Logo: to the web site of Uppsala University

Natural resources, such as mineral deposits and groundwater in particular, are crucial for our society, as the world prepares itself for a smooth transition towards green technologies and decarbonization. Apart from extraction and use, innovative mineral exploration solutions are needed to complete the full value chain and to achieve the sustainable development goals.  

The application of seismic methods for both near-surface environmental and deep mineral exploration investigations is known, but high costs are associated with data acquisition and processing. In order to illustrate the potential of the seismic methods for efficient exploration of groundwater and mineral resources, cost-effective seismic surveys were acquired within two locations in Finland and Sweden, for aquifer delineation and imaging of iron-oxide mineralization in a hardrock environment, respectively. Physical properties, obtained from geophysical downhole logging and laboratory measurements, were analyzed for a complete characterization of the mineralization and its host rocks. 3D ray-tracing and 2D finite-difference forward modeling were carried out for better assessing the seismic response of the mineralization.

The effectiveness of these seismic surveys was revealed by the quality seismic data acquired using a low-cost, easily operated seismic source and different sensors, including a broadband seismic landstreamer. In particular, the seismic source provided adequate penetration in two different and challenging environments, namely soft glacial sediments at Virttaankangas, southwest Finland, and swampy glacial cover at Blötberget, south central Sweden. The large-scale units of the Virttaankangas aquifer were successfully delineated and integrated with the hydrogeological units of the groundwater flow model. The mineralization at Blötberget was interpreted to further extend 300-400 m downdip, below the currently known depth from borehole observations. 3D processing of the 2D seismic profiles revealed a lateral extent of least 300 m, providing encouraging results for improved assessments of the mineral resources. The reflection pattern validated through forward modeling, suggested a possible new mineralized horizon below the known deposits. Physical property studies helped characterize the mineralization and its host rocks in terms of seismic attenuation and rock quality. Fracture zones detected through sonic full-waveform logging were associated with high seismic attenuation, suggesting low mechanical competence of the mineralized rocks despite good rock quality designation, providing thus important information for mine planning and exploration.   

The studies presented in this thesis illustrate the potential of seismic methods and physical property studies for efficient natural resources exploration in crystalline rocks and in overlying glacial sediments.

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.