermal gold deposits by interpreting historical regional gravity and magnetic surveys.

The analysis used published and archived gravity and magnetic data for the Amur region and Sikhote-Alin at scales of 1:1000000 and larger, mainly freely available results of 1:200 000 surveys. These were used to compile combined gravity- and magnetic-field grids with a cell size of 2 × 2 km. Given the geographical location and considerable size of the study area, the magnetic field was reduced to the pole and an isostatic reduction was applied to the gravity field to eliminate the influence of external factors on field structure.

A digital terrain model was used as an additional information parameter. It was constructed using the international global GEBCO (General Bathymetric Chart of the Oceans) model, which incorporates SRTM (Shuttle Radar Topography Mission) data for land areas and bathymetric data for the World Ocean. GEBCO is a grid of terrain elevations in the WGS84 geographic coordinate system with cells of 30 arcseconds in latitude and longitude, corresponding to approximately 1 km in this area. At the regional investigation stage, with source-data grid cells of 2 × 2 km, analytical methods based on forward-modelling techniques appear to be of limited effectiveness, because the dimensions of elements defining the material controls on the target object may be comparable to, or even smaller than, the grid spacing. In this case, probabilistic and statistical analysis techniques should play a key role.

To establish patterns in the expression of factors controlling the localization of the target object, where a statistically representative sample of reference objects was available, overlaid histograms of the parameter under analysis were examined. These were constructed separately for the population (the parameter-distribution grid) and for the target object (parameter values at the reference locations).

A wide range of possible geophysical-field transforms was analysed, with preference given to those capable, to some extent, of emphasizing factors controlling the localization of the target object as identified from the literature [4, 5]. The following gravity- and magnetic-field transformations were calculated and used in subsequent analysis:

• 1. A surface enveloping the lower edges of magnetized objects, with elevations calculated using a specialized technique.;

• 2. Shraybman V. I., Zhdanov M. S., Vitvitsky O. V. Correlation Methods for the Transformation and Interpretation of Geophysical Anomalies. Moscow: Nedra. – 1977. – 237
• 3. Andrew S. Glassner, Deep Learning, Vol. 1: From Basics to Practice Nevertheless, a new strapdown gravimeter was tested in 2020; its weight and dimensions permit the use of a UAV as the airborne platform for gravity surveying. UAVs certainly have a future and enormous potential in airborne geophysics, just as, more broadly, the robotization of production is a logical expectation for the coming decades. The emergence of a new-generation strapdown gravimeter insensitive to vertical accelerations has made it possible to combine airborne gravity, magnetic and electromagnetic surveying in a single system, whereas the first two methods had previously been methodologically incompatible with airborne electromagnetic surveying. This is because airborne electromagnetic surveys require flight at a constant height above the ground, following the terrain, whereas vertical accelerations affected data obtained with previous-generation platform-mounted gravimeters. The new-generation strapdown gravimeter was tested in the Russian Arctic. Weighing 15 kg, it provides data of a quality comparable to that of the widely used GT gravimeter, which weighs 160 kg. Thus, given the increased choice of airborne platforms and modernization of instrumentation, airborne geophysical services for hydrocarbon exploration can be grouped into three main categories. The first, historically conventional category includes gravimetry and magnetometry and addresses regional and exploration objectives relevant primarily when an extraction company obtains access to a licence area, assesses its geological exploration strategy and begins searching for hydrocarbon deposits. The second category, in addition to airborne gravity and magnetic surveying, also includes airborne electromagnetic surveying, photography and laser scanning, addressing regional and exploration objectives as well as preparation for ground exploration work. A helicopter must be used as the airborne platform in this case. The third category serves the preparation and monitoring of geological exploration, with a suite of methods that permits the use of ultralight aircraft and UAVs as airborne platforms. Alongside the rapid development of airborne geophysical field operations and instrumentation, office-based processing and interpretation of the resulting data is a crucial stage that fundamentally affects the final product. Today, previously used processing and interpretation approaches cannot meet extraction companies’ requirements for the final results of airborne geophysical surveys as a component of oil and gas exploration. In view of this, airborne geophysical data-processing and interpretation algorithms have been improved. Interpretation focuses primarily on integrating and jointly analysing potential-field data with ground electromagnetic, seismic and drilling data. Processing requires an individual approach to each study region rather than a standardized one. For example, reducing the influence of the upper layer in gravity data is a complex task, rather than simply assigning a constant density to the upper part of the section. During processing, a terrain correction was conventionally applied using a constant mean density of 2.3 or 2.67 g/cm3. In reality, this is far from always the case. A methodology for constructing multilayer models has been developed for areas with complex terrain and lateral density variations in near-surface layers, allowing density variations in the upper section to be described in greater detail. Fitting the densities of upper-section layers may be iterative, and the final result is determined by the processing geophysicist and interpreter, taking account of prior data and general understanding of the geology of the study area. During interpretation, integrating airborne geophysical data with seismic and drilling data produces a consistent geophysical model that substantially reduces uncertainties in geological structure associated with lateral variations in the density, acoustic and geometric properties of sedimentary-cover deposits and basement. Interpretation comprises a set of algorithms, each addressing a specific task. To address interpretation problems that are difficult to formalize at the geophysical data-integration stage, the company’s engineers use advanced machine learning algorithms available as open-source libraries in the high-level programming language Python. This approach provides an individually optimized solution to a complex problem, taking account of the specific project, and has proved highly effective. Integrated interpretation results are used to construct and refine geological models, which in turn support quantitative estimates of hydrocarbon volumes and the formulation of strategies for further exploration. Considerable scope for development remains in both airborne geophysical data processing and integrated interpretation, primarily in conjunction with seismic and drilling data. Conclusion Technological development in the three main components of airborne geophysics—airborne platforms, instrumentation and software—has substantially increased the operational and economic efficiency of these methods in hydrocarbon exploration. Airborne geophysical surveying, processing and interpretation methods continue to evolve. New algorithms for exploration tasks are developed using experience accumulated in airborne geophysical surveys in Russia, as well as the experience of foreign oil and gas producers and airborne geophysical companies. Airborne geophysical surveys rapidly provide a vast dataset without affecting the environment, helping refine the geological structure and hydrocarbon potential of the study area. This subsequently allows more expensive seismic and drilling operations to be focused on the most prospective areas.

References:

• 1. Babayants P. S., Kontarovich R. S. Capabilities of modern airborne geophysical methods in studying areas prospective for oil and gas // Development of Oil and Gas Resources of the Russian Shelf: The Arctic and the Far East. Proceedings of the III International Conference. Moscow, VNIIGAZ, 2010, pp. 47–58;
• 2. Shraibman V. I., Zhdanov M. S., Vitvitsky O. V. Correlation Methods for Transforming and Interpreting Geophysical Anomalies. Moscow: Nedra. 1977. 237
• 3. Andrew S. Glassner, Deep Learning, Vol. 1: From Basics to Practice