ALBERT

Description: The major-element chemical composition of garnet provides valuable petrogenetic information, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk-rock composition and mineral paragenesis of the initial garnet-bearing host-rock is absent. This prevents the application of chemical thermo-barometric techniques and calls for quantitative empirical approaches. Here we present a garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 chemical analyses of garnet that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) 〉 95% concerning the setting, that is either mantle, metamorphic, igneous, or metasomatic; (ii) 〉 84% concerning the metamorphic facies, that is either blueschist/greenschist, amphibolite, granulite, or eclogite/ultrahigh-pressure; and (iii) 〉 93% concerning the host-rock bulk composition, that is either intermediate–felsic/metasedimentary, mafic, ultramafic, alkaline, or calc–silicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization.

Description: deutsche forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

Description: Georg-August-Universität Göttingen (1018)

Description: http://134.76.17.86:443/garnetRF/

Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Heavy‐mineral suites are used widely in sandstone provenance and are key when connecting source and sink. When characterizing provenance related signatures, it is essential to understand the different factors that may influence a particular heavy‐mineral assemblage for example, chemical weathering or diagenetic processes. Hydrodynamics, causing size‐density sorting, exert major control on the distribution of heavy minerals. Here, we highlight the effect of grain‐size inheritance, essentially the absence of certain grain sizes within a specific heavy‐mineral species, on two distinct types of sediments. Modern deposits from a high‐energy beach in NW Denmark give an analog for heavily reworked sediment, primarily controlled by hydrodynamic processes. In contrast, three Palaeogene turbidite successions in the Eastern Alps were sampled, presenting a more complex history that includes diagenesis. All samples were processed for their heavy‐mineral compositions using Raman spectroscopy, and several techniques applied to determine the effect of grain‐size inheritance. Results show that (a) even within the hydrodynamically well‐sorted beach and placer deposits, evidence of grain‐size inheritance is apparent, and (b) turbidites of variable heavy‐mineral composition show strong effects of grain‐size inheritance for several mineral species. Moreover, considerable intersample contrasts within single turbidite beds are observed. We enforce the importance of understanding grain‐size inheritance, as well as other processes effecting size‐density relations in clastic sediment that go well beyond purely hydrodynamic control of intrasample heavy‐mineral variability.〈/p〉

Description: Plain Language Summary: Heavy minerals are commonly found within sediments and sedimentary rocks and can tell us from which source regions the sediment may have originated. However, it is important to understand that the type, size, and abundance of particular heavy minerals can change depending on factors such as environmental conditions. The size, shape, and density of the heavy minerals also limits when and where they will settle and/or stay. A lack of big or small grains of a particular heavy mineral in the source rocks dictates the size of the minerals deposited; this is known as grain‐size inheritance. Using both ancient and modern sediment, we are looking for traces of grain‐size inheritance. Surprisingly, in all samples investigated we noted effects of grain‐size inheritance, for different heavy‐mineral types. The modern beach sediments, as expected, show more impact of hydraulic processes, but inherited grain sizes are still apparent. Within the ancient examples, grain‐size inheritance is more obvious, with further variations even observed between samples collected from the same area. Having identified this control on grain size, we can highlight the importance of understanding this effect when analyzing clastic sediments.〈/p〉

Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Understanding factors that can modify a heavy‐mineral assemblage is fundamental in provenance analysis〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Heavy minerals of two distinct sedimentary environments were analyzed and compared to their “ideal” hydrodynamically sorted compositions〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Several heavy‐mineral species of modern and ancient settings were identified to be influenced by grain‐size inheritance from the source〈/p〉〈/list-item〉 〈/list〉 〈/p〉

