Hasil untuk "Geology"

R. F. Flint

D. H. Griffiths, R. Barker

P. Hoffman, A. Bally, A. R. Palmer

Rachael S. Skye, Erin G. Teich

Many tools and techniques measure local structure in materials in contexts ranging from biology to geology. We provide a survey of those tools and metrics that are especially useful for analyzing particulate soft matter. The metrics we discuss can all be computed from the positions of particles, and are thus most useful when there is access to this information, either from simulation or experimental imaging. For each metric, we provide derivations, intuition regarding its implications, example uses, and references to software packages that compute the metric. Our survey encompasses characterization techniques ranging from the simplest to the most complex, and will be useful for students getting started in the structural characterization of particle systems.

J. J. Carrera-Hernández

<p>This work presents Mexico's High Resolution Climate Database (MexHiResClimDB), which is a newly developed gridded, high-resolution climate dataset comprised of daily, monthly and yearly precipitation and temperature (<span class="inline-formula"><i>T</i>_min</span>, <span class="inline-formula"><i>T</i>_max</span>, <span class="inline-formula"><i>T</i>_avg</span>). This new database provides the largest temporal coverage of the aforementioned climate variables at the highest spatial resolution (20 arcsec, or 560 m on Mexico's CCL projection) when compared to the other currently available gridded datasets for Mexico and its development has allowed for the analysis of the country's climate extremes for the 1951–2020 period. By comparing the spatial distribution of precipitation from the MexHiResClimDB with other gridded data (Daymet, L15, CHIRPS and PERSIANN CDR), it was found that the precipitation provided by this new dataset adequately represents the spatial variation of extreme precipitation events, in particular for the precipitation that occurred during 15–16 September 2013, caused by the presence of Tropical storm Manuel in the Pacific Ocean and Hurricane Ingrid (Cat 1) in the Gulf of Mexico. Using data from 61 days retrieved from Automated Weather Stations located throughout Mexico – and correspoding to the two months with the largest precipitation in Mexico – it was found that precipitation data from MexHiResClimDB has the lowest MAE (8.7 mm), compared to those of L15 (9.5 mm), Daymet (10.1 mm) and CHIRPS (11.7 mm). For <span class="inline-formula"><i>T</i>_min</span> and <span class="inline-formula"><i>T</i>_max</span>, the lowest MAE was obtained with MexHiResClimDB (1.7 and 1.8 °C, respectively), followed by Daymet (2.0 °C for both temperatures) and L15 (2.4 and 2.5 °C). With this new database an analysis of the extreme events of precipitation and temperature in Mexico for the 1951–2020 period was undertaken: the wettest year was 1958, the wettest day 26 September 1970, and September of 2013 the wettest month. It was also found that eight out of the ten days with the highest <span class="inline-formula"><i>T</i>_min</span> occurred in 2020, the two months with the highest <span class="inline-formula"><i>T</i>_min</span> were July and August of 2020 and that the six years with the highest <span class="inline-formula"><i>T</i>_min</span> were 2015–2020. When <span class="inline-formula"><i>T</i>_max</span> was analysed, it was found that the hottest day was 15 June 1998, while June of 1998 was the hottest month and 2020 the hottest year, and that the four hottest years occurred between 2011–2020. Nationwide (and considering 1961–1990 as the baseline period), <span class="inline-formula"><i>T</i>_min</span>, <span class="inline-formula"><i>T</i>_avg</span> and <span class="inline-formula"><i>T</i>_max</span> have increased, with their anomalies drastically increasing in recent years and reaching values above 1.0 °C in 2020. At the same time, precipitation has also decreased in recent years – which combined with the increase in temperature will have severe impacts on water availability. This new database provides a tool to quantify – in detail – the spatio-temporal variability of climate throughout Mexico.</p> <p>The MexHiResClimDB entire dataset is available on Figshare (<a href="https://doi.org/10.6084/m9.figshare.c.7689428.v2">https://doi.org/10.6084/m9.figshare.c.7689428.v2</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx16">Carrera-Hernández</a>, <a href="#bib1.bibx16">2025</a><a href="#bib1.bibx16">a</a></span>).</p>

