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Geologic carbon storage in saline acquirers is a feasible and scalable way of reducing atmospheric carbon dioxide (CO2). Since 2022, Denmark has stepped up site characterization at five suitable onshore locations for this purpose with a particular focus on four-way domal structures. A dual-element recording system incorporating landstreamer and wireless recorders was innovated and upscaled in a cost- and time-effective way for this purpose at the R & oslash;dby structure (one of the five sites). The dual-element recording allows a better imaging of the near-surface structures but also because of its broadband nature, it helps to retain higher resolution for imaging toplap structures and smaller faults in the area. The landstreamer data better image fault structures offsetting the Bunter Sandstone Formation, which is the primary reservoir, and the overlying geological seals such as Fjerritslev, & Oslash;rslev and Falster Formations. The Vedsted Formation, which a secondary seal in the region, appears unfaulted in the landstreamer data. The landstreamer data also reveal glacial valleys, near the surface, which are a source of groundwater. The two complementary datasets, from the landstreamer and nodal data, help to de-risk geological carbon storage in Denmark and is a solution we recommend to be adapted for onshore sites elsewhere in the world specially where the logistical acquisition challenges are not significant.

In response to the rising needs for long-term research and innovation in the field of critical raw material exploration, the Smart Exploration Research Centre was established in 2024 in Sweden. Funded by the Swedish Foundation for Strategic Research (SSF), this initiative involves collaboration among academic institutions, industry, and the public sector. Building on the H2020-funded Smart Exploration project, which involved 27 European organisations including the European Association of Geoscientists and Engineers (EAGE), the centre aims to advance the global standing of Sweden’s exploration. It seeks to gather skills and create a network that will leave a lasting legacy in the field of mineral exploration. The multidisciplinary centre aims to be a fast-track hub for addressing exploration challenges in the mining industry through synergistic efforts. It connects exploration with mineral processing and nanotechnology to enhance environmental studies and develop effective extraction and beneficiation methods.

Diffraction wavefield contains valuable information on subsurface composition through velocity extraction and sometimes anisotropy estimation. It can also be used for the delineation of geological features, such as faults, fractures and mineral deposits. Diffraction recognition is, therefore, crucial for improved interpretation of seismic data. To date, many workflows for diffraction denoising, including deep-learning applications, have been provided, however, with a major focus on sedimentary settings or for ground-penetrating radar data. In this study, we have developed a workflow for a self-supervised learning technique, an autoencoder, for diffraction denoising on synthetic seismic, ground-penetrating radar and hardrock seismic datasets. The autoencoder provides promising results especially for the ground-penetrating radar data. Depending on the target of the studies, diffraction signals can be tackled using the autoencoder both as the signal and/or noise when, for example, a reflection is a target. The real hardrock seismic data required additional pre- and post-autoencoder image processing steps to improve automatic delineation of the diffraction. Here, we also coupled the autoencoder with Hough transform and pixel edge detection filters. Along inlines and crosslines, diffraction signals have sometimes a similar character as the reflection and may spatially be correlated making the denoising workflow unsuccessful. Coupled with additional image processing steps, we successfully isolated diffraction that is generated from a known volcanogenic massive sulphide deposit. These encouraging results suggest that the self-supervised learning techniques such as the autoencoder can be used also for seismic mineral exploration purposes and are worthy to be implemented as additional tools for data processing and target detections.

Seismic investigations were performed at a site in the southwest of Sweden where major quick-clay landslides have occurred in the past. Given the potential high risk of the area and the presence of medium infrastructures, the site posed a need for detailed investigations in a wide depth range and in high resolution. A high-fold seismic survey was designed and conducted along two profiles using a 1–2 m receiver and shot spacing in order to retrieve both P- and S-wavefield seismic images from vertical component data. The data were analysed by combining first-break traveltime tomography and surface-wave analysis as well as P- and S-wavefield reflection seismic imaging. Using the first breaks, P-wave velocity (VP) models were estimated, indicating the bedrock topography along the profiles and the sediment characteristics. The S-wave velocity (Vs) models were estimated from the surface waves and indicated areas of low shear strength. Combined with VP and Vs models, this permits the estimation of VP/VS, a parameter that can indicate areas with high water content, significant for the detection of quick clays and possible liquefaction issues. The results are integrated with the P- and S-wave reflection seismic images and compared with other geophysical investigations, such as magnetic and gravity data that were collected along the profiles.

