Hasil untuk "Geology"

Anandaroop Ray, Andrew McPherson, Ross C. Brodie et al.

Time domain airborne electromagnetic (AEM) surveying is a mature geophysical tool for imaging the Earth's shallow subsurface. It produces images of the electromagnetic conductivity structure of the earth, down to depths of a few hundred metres. The AEM method is fast, with aircraft acquiring data at speeds of 100-300 km/hr, making it an ideal near-surface reconnaissance tool. The physics of the AEM method are sensitive primarily to the subsurface conductivity, which is influenced by a range of geological factors such as mineral content, porosity, and water content and chemistry. In addition, the inferred subsurface conductivity depends on the accurate measurement and modelling of airborne transmitter and receiver geometries. In this work, we present inferences of the subsurface conductivity over Lake Menindee, New South Wales, Australia, using data from various AEM systems over the period 2014-2024. The lake storage has varied dramatically over this time and while this difference in storage volume undoubtedly influences the near surface conductivity, a remarkably consistent interpretation of the regional geology emerges. While the upper ten metres of the modelled depth sections exhibit the greatest time-variability in inferred electromagnetic conductivity, a correlation of lakebed near-surface conductivity with the lake water volume cannot robustly be established. We also provide information theoretic calculations for each inversion result to aid in their quantitative comparison. The implications of our study are that subtle, shallow, hydrogeological changes are difficult to image with repeat overflights. Conversely, we establish that different AEM systems robustly image the regional geo-electric structure of the near surface, validated by known stratigraphy and borehole conductivity logs.

Penggang Li, Haiyang He, Fenghua Gu et al.

Accurate discrimination of andesite tectonic settings is critical for unraveling Earth’s geodynamic processes. However, existing studies face three key challenges: (1) simplified traditional methods, which rely on single-element ratios and fail to capture the complex petrogenetic processes of andesites; (2) poor performance on small samples, as rare tectonic types (RV) are often misclassified owing to data scarcity; and (3) limited geological interpretability, with most models lacking clear links between geochemical features and magmatic mechanisms. To address these issues, we propose a “dual-track” framework integrating machine learning and few-shot learning using 26,463 andesite samples from the GEOROC database. For large-sample scenarios, optimized ensemble models (Random Forest, XGBoost, LightGBM) achieve high precision, with an Area Under the Receiver Operating Characteristic Curve (AUC, a metric reflecting overall classification performance) ≥ 0.99. LightGBM emerges as the dominant model, with a recall rate of 97% for small-sample RV. For rare tectonic types, a meta-learning (TabPFN pre-training) and knowledge distillation (transfer to CatBoost) framework boosts the recall rates of RV and OI to 99% while optimizing the inference speed to 0.01 seconds per sample. SHAP analysis identifies key discriminant elements (e.g., TiO2 and FeOt for CM; Nb and Lu for OI) and their synergistic effects, verifying classical magmatic theories (e.g., Fe-Ti oxide differentiation in subduction zones). This framework provides a reproducible standard for intermediate igneous rock classification, aiding paleotectonic reconstruction and mineral exploration in the future.

Venkatesan Selvaraj, Saradhambal Ramachandran Singarasubramania, Parthasarathy Pandu et al.