Zhenang Cui, Yueming Hou

The opening of the Qiongzhou Strait during the Holocene was a significant geological event in the Beibu Gulf, profoundly influencing sediment provenance and ocean circulation systems. Due to the scarcity of geological records documenting this event, the understanding of regional Holocene sedimentary evolution has been constrained. To investigate the impact of this event on sediment provenance and ocean currents in the Beibu Gulf, geochemical analyses were conducted on sediment core SO-31 retrieved from the South China Sea. The sediments in core SO-31 were stratigraphically divided into three units based on vertical geochemical profiles, reflecting changes in sea level and shifts in sediment provenance within the study area. The Th/Cr vs. Th/Sc scatter plot for core SO-31 indicate that sedimentary materials primarily originated from the Red River during 11,400–7700 a BP, and a significant change in provenance occurred in the study region around 7700 a BP, characterized by increased contributions from the Qiongzhou Strait and decreased contributions from the Red River. This suggests that the opening of the Qiongzhou Strait significantly influenced the sediment supply to the central Beibu Gulf around 7700 a BP. These findings provide critical geochemical evidence for studying the Qiongzhou Strait opening event and enhance our understanding of Holocene sedimentary evolution and “source–sink” transitions in the Beibu Gulf.

Laurent Duval, Frédéric Payan, Christophe Preux et al.

The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies. We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability. Among remedies, lossless and lossy compression techniques are becoming popular to better manage dataset volumes. Performance assessment -- with comparative benchmarks -- require open datasets shared under FAIR principles (Findable, Accessible, Interoperable, Reusable), with MRE (Minimal Reproducible Example) ancillary data for reuse. We share LUNDIsim, an exemplary faulted geological mesh. It is inspired by SPE10 comparative Challenge. Enhanced by porosity/permeability datasets, this dataset proposes four distinct subsurface environments. They were primarily designed for flow simulation in porous media. Several consistent resolutions (with HexaShrink multiscale representations) are proposed for each model. We also provide a set of reservoir features for reproducing typical two-phase flow simulations on all LUNDIsim models in a reservoir engineering context. This dataset is chiefly meant for benchmarking and evaluating data size reduction (upscaling) or genuine composite mesh compression algorithms. It is also suitable for other advanced mesh processing workflows in geology and reservoir engineering, from visualization to machine learning. LUNDIsim meshes are available at https://doi.org/10.5281/zenodo.14641958

