Highly expanded Cretaceous–Paleogene (K-Pg) boundary section from the Chicxulub peak ring, recovered by International Ocean Discovery Program (IODP)–International Continental Scientific Drilling Program (ICDP) Expedition 364, provides an unprecedented window into the immediate aftermath of the impact. Site M0077 includes ∼130 m of impact melt rock and suevite deposited the first day of the Cenozoic covered by <1 m of micrite-rich carbonate deposited over subsequent weeks to years. We present an interpreted series of events based on analyses of these drill cores. Within minutes of the impact, centrally uplifted basement rock collapsed outward to form a peak ring capped in melt rock. Within tens of minutes, the peak ring was covered in ∼40 m of brecciated impact melt rock and coarse-grained suevite, including clasts possibly generated by melt–water interactions during ocean resurge. Within an hour, resurge crested the peak ring, depositing a 10-m-thick layer of suevite with increased particle roundness and sorting. Within hours, the full resurge deposit formed through settling and seiches, resulting in an 80-m-thick fining-upward, sorted suevite in the flooded crater. Within a day, the reflected rim-wave tsunami reached the crater, depositing a cross-bedded sand-to-fine gravel layer enriched in polycyclic aromatic hydrocarbons overlain by charcoal fragments. Generation of a deep crater open to the ocean allowed rapid flooding and sediment accumulation rates among the highest known in the geologic record. The high-resolution section provides insight into the impact environmental effects, including charcoal as evidence for impact-induced wildfires and a paucity of sulfur-rich evaporites from the target supporting rapid global cooling and darkness as extinction mechanisms.

The linear stability of an erodible sediment bed beneath a turbidity current is analysed, in order to identify potential mechanisms responsible for the formation of longitudinal gullies and channels. On the basis of the three-dimensional Navier–Stokes equations, the stability analysis accounts for the coupled interaction of the three-dimensional fluid and particle motion inside the current with the erodible bed below it. For instability to occur, the suspended sediment concentration of the base flow needs to decay away from the sediment bed more slowly than does the shear stress inside the current. Under such conditions, an upward protrusion of the sediment bed will find itself in an environment where erosion decays more quickly than sedimentation, and so it will keep increasing. Conversely, a local valley in the sediment bed will see erosion increase more strongly than sedimentation, which again will amplify the initial perturbation.The destabilizing effect of the base flow is modulated by the stabilizing perturbation of the suspended sediment concentration and by the shear stress due to a secondary flow structure in the form of counter-rotating streamwise vortices. These streamwise vortices are stabilizing for small Reynolds and Péclet numbers and destabilizing for large values.For a representative current height of O(10–100m), the linear stability analysis provides the most amplified wavelength in the range of 250–2500m, which is consistent with field observations reported in the literature. In contrast to previous analyses based on depth-averaged equations, the instability mechanism identified here does not require any assumptions about sub- or supercritical flow, nor does it require the presence of a slope or a slope break.

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

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The Geophysical Tutorial in the October issue of The Leading Edge was the first we've done on the topic of machine learning. Brendon Hall's article ( Hall, 2016 ) showed readers how to take a small data set — wireline logs and geologic facies data from nine wells in the Hugoton natural gas and helium field of southwest Kansas ( Dubois et al., 2007 ) — and predict the facies in two wells for which the facies data were not available. The article demonstrated with 25 lines of code how to explore the data set, then create, train and test a machine learning model for facies classification, and finally visualize the results. The workflow took a deliberately naive approach using a support vector machine model. It achieved a sort of baseline accuracy rate — a first-order prediction, if you will — of 0.42. That might sound low, but it's not untypical for a naive approach to this kind of problem. For comparison, random draws from the facies distribution score 0.16, which is therefore the true baseline.