Controlled Source Seismic data are essential for understanding the structure and composition of the subsurface. The paper presents a metadata model that allows the user to judge whether a data set is suitable for a specific re-use purpose. The model’s implementation as a metadata catalogue will provide FAIR and open metadata on assets of any legal status and make the assets available for multidisciplinary data integration on the European Plate Observing System (EPOS) research infrastructure.

We present Curlew, an open-source Python tool for constructing 3D geological models using machine learning. It integrates diverse spatial data and structural observations into a flexible, event-based framework. Curlew captures complex features like folds and faults, handles uncertainty, and supports learning from sparse or unlabelled data. We demonstrate its capabilities on synthetic and real-world examples.

Volcanic rocks are shaped by many processes, including volcanism, chemical alteration and weathering. These processes change the rock's properties, making it difficult to predict volcanic hazards or design tunnels and mines in volcanic areas. In this study, we build on earlier research to connect unique spectral signatures that can be remotely imaged using hyperspectral cameras to the density, porosity, strength, and stiffness of volcanic rocks.

We present a new machine learning approach to reconstruct gravity and magnetic tensor data from sparse airborne surveys. By treating the data as derivatives of a hidden potential field and enforcing physical laws, our method improves accuracy and captures geological features more clearly. This enables better subsurface imaging in regions where traditional interpolation methods fall short.

We developed a deep learning model that uses hyperspectral imaging data to predict key physical rock properties, specifically density, slowness, and gamma-ray values. Our model successfully learned to translate hyperspectral information into predicted physical properties. Tests on independent data gave accurate results, demonstrating the potential of hyperspectral data for mapping physical rock properties.