Considerable effort has been devoted to the development of simulation algorithms for facies modeling, whereas a discussion of how to combine those techniques has not existed. The integration of multiple geologic data into a three-dimensional model, which requires the combination of simulation techniques, is yet a current challenge for reservoir modeling. This article presents a thought process that guides the acquisition and modeling of geologic data at various scales. Our work is based on outcrop data collected from a Jurassic carbonate ramp located in the High Atlas mountain range of Morocco. The study window is 1 km (0.6 mi) wide and 100 m (328.1 ft) thick. We describe and model the spatial and hierarchical arrangement of carbonate bodies spanning from largest to smallest: (1) stacking pattern of high-frequency depositional sequences, (2) facies association, and (3) lithofacies. Five sequence boundaries were modeled using differential global position system mapping and light detection and ranging data. The surface-based model shows a low-angle profile with modest paleotopographic relief at the inner-to-middle ramp transition. Facies associations were populated using truncated Gaussian simulation to preserve ordered trends between the inner, middle, and outer ramps. At the lithofacies scale, field observations and statistical analysis show a mosaiclike distribution that was simulated using a fully stochastic approach with sequential indicator simulation. This study observes that the use of one single simulation technique is unlikely to correctly model the natural patterns and variability of carbonate rocks. The selection and implementation of different techniques customized for each level of the stratigraphic hierarchy will provide the essential computing flexibility to model carbonate settings. This study demonstrates that a scale-dependent modeling approach should be a common procedure when building subsurface and outcrop models.

The end-Permian mass extinction occurred alongside a large swath of environmental changes that are often invoked as extinction mechanisms, even when a direct link is lacking. One way to elucidate the cause(s) of a mass extinction is to investigate extinction selectivity, as it can reveal critical information on organismic traits as key determinants of extinction and survival. Here we show that machine learning algorithms, specifically gradient boosted decision trees, can be used to identify determinants of extinction as well as to predict extinction risk. To understand which factors led to the end-Permian mass extinction during an extreme global warming event, we quantified the ecological selectivity of marine extinctions in the well-studied South China region. We find that extinction selectivity varies between different groups of organisms and that a synergy of multiple environmental stressors best explains the overall end-Permian extinction selectivity pattern. Extinction risk was greater for genera that had a low species richness, narrow bathymetric ranges limited to deep-water habitats, a stationary mode of life, a siliceous skeleton, or, less critically, calcitic skeletons. These selective losses directly link the extinctions to the environmental effects of rapid injections of carbon dioxide into the ocean–atmosphere system, specifically the combined effects of expanded oxygen minimum zones, rapid warming, and potentially ocean acidification.