Diffraction imaging can help better understand small-scale geological structures. Due to their often-weak signal, in order to image them, it is necessary to separate diffraction signals from the rest of the wavefield. Many different methods have been developed for diffraction wavefield separation, and the newest trend includes the application of artificial neural networks and deep learning. Available case studies with a deep-learning approach for diffraction separation show good results when applied to synthetic and sedimentary setting datasets where diffraction signals are either strong or have pronounced characteristics. Examples, however, are missing from crystalline or hardrock geological settings where the signal-to-noise ratio is by far lower and diffraction signals are usually within a complex reflectivity medium, have steep tails and are usually incomplete. In this study, we showcase the application of a deep semantic segmentation model on synthetic seismic, real ground-penetrating radar, and hardrock seismic datasets. Synthetic seismic sections were generated using different random noise levels and coherent noise resembling a complex reflectivity pattern interfering with diffraction tails. For the real GPR dataset, diffraction signals were successfully delineated, although in some locations reflections were picked up because of their similar pixel values as the apex of the diffractions. As for the real seismic dataset, through a number of approaches, we were able to completely delineate a single diffraction within several inlines that was generated from a massive sulphide body. The algorithm also enabled us to recognize an incomplete diffraction, at the edge of the seismic cube, which was never labelled. This diffraction originated from outside of the seismic volume and may be a target for future mineral exploration programmes, thanks to the deep semantic segmentation algorithm providing this possibility.

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.

We present pre-stack depth-imaging results for a case study of 3D reflection seismic exploration at the Blotberget iron oxide mining site belonging to the Bergslagen mineral district in central Sweden. The goal of the study is to directly image the ore-bearing horizons and to delineate their possible depth extension below depths known from existing boreholes. For this purpose, we applied a tailored pre-processing workflow and two different seismic imaging approaches, Kirchhoff pre-stack depth migration (KPSDM) and Fresnel volume migration (FVM). Both imaging techniques deliver a well-resolved 3D image of the deposit and its host rock, where the FVM image yields a significantly better image quality compared to the KPSDM image. We were able to unravel distinct horizons, which are linked to known mineralization and provide insights on their possible lateral and depth extent. Comparison of the known mineralization with the final FVM reflection volume suggests a good agreement of the position and the shape of the imaged reflectors caused by the mineralization. Furthermore, the images show additional reflectors below the mineralization and reflectors with opposite dips. One of these reflectors is interpreted to be a fault intersecting the mineralization, which can be traced to the surface and linked to a fault trace in the geological map. The depth-imaging results can serve as the basis for further investigations, drilling, and follow-up mine planning at the Blotberget mining site.

Many metallic mineral deposits have sufficient physical property contrasts, particularly density, to be detectable using seismic methods. These deposits are sometimes significant for our society and economic growth and can help to accelerate the energy transition towards decarbonization. However, their exploration at depth requires high-resolution and sensitive methods. Following a series of 2D seismic trials, a sparse, narrow source-receiver azimuth, 3D seismic survey was conducted in the Blotberget mine, in central Sweden, covering an area of approximately 6 km(2) for deep-targeting iron oxide deposits and their host rock structures. The survey benefited from a collaborative work by putting together 1266 seismic recorders and a 32 t vibrator, generating 1056 shot points in a fixed geometry setup. Shots were fired at every 10 m where possible, and receivers were placed at every 10-20 m. Notable quality data were acquired despite the area being dominated by swampy places as well as by built-up roads and historical tailings The data processing had to overcome these challenges for the static corrections and strong surface waves in particular. A tailored for hardrock setting and processing workflow was developed for handling such a dataset, where the use of mixed 2D and 3D refraction static corrections was relevant. The resulting seismic volume is rich in terms of reflectivity, with clear southeast-dipping reflections originating from the iron oxide deposits extending vertically and laterally at least 300 m beyond what was known from available boreholes. As a result, we estimate potential additional resources from the 3D reflection seismic experiment on the order of 10 Mt to be worth drilling for detailed assessments. The mineralization is crosscut by at least two major sets of northwest-dipping reflections interpreted to dominantly be normal faults and to be responsible for much of the lowland in the Blotberget area. Moreover, these post-mineralization faults likely control the current 3D geometry of the deposits. Curved and submerged reflections interpreted from folds or later intrusions are also observed, showing the geological complexity of the study area. The seismic survey also delineates the near-surface expression of a historical tailing as a by-product of refraction static corrections, demonstrating why 3D seismic data are so valuable for both mineral exploration and mine planning applications.

Reflection seismic data were acquired within two field campaigns in the Blötberget, Ludvika mining area of central Sweden, for deep imaging of iron-oxide mineralization that were known to extend down to 800-850 m depth. The two surveys conducted in years 2015 and 2016, one employing a seismic landstreamer and geophones connected to wireless recorders, and another one using cabled geophones and wireless recorders, aimed to delineate the geometry and depth extent of the iron-oxide mineralization for when mining commences in the area. Even with minimal and conventional processing approaches, the merged datasets provide encouraging information about the depth continuation of the mineralized horizons and the geological setting of the study area. Multiple sets of strong reflections represent a possible continuation of the known deposits that extend approximately 300 m further down-dip than the known 850 m depth obtained from historical drilling. They show excellent correlation in shape and strength with those of the Blötberget deposits. Furthermore, several reflections in the footwall of the known mineralization can potentially be additional resources underlying the known ones. The results from these seismic surveys are encouraging for mineral exploration purposes given the good quality of the final section and fast seismic surveys employing a simple cost-effective and easily available impact-type seismic source.