S. D. Zeigler, M. Baker, J. R. Metcalf et al.

<p>The conventional zircon (U–Th) <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="e653eaf840568ee76bb20ba3bf368ae0"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-199-2024-ie00004.svg" width="8pt" height="14pt" src="gchron-6-199-2024-ie00004.png"/></svg:svg></span></span> He (ZHe) method typically uses microscopy measurements of the dated grain together with the assumption that the zircon can be appropriately modeled as a geometrically perfect tetragonal or ellipsoidal prism in the calculation of volume (<span class="inline-formula"><i>V</i></span>), alpha-ejection correction (<span class="inline-formula"><i>F</i>_T</span>), equivalent spherical radius (<span class="inline-formula"><i>R</i>_FT</span>), effective uranium concentration (eU), and corrected (U–Th) <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="36bd7baae116a5efc17e692d563c2b51"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-199-2024-ie00005.svg" width="8pt" height="14pt" src="gchron-6-199-2024-ie00005.png"/></svg:svg></span></span> He date. Here, we develop a set of corrections for systematic error and determine uncertainties to be used in the calculation of the above parameters for zircon, using the same methodology as Zeigler et al. (2023) for apatite. Our approach involved acquiring both “2D” microscopy measurements and high-resolution “3D” nano-computed tomography (CT) data for a suite of 223 zircon grains from nine samples showcasing a wide range of morphology, size, age, and lithological source, calculating the <span class="inline-formula"><i>V</i></span>, <span class="inline-formula"><i>F</i>_T</span>, and <span class="inline-formula"><i>R</i>_FT</span> values for the 2D and 3D measurements and comparing the 2D vs. 3D results. We find that the values derived from the 2D microscopy data overestimate the true 3D <span class="inline-formula"><i>V</i></span>, <span class="inline-formula"><i>F</i>_T</span>, and <span class="inline-formula"><i>R</i>_FT</span> values for zircon, with one exception (<span class="inline-formula"><i>V</i></span> of ellipsoidal grains). Correction factors for this misestimation determined by regressing the 3D vs. 2D data range from 0.81–1.04 for <span class="inline-formula"><i>V</i></span>, 0.97–1.0 for <span class="inline-formula"><i>F</i>_T</span>, and 0.92–0.98 for <span class="inline-formula"><i>R</i>_FT</span>, depending on zircon geometry. Uncertainties (1<span class="inline-formula"><i>σ</i></span>) derived from the scatter of data around the regression line are 13 %–21 % for <span class="inline-formula"><i>V</i></span>, 5 %–1 % for <span class="inline-formula"><i>F</i>_T</span>, and 8 % for <span class="inline-formula"><i>R</i>_FT</span>, again depending on zircon morphologies. Like for apatite, the main control on the magnitude of the corrections and uncertainties is grain geometry, with grain size being a secondary control on <span class="inline-formula"><i>F</i>_T</span> uncertainty. Propagating these uncertainties into a real dataset (<span class="inline-formula"><i>N</i>=28</span> ZHe analyses) generates 1<span class="inline-formula"><i>σ</i></span> uncertainties of 12 %–21 % in eU and 3 %–7 % in the corrected ZHe date when both analytical and geometric uncertainties are included. Accounting for the geometric corrections and uncertainties is important for appropriately reporting, plotting, and interpreting ZHe data. For both zircon and apatite, the Geometric Correction Method is a practical and straightforward approach for calculating more accurate (U–Th) <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M25" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="64e3733ac81609367f37ca130d7132b9"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gchron-6-199-2024-ie00006.svg" width="8pt" height="14pt" src="gchron-6-199-2024-ie00006.png"/></svg:svg></span></span> He data and for including geometric uncertainty in eU and date uncertainties.</p>

K. Anderson, K. Anderson, K. Anderson et al.

<p>The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.</p>

Yongqiang Sun, Yongping Zhang, Anqi Wei et al.

Foam drainage agents enhance gas production by removing wellbore liquids. However, due to the ultra-high salinity environments of the Hechuan gas field (salinity up to 32.5 × 10⁴ mg/L), no foam drainage agent is suitable for this gas field. To address this challenge, we developed a novel nanocomposite foam drainage system composed of quaternary ammonium and two types of nanoparticles. This work describes the design and synthesis of a quaternary ammonium foam drainage agent and nano-engineered stabilizers. Nonylphenol polyoxyethylene ether sulfosuccinate quaternary ammonium foam drainage agent was synthesized using maleic anhydride, sodium chloroacetate, N,N-dimethylpropylenediamine, etc., as precursors. We employed the Stöber method to create hydrophobic silica nanoparticles. Carbon quantum dots were then prepared and functionalized with dodecylamine. Finally, carbon quantum dots were incorporated into the mesopores of silica nanoparticles to enhance stability. Through optimization, the best performance was achieved with a (quaternary ammonium foam drainage agents)–(carbon quantum dots/silica nanoparticles) ratio of 5:1 and a total dosage of 1.1%. Under harsh conditions (salinity 35 × 10⁴ mg/L, condensate oil 250 cm³/m³, temperature 80 °C), the system exhibited excellent stability with an initial foam height of 160 mm, remaining at 110 mm after 5 min. Additionally, it displayed good liquid-carrying capacity (160 mL), low surface tension (27.91 mN/m), and a long half-life (659 s). These results suggest the effectiveness of nanoparticle-enhanced foam drainage systems in overcoming high-salinity challenges. Previous foam drainage agents typically exhibited a salinity resistance of no more than 25 × 10⁴ mg/L. In contrast, this innovative system demonstrates a superior salinity tolerance of up to 35 × 10⁴ mg/L, addressing a significant gap in available agents for high-salinity gas fields. This paves the way for future development of advanced foam systems for gas well applications with high salinity.

