Seismic Exploration Solutions for Deep-Targeting Metallic Mineral Deposits : From high-fold 2D to sparse 3D, and deep-learning workflows

Mineral exploration has in recent years moved its focus to greater depths than ever before, particularly in brown fields. Exploring new deposits at depth, if economical, would not only expand the life of mine but also provide minimal environmental impacts. It allows the existing mining infrastructures to be used for a longer period. Exploration at depth, however, is challenging and requires a multidisciplinary team and methods, and innovative thinking for generating new targets and effective exploration expenditure. The application of seismic methods for mineral exploration has increasingly been conducted over the past 20 years because they provide high-resolution subsurface images, and retain good resolution with depth as compared with other geophysical methods. Nevertheless, and despite challenges in hardrock settings, only limited attention has been given to seismic interpretations, often performed subjectively. With the growing application of machine-learning solutions, hardrock seismic data can benefit these for improved interpretations and target generations. This thesis showcases different workflows developed for deep-targeting metallic mineral deposits, starting from high-fold 2D, through sparse 3D reflection imaging and the implementation of deep-learning algorithms for diffraction pattern recognitions. Three different deposits were studied from Sweden and Canada. The Blötberget iron-oxide mineralization in central Sweden was first targeted in 2D, followed-up, a sparse 3D dataset was acquired enabling to image the mineralization both laterally and with depth, providing good knowledge on subsurface structures controlling the geometry of the deposits. In Canada, Halfmile Lake and Matagami mining sites were studied due to the accessibility to 3D seismic datasets, which contained diffraction signals as deposit responses. Deeplearning algorithms were utilized for the proof-of-concept and at the same time helped to generate new potential targets from other diffraction signals that were not obvious to an interpreter’s eye due to their incomplete tails originated outside of the seismic volume. The studies in this thesis show the effectiveness of seismic methods for mineral exploration at depth, especially in 3D, as they provide, among others, structural interpretation for future mineplanning purposes. Deep-learning solutions provide improved results for diffraction delineation and denoising and have great potential for hardrock seismics.

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