Geophysics aims to image subsurface geologic structure and identify different geologic units. While the former has dominated the interpretation of applied geophysical data, the latter has received much less attention. This appears to have persisted despite applications such as those in mineral exploration that inherently rely on the inference of geologic units from geophysical and geologic observations. In practice, such activities are routinely carried out in a qualitative manner. Thus, it is meaningful to examine this aspect and to develop a system of quantitative approaches to identify different geologic units. The development of geophysical inversions in the last three decades makes such interpretation tools possible. We refer to this newly emerging direction as geology differentiation and the resultant representation of geology model as a quasi-geology model. In this article, we will provide an overview of the historical background of geology differentiation and the current developments based on physical property inversions of geophysical data sets. We argue that integrating multiple physical property models to differentiate and characterize geologic units and work with the derived quasi-geology model may lead to a step change in maximizing the value of geophysical inversions.
Mineral exploration dynamics often requires an efficient and objective means of evaluating a prospect in early exploration stages, when few holes have been drilled. In the case of deep prospects or prospects under cover, this evaluation will mostly be based on geophysical data. To develop an objective interpretation method capable of combining all the information available, we have developed an integrated interpretation scheme of geophysical models and sparse geologic data. Our method is based on the relationship between recovered physical properties obtained from 2D and 3D inversions, aiming to find patterns associated with geologic units, such as iron formation, copper ore, and host rock. The interpretation is guided by theoretical relations of the minerals of interest (chalcopyrite and magnetite) and the sparse geologic information available. It is suitable for prospects in the initial stages of exploration when only limited mineralogical information is available from, say, one drillhole. We have demonstrated the success of the method using magnetic and DC resistivity data from the Cristalino iron oxide copper-gold deposit, located in northern Brazil, which is covered by a thick soil overburden. The theoretical behavior of the physical properties of chalcopyrite and magnetite was first combined with the rock types identified in the drill cores to find groups or classes associated with different amounts of these minerals. Then, these relative relations between units were applied to define four classes in the scatterplot of recovered susceptibility and conductivity values from 2D inversions. These four classes are associated with iron formation, copper ore, and two types of host rocks. After the validation with the known geology, the same interpretation scheme was applied to the scatterplot of recovered susceptibility and conductivity values from 3D inversions. The final interpreted volume allows the explorationist to have an approximate estimate of the copper body extent.
Mineral exploration under a thick sedimentary cover naturally relies on geophysical methods. High-resolution airborne magnetic and gravity gradient data were acquired over Northeast Iowa to characterize the geology of the concealed Precambrian rocks and to evaluate the prospectivity of mineral deposits. Previous researchers interpret the magnetic and gravity gradient data in the form of a 2D geological map of the Precambrian basement rocks, which provides important geophysical constraints on the geological history and mineral potentials over the Decorah area located in the northeast of Iowa. However, their interpretations are based on 2D data maps and limited to the two horizontal dimensions. To fully tap into the rich information contained in the high-resolution airborne geophysical data, and to further our understanding of the undercover geology, we have performed both separate and joint inversions of magnetic and gravity gradient data to obtain 3D density contrast models and 3D susceptibility models, based on which we carried out geology differentiation. Based on separately inverted physical property values, we identified 10 geological units and their spatial distributions in 3D which are all summarized in a 3D quasi-geology model. The extension of 2D geological interpretation to 3D allows for the discovery of four previously unidentified geological units, a more detailed classification of the Yavapai country rock, and and the identification of the highly anomalous core of the mafic intrusions. Joint inversion allows for classification of a few geological units further into several sub-classes. Our work demonstrates the added value of the construction of a 3D quasi-geology model based on 3D separate and joint inversions.
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