The lithosphere, as the outermost solid layer of our planet, preserves a progressively more fragmentary record of geological events and processes from Earth’s history the further back in time one looks. Thus, the evolution of lithospheric thickness and its cascading impacts in Earth’s tectonic system are presently unknown. Here, we track the lithospheric thickness history using machine learning based on global lithogeochemical data of basalt. Our results demonstrate that four marked lithospheric thinning events occurred during the Paleoarchean, early Paleoproterozoic, Neoproterozoic, and Phanerozoic with intermediate thickening scenarios. These events respectively correspond to supercontinent/supercraton breakup and assembly periods. Causality investigation further indicates that crustal metamorphic and deformation styles are the feedback of lithospheric thickness. Cross-correlation between lithospheric thickness and metamorphic thermal gradients records the transition from intraoceanic subduction systems to continental margin and intraoceanic in the Paleoarchean and Mesoarchean and a progressive emergence of large thick continents that allow supercontinent growth, which promoted assembly of the first supercontinent during the Neoarchean.

Micro X-ray fluorescence analysis (micro-XRF) commonly has a lower spatial (lateral) resolution than other elemental mapping techniques like scanning electron microscopy. This disadvantage limits further applications of micro-XRF in microanalysis. In this study, the spatial resolution of micro-XRF was improved by oversampling and image restoration techniques, including both blind and non-blind methods. The point spread function (PSF) for the non-blind method was estimated efficiently by applying both the small point method and the knife-edge method. Three non-blind algorithms—the fast iterative shrinkage-thresholding algorithm, the Wiener filter, and the Richardson–Lucy (RL) method—were tested. The results showed that all these methods could improve the spatial resolution by as much as 26.5%. According to two image quality metrics—the mean squared error and the structural similarity index measure—the results of the RL method were better than those of the others. Compared to the non-blind method, the blind method called “blind deblurring with L0-regularized intensity and gradient prior” only yielded a 12.3% improvement of the resolution, but it obtained a more precise estimation of the size of the PSF. An oversized PSF helps to denoise, but it might reduce the resolution, and vice versa. When the correct PSF was used in the RL method, the improvement of the resolution was as high as 41.6%. A further application on rock samples showed that the technique helped to reduce the resolution variations caused by polychromatic X-rays. This study demonstrated that this technique is promising, but it is still necessary to accumulate more data and design more appropriate algorithms for micro-XRF images.

This research explored the feasibility of using portable X-ray fluorescence (pXRF) spectrometry to quantify Fe, Cu, Zn, and As concentrations in dried and ground organic materials (fungi, vegetation, and animal tissues) with intermediate thickness.