Carbonate rocks can be classified in terms of those properties relating to the pore system of lithified sediments, so‐called ‘petrophysical rock types’, or ‘depositional rock types’ which are categorized based on characteristics directly reflecting their original depositional environment. Whereas petrophysical rock types are typically used to identify and distribute rock bodies within a reservoir with similar flow characteristics, depositional rock types ignore pore types and capture sedimentary structures, lithology and fossils. Both classification systems are extensively used to describe reservoir rocks, but the degree of plurality between them remains poorly understood and is the motivation for this study. To examine the degree of congruency between the two classification schemes, a field assessment was conducted for a 175 km2 area situated offshore Al Ruwais, northern Qatar, encompassing depositional environments spanning supratidal, intertidal, shallow subtidal and open marine conditions. A total of 350 surficial sediment samples were collected along 24 shore‐normal transects. Each sample was assigned a ‘petrophysical rock type’ class based on analysis of sedimentary texture (grain size and sorting). ‘Depositional rock type’ classes, by contrast, were defined with reference to faunal content and, in turn, classes of mineralogy were delimited by weighting this content against the mineralogy of each faunal category. Of course, the samples studied correspond to unconsolidated sediments and not to indurated rocks. However, considering only primary porosity and permeability preservation, it is reasonable to assume that the classified sediments would become petrophysical rock types and depositional rock types when consolidated, following their primary grain size, sorting and grain type distribution. Therefore, the term ‘rock type’ is retained here for ease of terminology but, for clarity, these are sediment samples. The discrete samples were interpolated into continuous surfaces describing the distribution of depositional rock types, petrophysical rock types and mineralogy, and spatial correspondence between those surfaces was statistically evaluated. In order to link these parameters with environment of deposition, their correlation with water depth (as audited from airborne light detection and ranging) and ecological habitat (mapped from DigitalGlobe satellite imagery) was also assessed. The data reveal that spatial distributions of sedimentary faunal, petrographic and mineralogical properties do not show exactly congruent patterns. Other meaningful trends do exist, however. For example, the occurrence of certain depositional rock types is indicative of particular petrophysical rock types, and vice versa. Further, connections between petrophysical rock types and mineralogy are emphasized and offer insight as to how the evolution of matrix porosity might be predicted via diagenetic models tuned to specific sediment textures. Useful relationships are also identified between the occurrence of petrophysical...

Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.

Resumen Objetivos Estimar un modelo predictivo para la no-unión en pacientes que presentan fractura de tibia. Materiales y Métodos Estudio de cohorte retrospectivo, en pacientes con fractura de tibia operadas entre 2012 y 2018, con un mínimo de 12 meses de seguimiento, excluyendo amputaciones traumáticas. Realizamos un modelo de regresión logística con 13 variables descritas en la literatura. Se descartaron las variables estadísticamente no significativas y las que no causaban efecto de confusión. Se evaluó la bondad de ajuste mediante el test de Hosmer-Lemeshow y la discriminación del modelo con la curva ROC. Resultados Se incluyeron 411 fracturas de tibia, las variables estadísticamente significativas fueron: exposición ósea OR = 2,57(IC:1,15–5,75, p = 0,022), diabetes OR = 3,29(IC:1,37–7,91, p = 0,008) y uso de tutor externo OR = 1,77(IC:0,81–3,85), el que tuvo efecto de confusión. La bondad de ajuste demostró que los datos se ajustan adecuadamente al modelo (p = 0,35). La curva ROC demuestra un 70,91% de poder discriminatorio. Al evaluar aisladamente las fracturas expuestas, no hubo asociación estadísticamente significativa con ninguna variable. Discusión Al evaluar el modelo, obtuvimos una asociación estadísticamente significativa entre: no unión, exposición ósea, diabetes y uso de tutor externo, información concordante con la literatura. Al estudiar el subgrupo de fracturas expuestas, las demás variables son estadísticamente no significativas. Eso refleja que la exposición ósea es la variable que confiere mayor riesgo. El seguimiento adecuado de esos pacientes es fundamental dado este alto riesgo de evolucionar con no-unión. Conclusión En nuestra serie, la exposición ósea es el factor de riesgo más importante para presentar no unión de tibia.