Description: https://doi.org/10.25625/MVUIJQ

Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Sediment composition in modern fluvial settings is commonly assessed regarding spatial but rarely temporal variability, potentially leading to a bias of unknown extent. Here, we present the grain‐size distribution, bulk chemical and mineralogical composition of a time‐series set of 36 suspended sediment samples from the Brahmaputra river, as well as clay and heavy mineral analysis of selected samples. Sampling covers the June–November 2021 period, which included two major flooding events. We show that the two flooding events are characterized by contrasting grain size, with the first event characterized by a grain‐size minimum and the second by a grain‐size maximum. Although grain sizes of the first flood and the period after the second are similar, their compositions differ significantly, highlighted by a factor‐two decrease of biotite largely compensated by an increase in quartz. By contrast, the content of garnet, clinopyroxene, sillimanite, and rutile increased compared to epidote and amphibole during the second flood event. By relating the results to spatio‐temporal rainfall and discharge patterns and basin morphology, we conclude that the first flooding primarily mobilized hydraulically pre‐sorted sediments from the exposed sandbars of the floodplains, while those sandbars are already submerged during the second flooding in a single‐channel system, resulting in higher sediment contributions from highland tributaries draining igneous and high‐grade metamorphic rocks. Such temporal variations pose constraints on the interpretation of compositional differences between individual samples regarding sediment provenance and dispersal and should be considered in studies of modern drainage basins as well as ancient sediment routing systems.〈/p〉

Description: Plain Language Summary: Sediment provenance, which refers to where the sediment in a river comes from, is important to understand because it can tell us about the geology of an area, various earth‐surface processes and how the landscape is changing over time. However, sediment provenance is typically studied at a spatial scale in present day river basins, and temporal variability is rarely considered. This study examines the physical, chemical and mineralogical properties of sediment in the Brahmaputra river during two major flooding events that occurred in the same season. The results show that the sediment composition varies between the events, indicating a change in the relative proportions of distinct sources. This emphasizes the importance of considering temporal variations in sediment composition when interpreting sediment provenance signals.〈/p〉

Description: Key Points: 〈list list-type="bullet"〉 〈list-item〉 〈p xml:lang="en"〉Time‐series analysis of sediment composition during two major flooding events of a single monsoon season is presented〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉The two flooding events show contrasting grain‐size, chemical and mineralogical composition〈/p〉〈/list-item〉 〈list-item〉 〈p xml:lang="en"〉Temporal variations in sediment composition pose constraints on the interpretation of provenance and dispersal based on individual samples〈/p〉〈/list-item〉 〈/list〉 〈/p〉

Description: DAAD

Description: German Ministry of Education and Research

Description: https://doi.org/10.5281/zenodo.7588054

Description: http://flood.umd.edu/

Description: Metamorphic textures in medium-grade (~500– 550°C) metasedimentary rocks from the Erzgebirge give evidence of prograde rutile crystallization from ilmenite. Newly-crystallized grains occur as rutile-rich polycrystalline aggregates that pseudomorph the shape of the ilmenites. In-situ trace element data (EMP and SIMS) show that rutiles from the higher-grade samples record large scatter in Nb content and have Nb/Ti ratios higher than coexisting ilmenite. This behavior can be predicted using prograde rutile crystallization from ilmenite and indicates that rutiles are reequilibrating their chemistry with remaining ilmenites. On the contrary, rutiles from the lowest grade samples (~480°C) have Nb/Ti ratios that are similar to the ones in ilmenite. Hence, rutiles from these samples did not equilibrate their chemistry with remaining ilmenites. Our data suggest that temperature may be one of the main factors determining whether or not the elements are able to diffuse between the phases and, therefore, reequilibrate. Newly-crystallized rutiles yield temperatures (from ~500 to 630°C, Zr-in-rutile thermometry) that are in agreement with the metamorphic conditions previously determined for the studied rocks. In quartzites from the medium-grade domain (~530°C), inherited detrital rutile grains are detected. They are identified by their distinct chemical composition (high Zr and Nb contents) and textures (single grains surrounded by fine grained ilmenites). Preliminary calculation, based on grain size distribution of rutile in medium-grade metapelites and quartzites that occur in the studied area, show that rutiles derived from quartzites can be anticipated to dominate the detrital rutile population, even if quartzites are a minor component of the exposure.

