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Re-examination of the rare earth elements (REE) deposits in the Cornudas Mountains is warranted in light of today's economic importance of critical minerals, including REE that are essential in most of our electronic devices. New mapping, petrography, 40Ar/39Ar geochronology, and geochemical analyses have provided a better understanding of the emplacement of these intrusions and associated mineral deposits. The Cornudas Mountains form the northern Trans-Pecos alkaline magmatic province in the southern part of the North American Cordilleran alkaline-igneous belt. The igneous rocks in the Cornudas Mountains were emplaced in two pulses at 37.14-34.5 and 32.48-26.95 Ma, just prior to or during the early phases of Rio Grande rift extension, and consist of 1) larger nepheline syenite-syenite laccoliths and plugs, 2) phonolite plugs, sills, and dikes, 3) smaller syenite plugs and dikes that intrude Permian and Cretaceous sedimentary rocks, and 4) volcanic breccia dikes. New USGS geophysical data indicate that some of these intrusions extend deep into the subsurface, with additional buried intrusions potentially at depth. The focus of REE exploration is along the lower unit (PEnsp2) of the Wind Mountain nepheline syenite laccolith, as well as within syenite-phonolite and volcanic breccia dikes, plugs and skarns and carbonate-replacement deposits in Chess Draw. Some samples contain as much as 3110 ppm total REE. REE could be leached from a mineral concentrate of the REE-bearing minerals (eudialyte, zircon, monazite, bastnasite, calcio-catapleiite, vitusite, roumaite, xenotime). We incorporate whole rock and clinopyroxene chemistry of each intrusion into the clinopyroxene-liquid geothermobarometer (Masotta, 2013) to determine the temperatures and pressures of emplacement. This thermometer provides higher crystallization temperature estimates for the syenite intrusions (857-1027 deg. C) than the phonolite sills (760-869 deg. C). We then use the barometric estimates (0.3-3.3 kbar) to calculate emplacement depths (1.2-12.3 km). Pairing these depths with the new geochronology, minimum exhumation rates for intrusions in the Cornudas Mountains are estimated that range from 0.04-0.34 mm/yr crystallization temperatures and exhumation rates provide additional information to aid in developing a model for the formation of REE deposits.

Rare earth elements (REE) became a strategic raw material in the 21st century and carbonatite-related deposits frequently carry potentially economic levels of these critical metals. To determine concentrations of major and trace elements (including REE) conventional analysis of bulk rock geochemical samples by means of inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) is mostly applied. However, these analytical methods are labor-intensive, costly and time-consuming. In contrast, modern Remote Sensing (RS) – e.g. proximal remote sensing or imaging spectroscopy – has become a novel tool for detecting and quantifying geological materials as they offer an efficient, non-destructive and cost-effective way not only to identify different minerals and/or chemical elements but also model their abundances. In this study, we investigated whether Partial Least Squares Regression (PLSR) models linking the laboratory sample reflectance to mineral/geochemical property could be employed for quantitative predictions of bulk LREE and HREE concentrations. The suite included samples from four different geographic regions in India, East Africa and the USA. Our results showed that for such samples the quantitative approaches have some limitations. First, our models required to exclude samples with high modal abundance of hematite and samples with too low REE contents (LREE We posit that this topic requires future systematic investigations using the extended carbonatite datasets – samples having a wide range of REE abundances – collected from lithologically diverse regions. This would enable further validation and re-calibration of the constructed prediction models.