Mapping rock fractures in high resolution aerial images is essential for understanding Earth processes and managing resources, but manual tracing is slow and inconsistent. We created FraXet, a large harmonized dataset of nearly nine thousand images, and compared standard image filters with modern deep learning models. The deep learning methods were far more accurate and produced smoother, more reliable maps, while also showing where results are uncertain.

The shape of the water filled cavity hidden beneath ice shelves is extremely important, but hard to measure. Gravity data collected from aircraft can be used to predict the shape. However, in the Dotson Crosson ice shelves in West Antarctica, new direct observations from autonomous platforms sent beneath the ice, and seismic observations from the ice surface showed gravity models were systematically too shallow. We explain why and provide a solution applicable her and in other ice shelves.

By analyzing the faint, late-arriving seismic waves from large earthquakes, we can create accurate maps of Earth's deep core. This study provides a new mathematical foundation to confirm the reliability of this method, showing that precise measurements are possible even when earthquake sources are not perfectly distributed. Our findings help improve imaging of Earth's interior, making it more trustworthy.