Description: Arquitectura sísmica-estratigráfica de la Formación Camaná del Oligoceno-Plioceno, Antearco del sur de Perú (Provincia de Arequipa). La Cuenca Camaná-Mollendo es una depresión de margen activo en el sur de Perú, la cual es elongada en sentido ~NW-SE y se extendiende desde la cordillera de la Costa hasta la fosa Perú-Chile. Esta cuenca consiste en un sistema de graben y semigrábenes y está rellenada con rocas sedimentarias de edad Oligoceno a Plioceno, correspondientes a la Formación Camaná (deltaico y fluvial, ~500 m de espesor). Una integración de datos provenientes de columnas estratigráficas, sísmica de reflexión 2D costa afuera, proveniencia sedimentaria, y geocronología de U-Pb en zircones volcanoclásticos ayudó a elaborar un cuadro tectono-cronoestratigráfico de toda la Cuenca Camaná-Mollendo. Para llevar a cabo esta integración, en primer lugar se requirió reinterpretar geológicamente la data sísmica 2D costa afuera y resaltar las características estratigráficas más prominentes (i.e., superficies erosivas), las cuales son atribuibles a la Formación Camaná. Estas características lograron ser correlacionadas con las superficies erosivas definidas en la Formación Camaná costa adentro y dieron como resultado la siguiente división: (i) “Unidad CamA” de la Formación Camaná (deltas de grano grueso) y (ii) “Unidad CamB” de la Formación Camaná (depósitos fluviales). La Unidad CamA se subdividió en tres subunidades en base a discontinuidades estratigráficas menores y diferencias en su geometría depositacional (i.e., A1: Oligoceno; A2: Mioceno inferior; y A3: Mioceno medio). La Unidad CamA refleja geometría progradante (A1 y A2) y “onlapante” (A3). La Unidad CamB (Mioceno superior a Plioceno) comprende conglomerados fluviales e hiperpícnicos de alta energía. Cada una de estas unidades y subunidades se extienden costa afuera de Camaná y mantienen similares geometrías depositacionales y los mismos límites secuenciales. En los depósitos costa afuera, las subunidades A1 y A2 (Oligoceno a Mioceno Inferior) están agrupadas como “A1+A2” debido a que ambos muestran similares geometrías progradacionales y es difícil diferenciarlos. Un sistema regresivo (RST) representa estas subunidades. Estos depósitos alcanzan ~2,5 km de espesor, y están intensamente afectados por fallas normales y lístricas asociados a geometrías depositacionales pinch-out. Los estratos de la subunidad A3 (Mioceno Medio) reflejan un sistema transgresivo (TST), y cubren toda la cuenca con sedimentos finos. La subunidad A3 alcanza ~1 km de espesor, y se caracteriza por su geometría “onlapante”, y menor proporción de tectónica sinsedimentaria. Finalmente, la depositación de la Unidad CamB (Mioceno Superior a Plioceno) ocurrió durante un nuevo episodio regresivo (RST), la cual se vuelve deltaica y progradacional costa afuera y está mucho menos afectada por fallas sinsedimentarias. Los límites estratigráficos entre “A1+A2” y A3, y entre A3 y CamB observados costa adentro se utilizan para diferenciar, correlacionar y predecir las principales geometrías depositacionales y sistemas depositacionales encadenados interpretados para los depósitos costa afuera. Los reflectores sísmicos de alta frecuencia representan tales límites y apoyan la subdivisión de la Formación Camaná costa afuera. Estos límites son además utilizados para definir depocentros a lo largo de la Cuenca Camaná- Mollendo, donde los depocentros más voluminosos están ubicados en las cercanías de los grandes valles (e.g., Planchada, Camaná y Punta de Bombón). Los depósitos de las subunidades “A1+A2” son considerados como un potencial reservorio de hidrocarburos debido a su alta tasa de sedimentación. Los depósitos de la subunidad A3 son transgresivos y considerados como una potencial roca sello. Estructuralmente, la Cuenca Camaná-Mollendo está compuesta por elementos estructurales propios de sistemas de grábenes y semi-grábenes, los cuales están orientados preferencialmente ~NW-SE (orientación andina)