Chronology of Dune Development in the White River Badlands, Northern Great Plains, USA

Aeolian dune field chronologies provide important information on drought history on the Great Plains. The White River Badlands (WRB) dunes are located approximately 60 km north of the Nebraska Sand Hills (NSH), in the western section of the northern Great Plains. Clifftop dunes, sand sheets, and stabilized northwest-southeast trending parabolic dunes are found on upland mesas and buttes, locally called tables. The result of this study is a dune stabilization history determined from samples collected from stratigraphic exposures and dune crests. Thirty-seven OSL ages, from this and previous investigations, show three periods of dune activity: 1) ∼21,000 years ago to 12,000 years ago (a), 2) ∼9 to 6 ka, and 3) post-700 a. Stratigraphic exposures and low-relief dune forms preserve evidence of late Pleistocene and middle Holocene dune development, while high-relief dune crests preserve evidence of late Holocene dune development. Results of 12 OSL ages from the most recent dune activation event indicate that Medieval Climate Anomaly (MCA) droughts and Little Ice Age (LIA) droughts caused dune reactivation on the tables. Dune reactivation was accompanied by other drought-driven geomorphological responses in the WRB, including fluvial incision of the prairie and formation of sod tables. Regional significance of the MCA and LIA droughts is supported by similarities in the aeolian chronologies of the NSH at 700–600 a and some western Great Plains dune fields at 420–210 a. Aerial photographs of the WRB show little activity during the Dust Bowl droughts of the 1930s

Nuclear structures: Twinning and modulation in crystals

Crystal structure analysis is a standard technique routinely applied to single crystals as well as powders. However the process is not so straightforward if the crystal sample is affected by twinning or if the structure is modulated. In such cases the standard procedures are not directly applicable. The main purpose of this contribution is to show how to solve and refine such difficult structures. While for twinned structures the basic property of crystal – translation symmetry in three dimensional space–remains valid, for modulated crystals a special superspace theory must be exploited in order to describe the atomic structure with crystallographic methods generalized for superspace

True substrates: The exceptional resolution and unexceptional preservation of deep time snapshots on bedding surfaces

Abstract: Rock outcrops of the sedimentary–stratigraphic record often reveal bedding planes that can be considered to be true substrates: preserved surfaces that demonstrably existed at the sediment–water or sediment–air interface at the time of deposition. These surfaces have high value as repositories of palaeoenvironmental information, revealing fossilized snapshots of microscale topography from deep time. Some true substrates are notable for their sedimentary, palaeontological and ichnological signatures that provide windows into key intervals of Earth history, but countless others occur routinely throughout the sedimentary–stratigraphic record. They frequently reveal patterns that are strikingly familiar from modern sedimentary environments, such as ripple marks, animal trackways, raindrop impressions or mudcracks: all phenomena that are apparently ephemeral in modern settings, and which form on recognizably human timescales. This paper sets out to explain why these short‐term, transient, small‐scale features are counter‐intuitively abundant within a 3.8 billion year‐long sedimentary–stratigraphic record that is known to be inherently time‐incomplete. True substrates are fundamentally related to a state of stasis in ancient sedimentation systems, and distinguishable from other types of bedding surfaces that formed from a dominance of states of deposition or erosion. Stasis is shown to play a key role in both their formation and preservation, rendering them faithful and valuable archives of palaeoenvironmental and temporal information. Further, the intersection between the time–length scale of their formative processes and outcrop expressions can be used to explain why they are so frequently encountered in outcrop investigations. Explaining true substrates as inevitable and unexceptional by‐products of the accrual of the sedimentary–stratigraphic record should shift perspectives on what can be understood about Earth history from field studies of the sedimentary–stratigraphic record. They should be recognized as providing high‐definition information about the mundane day to day operation of ancient environments, and critically assuage the argument that the incomplete sedimentary–stratigraphic record is unrepresentative of the geological past

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods

A wealth of research has focused on elucidating the key controls on mass loss from the Greenland and Antarctic ice sheets in response to climate forcing, specifically in relation to the drivers of marine-terminating outlet glacier change. The manual methods traditionally used to monitor change in satellite imagery of marine-terminating outlet glaciers are time-consuming and can be subjective, especially where mélange exists at the terminus. Recent advances in deep learning applied to image processing have created a new frontier in the field of automated delineation of glacier calving fronts. However, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. Here, we apply and test a two-phase deep learning approach based on a well-established convolutional neural network (CNN) for automated classification of Sentinel-2 satellite imagery. The novel workflow, termed CNN-Supervised Classification (CSC) is adapted to produce multi-class outputs for unseen test imagery of glacial environments containing marine-terminating outlet glaciers in Greenland. Different CNN input parameters and training techniques are tested, with overall F1 scores for resulting classifications reaching up to 94 % for in-sample test data (Helheim Glacier) and 96 % for out-of-sample test data (Jakobshavn Isbrae and Store Glacier), establishing a state of the art in classification of marine-terminating glaciers in Greenland. Predicted calving fronts derived using optimal CSC input parameters have a mean deviation of 56.17 m (5.6 px) and median deviation of 24.7 m (2.5 px) from manually digitised fronts. This demonstrates the transferability and robustness of the deep learning workflow despite complex and seasonally variable imagery. Future research could focus on the integration of deep learning classification workflows with free cloud-based platforms, to efficiently classify imagery and produce datasets for a range of glacial applications without the need for substantial prior experience in coding or deep learning