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Canterbury Basin
Unsupervised contrastive learning for seismic facies characterization
A deep-learning method for latent space analysis of multiple seismic attributes
An in-depth analysis of logarithmic data transformation and per-class normalization in machine learning: Application to unsupervised classification of a turbidite system in the Canterbury Basin, New Zealand, and supervised classification of salt in the Eugene Island minibasin, Gulf of Mexico
Workflow model for the digitization of mudrocks
Abstract Mudrocks are highly heterogeneous in a range of physical and chemical properties, including: porosity and permeability, fissility, colour, particle composition, size, orientation, carbon loading, degree of compaction, and diagenetic overprint. It is therefore important that the maximum information be extracted as efficiently and completely as possible. This can be accomplished through high-resolution analysis of polished thin sections by scanning electron microscopy (SEM), with the collection of large-area images and X-ray elemental map montages, and the application of targeted particle analysis. A workflow model, based on these techniques, for the digitization of mudrocks is presented herein. A range of the data that can be collected and the variety of analyses that can be achieved are also illustrated. Data collection is discussed in terms of inherent problems with acquisition, storage, transfer and manipulation, which can be time-consuming and non-trivial. Similar information and resolutions can be achieved through other techniques, such as QEMSCAN and infra-red (IR)/Raman spectroscopic mapping. These can be seen as complementary to the workflow described herein.
ABSTRACT We combined sand petrofacies with lithofacies to characterize sedimentation within unconformity-bounded sequences at Integrated Ocean Drilling Program (IODP) Expedition 317 sites drilled across the Canterbury shelf to the slope, located off the east coast of the South Island, New Zealand. Differentiation of the relative influence of along- and across-shelf sand supply in this system is made possible by the unique aspects of the onshore geology. Northern rivers draining mainly Torlesse composite terrane lithologies are dominated by lower-grade metamorphic lithic fragments, whereas central rivers, draining the Torlesse to schist (semi-schist) transition (Haast/Otago Schist), contain more higher-grade metamorphic lithic fragments, and the southern rivers contain sand that is quartzofeldspathic and mica rich, having been derived predominantly from coarse schist. Differences are documented in onshore river sand that allow for the provenance classification of 38 offshore sand samples from IODP Expedition 317 cores into four provenance groups based on their likely bedrock sources: (1) Torlesse, (2) Torlesse-schist transition, (3) schist, or (4) mixed. The distribution of sand composition in the 0–0.62 Ma sections of the shelf and slope sites indicates a dynamic system where shore-parallel and shore-perpendicular processes alternated on the shelf, and shore-perpendicular processes dominated at the slope site. When sand compositions are placed in a sequence-stratigraphic context, they indicate an evolving paleogeography through time. Significant sand provenance shifts are linked to falling sea level, with Torlesse-schist transition compositions characteristic of regressive systems tracts. Torlesse compositions are supplied to the sites during falling sea level and sea-level lowstands, when fluvial and coastal geomorphology promotes influx from the north. Mixed compositions characteristic of transgressive systems tracts are likely products of littoral- and shelf-current mixing and potential influx of schist detritus from the south.