Mercedes Arauzo Sánchez, María Valladolid Martín, Delia M. Andries

La investigación se centra en analizar el impacto de la contaminación por nitrato en las masas de aguas subterráneas de la Demarcación Hidrológica del Segura bajo un enfoque fuente-vía-receptor y su particular incidencia en el área de captación de la laguna litoral del Mar Menor, con los siguientes objetivos específicos: (1) valorar la eficacia de las zonas vulnerables a la contaminación por nitrato (ZVNs) mediante el análisis estadístico de los niveles de contaminación en sus masas de agua subterránea durante el periodo 2010-2021; (2) analizar la distribución actual del nitrato en las masas de agua subterránea para identificar las zonas contaminadas y en riesgo; (3) delimitar y caracterizar las áreas de captación de las zonas afectadas por la contaminación; y (4) explorar el papel del medio físico y de los usos del suelo en la distribución de las zonas contaminadas por nitrato mediante el análisis de componentes principales (ACP) de sus áreas de captación. Dado el grave estado de degradación ambiental del Mar Menor, se analiza con especial atención la incidencia de la contaminación por nitrato en las aguas subterráneas (receptoras primarias) que drenan a la laguna (receptora secundaria) y se identifican los territorios que conforman su área de captación. No se han observado diferencias interanuales estadísticamente significativas en los niveles de nitrato de las masas de agua subterránea afectadas por contaminación de nitrato durante el periodo 2010-2021, lo que sugiere que las ZVNs designadas en la Demarcación del Segura no parecen estar cumpliendo, por el momento, con las expectativas de reducir la contaminación. Utilizando el área de captación de las zonas contaminadas como unidad de análisis, el ACP evidenció la relación directa entre la extensión de las superficies contaminadas y la agricultura intensiva de cultivos de herbáceos en regadío, cítricos y frutales (grupos de cultivos que presentan los mayores excedentes anuales de N de la Demarcación). En contraposición, las áreas de montaña, con abundancia de bosques y escasa presencia agrícola, constituyen un claro elemento protector para los recursos hídricos frente a los procesos de contaminación por el nitrato procedente de fuentes difusas. La situación de emergencia ecológica del Mar Menor es consecuencia de su grave estado de eutrofización, al que contribuye el sobreexceso de N procedente de descargas subterráneas ricas en nitrato desde el acuífero Cuaternario de la masa de agua Campo de Cartagena. A la vista de los resultados y como paso previo al reforzamiento de los programas de acción de las ZVNs de la Demarcación del Segura, se propone analizar la idoneidad de las 89 ZVNs designadas en la Demarcación mediante validaciones frente a un mapa de vulnerabilidad específica a la contaminación por nitrato y revisarlas si procede.

Wan-Qian Zhao, Zhan-Yong Guo, Yu-Qi Guo et al.

This groundbreaking research extracted DNA from petroleum using nanoparticle affinity bead technology, yielding 3,159,020 petroleum DNA (pDNA) sequences, primarily environmental DNA. While most original in situ DNA (oriDNA) was lost, ancient DNA (aDNA) from petroleum offers an important source of ecological and evolutionary information, surpassing traditional fossils. This study reveals that oil, mainly sourced from algae and lower aquatic plants, now serves as a new type of fossil, providing detailed insights into Earth's hidden history, including unclassified species and ancient events, revolutionizing petroleum geology and paleontology.

