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The Canary Archipelago comprises seven volcanic islands formed by the activity of the Canary mantle anomaly that might have been caused by an ascending plume at the NW-African passive margin. The “Basal Complex (BC)”, which contains the islands pre-shield rock formations, is exposed in the northwest and central Fuerteventura and NW-La Gomera and preserves the archive of giant landslides that caused the removal of most of the shield-stage volcanic rocks. Tools, like low-temperature thermochronology (LTT) are sensitive to rapid cooling activities that accompany landslides. In addition, integrating LTT data with time–temperature (t–T) numerical modelling are a powerful tool for reconstructing the thermo-tectonic evolution as well as defining and quantifying long-term landscape evolution in a variety of geological settings. To unravel part of the long-term landscape evolution of Fuerteventura and La Gomera, zircon and apatite fission-track, and (U–Th)/He data combined with t–T numerical modelling were applied to 39 samples representing the main rock units of the BCs and younger magmatic rocks on both islands. In Fuerteventura, the Northwest and Central Basal Complexes reveal rapid cooling/exhumation of more than 200 °C at ~ 20 Ma. The quantification of the thickness of the rock column using the t–T cooling path would need the knowledge of the palaeo-heat flow. The published thickness of the moved rock column in Fuerteventura and La Gomera does not point to an extreme high heat flow. Therefore, the formation of a giant landslide leads to the removal of ~ 2.0 (± 0.5) km of the volcano rock column. Offshore, such a landslide has led to part of the Puerto Rosario large debris avalanche. The “Central Basal Complex” revealed two more rapid cooling/exhumation events at ~ 16 Ma and ~ 14 Ma that might also be related to landslides. The three landslides might be responsible for the formation of the nowadays Puerto Rosario Debris Avalanche Unit offshore. What might have caused the landslides in Fuerteventura. Age data published provide evidence for magmatic and tectonic activity that occur at the time of the formation of the giant landslides. In addition, the Miocene climate significant changes lead to changes in precipitation, and such changes might also provide a destabilisation of pyroclastic units. Therefore, the causes of the giant landslides might be related to more than only one process. The La Gomera BC has experienced two rapid cooling/exhumation events: the first at ~ 9 Ma, which might have caused ~ 2.0 (± 0.2) km of erosion forming the offshore Tazo avalanche, also known as the Tazo landslide. The second rapid cooling at ~ 8.0 Ma is located at the northwest of the Island and might have been caused by the Garajonay caldera collapse and followed by landslides. The landslides are assumed to have formed the Segments I, II, III, and VIII of the submarine debris avalanches offshore. Like Fuerteventura, both landslides might have been triggered by tectonic and magmatic activities as well as due to variation in precipitation caused by climate variation.

<p>In a science-based site selection process (StandAV), the Federal Republic of Germany searches for the site with the best possible safety for a repository of high-level waste (HLW) over a period of one million years. For this purpose, the geological subsurface of the German federal territory must be investigated and evaluated.</p><p>Challenges include the large area under investigation, that encompasses the entire federal territory of Germany with its large variability in geology, as well as the verification period which must be met to ensure the best possible long-term safety. Furthermore, an enormous amount of heterogenous geodata will have to be processed.</p><p>The application of AI-based methods in geosciences promises high potentials when dealing with large heterogenous data sets and cost- and time-consuming model calculations of complex and coupled processes. Accordingly, research on the application of AI has increased significantly in the geosciences over the last few years.</p><p>In our recent study “The use of artificial intelligence (AI) in the site selection process for a deep geological repository”, we succeeded developing an interdisciplinary assessment tool to evaluate the applicability of AI methods in geosciences in general and especially regarding their use for geoscientific issues in the StandAV. Here, we focus on potentials and challenges of applying AI in geosciences with respect to geological key activities in the StandAV. Thus, we emphasize on limitations that may arise from the use of AI regarding key activities in StandAV and propose necessary conditions for its applicability in the future.</p><p>Our results show that AI methods are superior to conventional methods, especially when it comes to data management and dealing with large geological data sets and model calculations of complex long-term and coupled geological processes. However, AI methods are generally only transferable to the geoscientific issues in the StandAV with methodological and subject-specific adaptations. Nevertheless, sufficient data, both in quality and quantity, is a prerequisite for the use of AI. Our study also shows that AI should only be used in a supportive way to tackle geological issues in the key activities and must not have any decision-making power when used in the StandAV.</p><p>High demands must be placed on the traceability of the applied AI methods. AI methods that do not meet the transparency requirements of the StandAV bear considerable risks of jeopardizing the trust of the population in the participation process. This could increase the general suspicion and scepticism towards AI in the public perception. Therefore, we strongly recommend to always evaluate and validate iteratively all methods and providing results to the public when applied to the key activities of the StandAV.</p><p>Title of study: “The use of artificial intelligence (AI) in the site selection process for a deep geological repository”, <br>a project on behalf of the Federal Office for the Safety of Nuclear Waste Management (BASE-FKZ 4721E03210)</p>