Pre-training foundation models has become the de-facto procedure for deep learning approaches, yet its application remains limited in the geological studies, where in needs of the model transferability to break the shackle of data scarcity. Here we target on the X-ray fluorescence (XRF) scanning data, a standard high-resolution measurement in extensive scientific drilling projects. We propose a scalable self-supervised learner, masked autoencoders on XRF spectra (MAX), to pre-train a foundation model covering geological records from multiple regions of the Pacific and Southern Ocean. In pre-training, we find that masking a high proportion of the input spectrum (50\%) yields a nontrivial and meaningful self-supervisory task. For downstream tasks, we select the quantification of XRF spectra into two costly geochemical measurements, CaCO$_3$ and total organic carbon, due to their importance in understanding the paleo-oceanic carbon system. Our results show that MAX, requiring only one-third of the data, outperforms models without pre-training in terms of quantification accuracy. Additionally, the model's generalizability improves by more than 60\% in zero-shot tests on new materials, with explainability further ensuring its robustness. Thus, our approach offers a promising pathway to overcome data scarcity in geological discovery by leveraging the self-supervised foundation model and fast-acquired XRF scanning data.

Researchers in the United States have discovered a painting hidden under the famous "The Crouching Beggar" (La Misereuse Accroupie) artwork by Pablo Picasso. According to a report by the Guardian, researchers used a non-invasive imagine technique to inspect the Picasso painting which resulted in them finding a landscape painting hidden underneath it. The new study also provided more information on certain hidden features discovered in the painting earlier. Marc Walton of Northwestern University, who presented the study at a meeting of the American Association for the Advancement of Science in Texas on Saturday, said, "This is where technology allows us to get into the mind of the artist, so we can actually understand the creative process of Picasso and how he actually started producing this work of art." When an x-ray image -- also known as x-radiograph -- of the Picasso painting was taken for a background research, the gallery team of the Art Gallery of Ontario, Canada -- where the painting is now kept -- found the hidden landscape. In order to further delve into the subject, the gallery team was joined by researchers from Northwester University, Illinois.