Lamprophyre as the Source of Zircon in the Veneto Region, Italy

Discrete zircons, up to 9 mm in length, occur in alluvial deposits from the Veneto area. They are likely derived from the disaggregation of lamprophyric rocks belonging to a regional, pervasive dyke-swarm. Zircon and REE phases occur in both alkaline lamprophyres and connate calcite-bearing felsic lithics and their debris in lamprophyre breccia. We present 36 new complete U–Pb and trace element analyses of zircons and associated inclusions. We used a statistical approach on a larger dataset using new and literature data to evaluate the confidence figure to give an estimation of age of zircons. Inclusions suggest a genetic link with an S–CO2–ZrO–BaO–SrO–CaO-rich fluid/melt possibly associated with carbonate-rich alkaline parental magma and a metasomatised mantle source. This paper confirms the importance of calcite–syenite and lamprophyre genetic link and zircon magmatic origin, in contrast with hydrothermal and metamorphic zircons. U–Pb dating by LA-ICP-MS provides time constrains (40.5–48.4 Ma, Lutetian), consistent with the age of the alkaline magmatic event. Trace element data indicate a link to anorogenic magmatism associated with mantle upwelling. Complex zoning is highlighted by cathodoluminescence images. The Veneto zircons are helpful for regional geological information and may have commercial potential as a critical resource for green technologies

Dissolution-Repackaging of Hellandite-(Ce), Mottanaite-(Ce)/Ferri-Mottanaite-(Ce)

We investigated hellandite-group mineral phases from the Roman Region, alkali syenite ejecta, by multimethod analyses. They show a complex crystallisation history including co-precipitation of hellandite-(Ce) with brockite, resorption, sub-solidus substitution with mottanaite-(Ce), exsolution of perthite-like ferri-mottanaite-(Ce), overgrowth of an oscillatory-zoned euhedral shell of ferri-mottanaite-(Ce) and late, secondary precipitation of pyrochlore in the cribrose hellandite-(Ce) core. LREE/HREE crossover and a negative Eu anomaly in hellandite-group minerals follows fO2 increase during magma cooling. The distinction among the hellandite-group minerals is based on the element distribution in the M1, M2, M3, M4 and T sites. Additional information on miscibility relationship among the hellandite sensu strictu, tadzhikite, mottanaite, ferri-mottanaite and ciprianiite endmembers derives from molar fraction calculation. We observed that change in composition of hellandite-group minerals mimic the ligands activity in carbothermal-hydrothermal fluids related to carbonatitic magmatism

A critical review on the state-of-the-art and future prospects of Machine Learning for Earth Observation Operations

The continuing Machine Learning (ML) revolution indubitably has had a significant positive impact on the analysis of downlinked satellite data. Other aspects of the Earth Observation industry, despite being less susceptible to widespread application of Machine Learning, are also following this trend. These applications, actual use cases, possible prospects and difficulties, as well as anticipated research gaps, are the focus of this review of Machine Learning applied to Earth Observation Operations. A wide range of topics are covered, including mission planning, fault diagnosis, fault prognosis and fault repair, optimization of telecommunications, enhanced GNC, on-board image processing, and the use of Machine Learning models on platforms with constrained compute and power capabilities, as well as recommendations in the respective areas of research. The review tackles all on-board and off-board applications of machine learning to Earth Observation with one notable exception: it omits all post-processing of payload data on the ground, a topic that has been studied extensively by past authors. In addition, this review article discusses the standardization of Machine Learning (i.e., Guidelines and Roadmaps), as well as the challenges and recommendations in Earth Observation operations for the purpose of building better space missions