Francesco Pappone, Federico Califano, Marco Tafani

Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the country level. We employ a one-dimensional ConvNeXt1D neural network architecture to classify mineral spectra based solely on their spectral signatures. The processed dataset comprises over 32,900 mineral samples, predominantly natural, spanning 101 countries. Through five-fold cross-validation, the ConvNeXt1D model achieved an impressive average classification accuracy of 93%, demonstrating its efficacy in capturing geospatial patterns inherent in Raman spectra.

V.G. Bakhmutov, O.V. Mytrokhyn, I.B. Poliachenko et al.

A palaeomagnetic study of rocks for two Palaeoproterozoic anorthosite-mangerite-charnockite-granite (AMCG) complexes in the Ukrainian Shield was done to put additional constraints on the interpretation of palaeogeography of Fennoscandia and Volgo-Sarmatia in the Palaeoproterozoic. With this study, 5 sites of Korsun-Novomyrhorod and 3 sites of Korosten AMCG complexes in central and north-western parts of the shield, respectively, were chosen for palaeomagnetic sampling given the geological, modern geochronological and previous palaeomagnetic data. Primary remanent magnetization was isolated on samples of anorthosites, Gabbro, and monzonites within a narrow time interval of U-Pb geochronology dataset of 1.76—1.75 Ga. The palaeomagnetic poles calculated for Korosten and Korsun-Novomyrhorod complexes are almost identical, which indicates that the Volyn and Ingul Domains developed within a single structure of the Ukrainian Shield since at least 1.75 Ga. The new palaeomagnetic pole calculated for all 8 sites (Plat=22.7 °N, Plon=167.4 °E, A95=3.3°) agrees well with previous studies by Elming et al. [2001, 2010]. The selection of the most reliable palaeomagnetic poles for Fennoscandia and Volgo-Sarmatia of this time indicates that the present position of the Ukrainian Shield relative to Fennoscandia is not the same as for about 1.75 Ga, when Fennoscandia occupied a subequatorial position within palaeolatitudes of 5—20 °N, and Volgo-Sarmatia was located close to the equator and rotated relative to Fennoscandia counterclockwise by about 40° compared to its present position.

Xiangli He, Yuandong Huang, Zhaoning Chen et al.

The second academic forum of the Committee on the Earthquake Hazard Chain, Seismological Society of China was held on 12 November 2022 in Beijing, China. The theme of this forum was theoretical research, technical application and popularization of science related to the earthquake hazard chain. It includes an opening ceremony and online lecture presentations. The work related to disaster prevention, mitigation, and relief for the earthquake hazard chain has been widely concerned. There are 49 speeches or lectures at the conference. The contents involve multiple stages and aspects of earthquake hazard chain, such as formation mechanism, database establishment, identification methods, risk assessment, monitoring and early warning, post-disaster rescue and reconstruction, and hazard prevention measures. This activity specially promotes the communication on the first few aspects. In future, the study on monitoring and early warning, emergency response and rescue, post-disaster reconstruction, and other related science and technology of earthquake hazard chain should be pay more attention.

Li-Xin Guo, Meng-Long Hsieh, Olga Gorodetskaya et al.

Abstract The Yellow River Plain (YRP), being regarded as the cradle of Chinese civilization, is traditionally thought to be the locale of the Great Flood, a hazardous flood (or floods) tamed by Yu who started China’s first “dynasty”, Xia, in ~ 2000 BC. However, by integrating published archaeological data, we propose that the Great Flood in fact impacted the Jianghan Plain (JHP) along the middle course of the Yangtze River. The arguments include: (1) around the era of the Great Flood, the most civilized and populated society in East Asia, named the Jianghan society, was located around the JHP (at that time, the habitation on the YRP remained limited); (2) the Jianghan society lived on river resources (shipping and rice growing) and was thus subject to flood risks (but not for the people inhabiting the YRP); (3) the people in the Jianghan society were experienced in dredging moats/ditches for shipping and irrigation; (4) unlike the floods on the YRP that were characterized by dynamic sedimentation and channel avulsion, those on the JHP typically occurred with slow-moving water manageable to ancient people; (5) the JHP has been associated with lake/wetland systems serving as detention basins during floods. Here, the recorded method for controlling the Great Flood, dredging channels to divert flood water to a “sea”, was feasible. Known speleothem paleo-rainfall data from multiple sites show that the climate of the JHP had been wet since the middle Holocene (earlier than the era of the Great Flood) and significantly turned dry after ~ 1850 BC (~ 150 years later than the Great Flood). Thus, the uniqueness of the Great Flood was likely to reflect an increase in land use on the JHP with the expansion of the Jianghan society, and the success in taming this flood was mainly due to the efforts of the society, not by luck.

