Apatite (U-Th)/He (AHe) dating is a widely-applied thermochronological technique used to decipher low-temperature thermal histories. Accurate dates require that the results are corrected for α-ejection because 4He atoms travel ~20 µm during α-decay and a correction is required to account for He lost by this effect. Effective uranium concentrations (eU) are important for accurate AHe data interpretation because radiation damage scales with eU, which affects He retentivity. Both α-ejection correction parameter (Ft) and eU are calculated on the basis of crystal size and assuming an idealized morphology. However, the uncertainty stemming from the calculations’ assumptions depends on how much the real crystal geometry deviates from that assumed, and this uncertainty is typically not included in the propagated uncertainties on AHe data. Our goal for this study was to develop a ‘rule of thumb’ for Ft and eU uncertainties associated with the full range of commonly analyzed apatite geometries by comparing manually measured grain size and actual grain size using nano-computed tomography (nano-CT). Apatite geometry and roughness were characterized using a Grain Evaluation Matrix (GEM). The geometry of each grain was described as: A (prismatic/hexagonal), B (subprismatic), or C (rounded/ellipsoid). Surface roughness was graded from ‘least’ to ‘most’ using values from 1 to 3. The GEM allows for a single parameter (eg. B2) to succinctly classify a grain’s morphology. High resolution nano-CT scans of ~260 grains representative of those usually analyzed for AHe dates were completed and processed using Dragonfly and Blob3D. Initial analysis shows that manual grain measurements systematically overestimate the actual grain size, leading to overestimates in Ft and eU values. One correction exists for A and B grains (hexagonal) and another for C grains (ellipsoid). The correction is controlled primarily by grain size and shape, while the uncertainty on the correction appears to be controlled primarily by surface roughness. Together, this approach provides a simple and practical tool for deriving more accurate Ft and concentration values, and for incorporating this oft neglected geometric uncertainty into AHe dates.

Drilling by the Oman Drilling Project provided a unique opportunity to access partially-serpentinized harzburgite and dunite. These are in contact with alkaline fluids in a subsurface environment that support a microbial ecosystem. In concert with studies of the rock-hosted microbial community, we are characterizing the mineralogy and petrology of the serpentinized mantle rocks that host this ecosystem. Samples of whole-round core were collected and preserved every 10 m from 3 boreholes and split into paired subsamples for microbiology and mineral characterization. Thin sections were analyzed with a petrographic microscope to complete mineral abundance estimations and interpret textural relationships. Raman spectroscopy was conducted on the thin sections to reveal structural/compositional data about mineral phases. Powders were prepared for XRD analysis for quantitative phase identification. The main rock types are altered harzburgite and dunite, and altered veins of gabbro or pyroxenite occur at certain depths. All of the cores have experienced multiple episodes of serpentinization. The observed mineral assemblages include relict olivine, pyroxene and abundant secondary serpentine, brucite, iron sulfide and andradite-grossular garnet. The assemblages are generally expected from partial serpentinization of peridotite, but the widespread distribution of garnet was particularly surprising. Over 50% of the samples contained sufficient garnet to be detected by XRD. Optical and Raman analyses show that garnet occurs in many textural contexts, notably inside mm-scale, late-stage serpentine veins. Andradite garnet in serpentine veins similar to those found here are likely to have formed during serpentinization at temperatures below ~200°C [1,2]. Incorporating Fe3+ into the andradite component could facilitate H2 production, a potent energy source for microbial metabolisms [2]. Its high abundance may provide key insights into H2 production and habitability during late-stage serpentinization of the Oman ophiolite. [1] Ménez et al. (2018) LITHOS DOI: 10.1016/j.lithos.2018.07.022 [2] Plümper et al. (2014) Geochimica et Cosmochimica Acta 141 (454-471).

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall-triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select among a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide-triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity-duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of 228 reported landslides across the continental US and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/hr and as much as 57 mm/hr. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity-duration storm thresholds, but all products exhibited hit-ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually-verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.