ABSTRACT Quantitative insights into the geochemistry and petrology of proximal impactites are fundamental to understand the complex processes that affected target lithologies during and after hypervelocity impact events. Traditional analytical techniques used to obtain major- and trace-element data sets focus predominantly on either destructive whole-rock analysis or laboratory-intensive phase-specific micro-analysis. Here, we present micro–X-ray fluorescence (µXRF) as a state-of-the-art, time-efficient, and nondestructive alternative for major- and trace-element analysis for both small and large samples (up to 20 cm wide) of proximal impactites. We applied µXRF element mapping on 44 samples from the Chicxulub, Popigai, and Ries impact structures, including impact breccias, impact melt rocks, and shocked target lithologies. The µXRF mapping required limited to no sample preparation and rapidly generated high-resolution major- and trace-element maps (~1 h for 8 cm 2 , with a spatial resolution of 25 µm). These chemical distribution maps can be used as qualitative multi-element maps, as semiquantitative single-element heat maps, and as a basis for a novel image analysis workflow quantifying the modal abundance, size, shape, and degree of sorting of segmented components. The standardless fundamental parameters method was used to quantify the µXRF maps, and the results were compared with bulk powder techniques. Concentrations of most major elements (Na 2 O–CaO) were found to be accurate within 10% for thick sections. Overall, we demonstrate that µXRF is more than only a screening tool for heterogeneous impactites, because it rapidly produces bulk and phase-specific geochemical data sets that are suitable for various applications within the earth sciences.

Abstract Changes in society coupled with more ambitious environmental goals increase the need to make the benefits of geological knowledge visible. The Geological Survey of Sweden (SGU) is therefore evolving from its historical role as a ‘knowledge bank’ to become part of the integrated flow of public information. Three examples of the ongoing digital transformation, and how this will enable the SGU to contribute digital geological data for sustainable development, are: more automated data collection to monitor drinking water to be able to foresee water shortages; several new non-traditional marine projects, producing new information and recommendations for innovative measures to support Blue Growth, management and planning; an online virtual archive containing new data adding to our understanding of bedrock and mineral deposits, in turn leading to more efficient use of Sweden's mineral resources.

Abstract Data-driven techniques including machine-learning (ML) algorithms with big data are re-activating and re-empowering research in traditional disciplines for solving new problems. For geoscientists, however, what matters is what we do with the data rather than the amount of it. While recent monitoring data will help risk and resource assessment, the long-earth record is fundamental for understanding processes. Thus, how big data technologies can facilitate geoscience research is a fundamental question for most organizations and geoscientists. A quick answer is that big data technology may fundamentally change the direction of geoscience research. In view of the challenges faced by governments and professional organizations in contributing to the transformation of Earth science in the big data era, the International Union of Geological Sciences has established a new initiative: the IUGS-recognized Big Science Program. This paper elaborates on the main opportunities and benefits of utilizing data-driven approaches in geosciences and the challenges in facilitating data-driven earth science transformation. The main benefits may include transformation from human learning alone to integration of human learning and AI, including ML, as well as from known questions seeking answers to formulating as-yet unknown questions with unknown answers. The key challenges may be associated with intelligent acquisition of massive, heterogeneous data and automated comprehensive data discovery for complex Earth problem solving.