Fanny Lehmann, Filippo Gatti, Michaël Bertin et al.

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.

Gaolei Jiang, Nai'ang Wang, Dayou Zhai et al.

Limnocythere inopinata (Baird, 1843) is a widely distributed ostracod in modern non-marine waters and Quaternary sediments. Based on its morphological variation (the number and position of its nodes), different phenotypes have been identified. However, the factors controlling its morphological variation are currently open to debate, which hinders palaeoecological reconstructions based on this species. In this study, ostracod distribution and hydrochemical analyses of the ambient environment of 21 lakes in the Badain Jaran Desert were carried out. Three ostracod species belonging to two genera are identified as Limnocyhtere inopinata, Cypris cf. granulate and Cypris sp. with the dominant species L. inopinata represented by six phenotypes. The distribution features of these ostracods in the lakes and related ecological information are reported. In addition, the factors controlling the morphological variations of L. inopinata were analysed. Our data indicate that the noded individuals of L. inopinata prefer water with appropriate Ca2+ content (30–40 mg L−1) and low salinities (below 4.60 g L−1). The percentage of noded individuals of L. inopinata increases with increasing salinity only within a certain salinity range. Hence, caution should be exercised in reconstructions of palaeosalinity based on the morphological variability of L. inopinata.

Yusuf Nasir, Louis J. Durlofsky

A general control policy framework based on deep reinforcement learning (DRL) is introduced for closed-loop decision making in subsurface flow settings. Traditional closed-loop modeling workflows in this context involve the repeated application of data assimilation/history matching and robust optimization steps. Data assimilation can be particularly challenging in cases where both the geological style (scenario) and individual model realizations are uncertain. The closed-loop reservoir management (CLRM) problem is formulated here as a partially observable Markov decision process, with the associated optimization problem solved using a proximal policy optimization algorithm. This provides a control policy that instantaneously maps flow data observed at wells (as are available in practice) to optimal well pressure settings. The policy is represented by a temporal convolution and gated transformer blocks. Training is performed in a preprocessing step with an ensemble of prior geological models, which can be drawn from multiple geological scenarios. Example cases involving the production of oil via water injection, with both 2D and 3D geological models, are presented. The DRL-based methodology is shown to result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative to robust optimization over prior models, and to an average improvement of 4% in NPV relative to traditional CLRM. The solutions from the control policy are found to be comparable to those from deterministic optimization, in which the geological model is assumed to be known, even when multiple geological scenarios are considered. The control policy approach results in a 76% decrease in computational cost relative to traditional CLRM with the algorithms and parameter settings considered in this work.

Sébastien Fumeron, Bertrand Berche

Liquid crystals are assemblies of rod-like molecules which self-organize to form mesophases, in-between ordinary liquids and anisotropic crystals. At each point, the molecules collectively orient themselves along a privileged direction, which locally defines an orientational order. Sometimes, this order is broken and singularities appear in the form of topological defects. This tutorial article is dedicated to the geometry, topology and physics of these defects. We introduce the main models used to describe the nematic phase and discuss the isotropic-nematic phase transition. Then, we present the different families of defects in nematics and examine some of their physical outcomes. Finally, we show that topological defects are universal patterns of nature, appearing not only in soft matter, but also in biology, cosmology, geology and even particle physics.