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In this paper, we use a new workflow to substantiate the characterization of a prominent, deep sediment conductor in the hyperextended Bjørnøya Basin (SW Barents Sea) previously identified in smooth resistivity models from 3-D deterministic inversion of magnetotelluric data. In low-dimensionality environments like layered sedimentary basin, 1-D Bayesian inversion can be advantageous for a thorough exploration of the solution space, but the violation of the 1-D assumption has to be efficiently handled. The primary geological objectives of this work is therefore preceded by a secondary task: the application of a new machine learning approach for handling the 1-D violation assumption for 21 MT field stations in the Barents Sea. We find that a decision tree can adequately learn the relationship between MT dimensionality parameters and the 1-D–3-D residual response for a training set of synthetic models, mimicking typical resistivity structures of the SW Barents Sea. The machine learning model is then used to predict the dimensionality compensation error for MT signal periods ranging of 1–3000 s for 21 receivers located over the Bjørnøya Basin and Veslemøy High. After running 1-D Bayesian inversion, we generated a posterior resistivity distribution for an ensemble of 6000 1-D models fitting the compensated MT data for each 21 field stations. The proportion of 1-D models showing ρ < 1 Ω·m is consistently beyond 80 per cent and systemically reaches a maximum of 100 per cent in the Early Aptian–Albian interval in the Bjørnøya Basin. In hyperextended basins of the SW Barents Sea, the dimensionality compensation workflow has permitted to refine the characterization of the deep basin conductor by leveraging the increased vertical resolution and optimal used of MT data. In comparison, the smooth 3-D deterministic models only poorly constrained depth and lateral extent of the basin anomaly. The highest probability of finding ρ < 1 Ω·m is robustly assigned to the syn-tectonic Early Aptian–Albian marine shales, now buried at 6–8 km depth. Based on a theoretical two phase fluid-rock model, we show that the pore fluid of these marine shales must have a higher salinity than seawater to explain the anomaly ρ < 1 Ω·m. Therefore, the primary pore fluid underwent mixing with a secondary brine during rifting. Using analogue rift systems in palaeomargins, we argue that two possible secondary brine reservoir may contribute to deep saline fluid circulation in the hyperextended basin: (1) Permian salt-derived fluid and, (2) mantle-reacted fluid from serpentinization. [ABSTRACT FROM AUTHOR]

Marine seismic data is often missing near offset information due to separation between the source and receiver cables. To solve this problem, a convolutional neural network is trained on synthetic seismic data to reconstruct the near offset gap. The synthetic data is created using a two‐dimensional finite difference method within a heterogeneous velocity model. These synthetics are generated with a source‐over‐receiver acquisition geometry so that they contain complete near offset data. The convolutional neural network is then trained on input‐target synthetic pairs where the inputs are common midpoint gathers with the near offset section removed, and the targets are the same gathers with the near offset section retained. Following training, the robustness of the method is investigated with regards to common midpoint data sorting, normal moveout correction and changes in the velocity model. It is found that training on common midpoint‐sorted data results in 2.8 times lower error than training on shot gathers, that normal moveout correction of the training data makes no significant difference in error levels, and that the model can reconstruct realistic near offsets on synthetic data generated 10 km away within the heterogeneous velocity model. In field data testing, first a dataset with source‐over‐cable acquisition geometry from the Barents Sea is used to compare the reconstructed wavefields to ground truth values. Although the reconstructed amplitudes require minor scaling to match the true values, predictions on this dataset yield 2.5 times lower near offset reconstruction error compared to a simple Radon transform interpolation method. Furthermore, amplitude versus offset gradient and intercept sections from the Barents Sea dataset are estimated with half the error when including the convolutional neural network‐predicted near offset data, compared to only using the conventionally‐acquirable portion of the data (beyond 112.5 m of offset). In a secondary field data test, a conventional northern North Sea dataset is used to demonstrate how the method may be applied in practice. Here, the convolutional neural network generates more realistic predictions than the Radon method, and the gradient and intercept sections calculated using the convolutional neural network‐predicted traces have higher signal‐to‐noise ratios than the sections calculated using only the original data. The combination of high‐quality synthetic training data and interpolation in the common midpoint domain enables near offset reconstruction at significant depth (1 s of two‐way traveltime or more), which is demonstrated in both synthetic and field examples. [ABSTRACT FROM AUTHOR]

