Geoscience organizations shape the discipline. They influence attitudes and expectations, set standards, and provide benefits to their members. Today, racism and discrimination limit the participation of, and promote hostility towards, members of minoritized groups within these critical geoscience spaces. This is particularly harmful for Black, Indigenous, and other people of color in geoscience and is further exacerbated along other axes of marginalization, including disability status and gender identity. Here we present a twenty-point anti-racism plan that organizations can implement to build an inclusive, equitable and accessible geoscience community. Enacting it will combat racism, discrimination, and the harassment of all members.

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We present a new probabilistic lava flow hazard assessment for the U.S. Department of Energy's Idaho National Laboratory (INL) nuclear facility that (1) explores the way eruptions are defined and modeled, (2) stochastically samples lava flow parameters from observed values for use in MOLASSES, a lava flow simulator, (3) calculates the likelihood of a new vent opening within the boundaries of INL, (4) determines probabilities of lava flow inundation for INL through Monte Carlo simulation, and (5) couples inundation probabilities with recurrence rates to determine the annual likelihood of lava flow inundation for INL. Results show a 30% probability of partial inundation of the INL given an effusive eruption on the eastern Snake River Plain, with an annual inundation probability of 8.4 × 10 −5 to 1.8 × 10 −4. An annual probability of 6.2 × 10 −5 to 1.2 × 10 −4 is estimated for the opening of a new eruptive center within INL boundaries.

2019) How to use kernel density estimation as a diagnostic and forecasting tool for distributed volcanic vents, Statistics in Volcanology 4.3 : 1 − 25. AbstractVolcanic activity often results in the formation of new volcanic vents. These new vents can create hazards in unexpected areas. Therefore, the probability of new vent formation should be assessed as part of volcanic hazard assessments. This paper describes our use of kernel density estimation (KDE) as a way to estimate the spatial density of future volcanic vents. The bivariate Gaussian kernel function is described step-by-step using pseudocode. Our computer code, written in PERL, is used to calculate the spatial density of existing vents and then create a contour map using GMT (Generic Mapping Tools). Application of this method and code relies on several assumptions about the definition of volcanic events, independence of events, the type of kernel function used, and the selection of kernel bandwidth. Three examples using the code are provided: (1) for volcanic vents located west of the city of Managua (Nicaragua), (2) for volcanic vents distributed within the Arsia Mons caldera (Mars), weighted by volume, and (3) for vents of the Lassen volcanic system (northern California), sub-divided by geochemistry.

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