Geologically, the Arctic is one of the least explored regions of Earth. Its significance, in terms of indigenous populations, resource extraction, tourism, shipping and a rapidly changing climate, is increasing. The Arctic offers geological diversity encompassing onshore and offshore environments, include active subduction zones in Alaska, deep sedimentary basins on the Siberian and Barents Sea shelves, widespread ancient Arctic volcanism and magmatism, the world's slowest spreading mid-ocean ridge (Gakkel Ridge in the Eurasia Basin), as well as world-class examples of extensional and compressional basins exposed onshore Svalbard. Obtaining data is logistically, economically and environmentally expensive in the high Arctic, but the township of Longyearbyen at 78° N represents a relatively easily accessible gateway to Arctic geology. The year-round settlement on Spitsbergen, the main island of the Svalbard archipelago is home to The University Centre in Svalbard (UNIS). Reached by a year-round airport with regular connections to mainland Norway, Svalbard provides a foundation from which to teach and explore Arctic geology via the classroom, the laboratory, and the field. In this contribution, we present a new graduate course (Masters and PhD level) on Arctic Tectonics and Volcanism that we have established and taught annually at UNIS since 2018. We outline the course itself, before presenting student perspectives based on both an anonymous questionnaire (n = 27) and in-depth perceptions of four selected students. The course, with an intake of up to 20 MSc and PhD international students, is held over a 6-week period, typically in Spring or Autumn. The course comprises modules on field and polar safety, Svalbard/Barents Sea geology, wider Arctic geology, plate tectonics, mantle dynamics, geo- and thermochronology, and geochemistry of igneous systems. All modules include individual and group-based exercises in addition to introductory lectures. A field component, which in some years included an overnight expedition, provides an opportunity to appreciate Arctic geology and gather own field observations and data. Digital outcrop models and photospheres viewed with state-of-the-art visualization in the classroom facilitate efficient fieldwork through pre-fieldwork preparation and post-field work quantitative analyses. The course assessment is centered on an individual research project that is presented orally and in a short and impactful Geology journal-style article. Apart from the course at UNIS we have jointly initiated several one-off research and education-based events at partner institutions, and briefly outline these. [ABSTRACT FROM AUTHOR]

For the first time, this study presents interpretations of alluvial fans in spectral decomposition RGB Blends, analysed in seismic time slices from the Middle Triassic to Lower Jurassic stratigraphical interval of the Horda Platform (northern North Sea). The time slices record a shifting alluvial fan front, fluvial variability, uplift and erosion and reveals depositional elements that may set the common conception of the geological development of this area during the Early–Middle Mesozoic up for discussion. Results show that the Upper Triassic Lunde Formation in the eastern margin of the Horda Platform was characterized by the deposition of coalesced alluvial fans. A variable extent of the fan front through the Upper Triassic is linked to interplaying allogenic factors: uplifted source areas in the aftermath of Early–Middle Triassic rifting determined sediment availability; climate transitioning from arid to semi‐humid, with increasingly fluctuating precipitation, controlled sedimentation, and run‐off; provenance dictated bulk sedimentology and affected prevailing alluvial processes. An overall retreat of the fan system through the Late Triassic coincided with a significant change in landscape characteristics at the transition into the overlying Statfjord Group. Uplift and initial tilting of the Horda Platform caused landscape degradation and the formation of plateaus and incised valleys, contemporaneous with increased humidity and marine transgression, forming estuaries. The shift in depositional style has implications for reservoir properties, creating complexity and heterogeneity in terms of facies distribution and connectivity, which may benefit potential CO2 storage. Upper Triassic alluvial fan development in the Horda Platform (northern North Sea) depicted through spectral decomposition of seismic time slices Enhanced level of seismic stratigraphic interpretation with RGB blending Impact of allogenic factors on alluvial depositional development Implications of alluvial variability on reservoir properties [ABSTRACT FROM AUTHOR]