Abstract This study examines the presence of heavy metals (HMs) and their environmental impacts in samples collected from the surface sediments of Karaikkal Beach, located on the southeastern coast of India. To assess the textural attributes and heavy metal content in the area, 26 sediment samples were collected and analyzed using atomic absorption spectroscopy (AAS). The sediments are composed primarily of sand (98.56%), followed by silt (1.2%), clay (0.41%), and calcium carbonate, which ranges from 3.19% to 6.71%, with a mean value of 4.77% present at a significant level. Organic inputs from riverine sources were also observed to influence sediment composition, and the average organic matter concentration is 0.52%, with values ranging from 0.26% to 0.75%. The HM concentrations followed the descending order: Fe (22,434.42–36,525.69 µg/g) > Mn (230.15–395.49 µg/g) > Cr (114.33–244.63 µg/g) > Ni (13.60–24.22 µg/g) > Pb (29.89–55.96 µg/g) > Cu (22.47–36.52 µg/g) > Zn (24.14–40.69 µg/g) > Co (12.00–20.32 µg/g). Fe and Mn concentrations were primarily controlled by fluvial inputs and terrestrial influences. The derived indices such as enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI), sediment pollution index (SPI), and potential ecological risk index (PERI) reveal that the coastal sediments mostly fall within the unpolluted to slightly polluted categories, indicating a low ecological threat. The origin of metal enrichment in the sediment fractions is attributed to natural geogenic sources. The sources of HMs and their inter-element associations were interpreted using principal component analysis (PCA) and a correlation matrix. These baseline data underscore the importance of continuous environmental monitoring to identify emerging pollution patterns and guide sustainable coastal management.

Jiadong Lou, Bingqing Liu, Yuanheng Xiong et al.

Phytoplankton absorb and scatter light in unique ways, subtly altering the color of water, changes that are often minor for human eyes to detect but can be captured by sensitive ocean color instruments onboard satellites from space. Hyperspectral sensors, paired with advanced algorithms, are expected to significantly enhance the characterization of phytoplankton community composition, especially in coastal waters where ocean color remote sensing applications have historically encountered significant challenges. This study presents novel machine learning-based solutions for NASA's hyperspectral missions, including EMIT and PACE, tackling high-fidelity retrievals of phytoplankton absorption coefficient and chlorophyll a from their hyperspectral remote sensing reflectance. Given that a single Rrs spectrum may correspond to varied combinations of inherent optical properties and associated concentrations, the Variational Autoencoder (VAE) is used as a backbone in this study to handle such multi-distribution prediction problems. We first time tailor the VAE model with innovative designs to achieve hyperspectral retrievals of aphy and of Chl-a from hyperspectral Rrs in optically complex estuarine-coastal waters. Validation with extensive experimental observation demonstrates superior performance of the VAE models with high precision and low bias. The in-depth analysis of VAE's advanced model structures and learning designs highlights the improvement and advantages of VAE-based solutions over the mixture density network (MDN) approach, particularly on high-dimensional data, such as PACE. Our study provides strong evidence that current EMIT and PACE hyperspectral data as well as the upcoming Surface Biology Geology mission will open new pathways toward a better understanding of phytoplankton community dynamics in aquatic ecosystems when integrated with AI technologies.

Ethan Fulcher, J. Diego Caporale, Yifeng Zhang et al.

In-situ robotic exploration is an important tool for advancing knowledge of geological processes that describe the Earth and other Planetary bodies. To inform and enhance operations for these roving laboratories, it is imperative to understand the terramechanical properties of their environments, especially for traversing on loose, deformable substrates. Recent research suggested that legged robots with direct-drive and low-gear ratio actuators can sensitively detect external forces, and therefore possess the potential to measure terrain properties with their legs during locomotion, providing unprecedented sampling speed and density while accessing terrains previously too risky to sample. This paper explores these ideas by investigating the impact of gait on proprioceptive terrain sensing accuracy, particularly comparing a sensing-oriented gait, Crawl N' Sense, with a locomotion-oriented gait, Trot-Walk. Each gait's ability to measure the strength and texture of deformable substrate is quantified as the robot locomotes over a laboratory transect consisting of a rigid surface, loose sand, and loose sand with synthetic surface crusts. Our results suggest that with both the sensing-oriented crawling gait and locomotion-oriented trot gait, the robot can measure a consistent difference in the strength (in terms of penetration resistance) between the low- and high-resistance substrates; however, the locomotion-oriented trot gait contains larger magnitude and variance in measurements. Furthermore, the slower crawl gait can detect brittle ruptures of the surface crusts with significantly higher accuracy than the faster trot gait. Our results offer new insights that inform legged robot "sensing during locomotion" gait design and planning for scouting the terrain and producing scientific measurements on other worlds to advance our understanding of their geology and formation.

Dakota K. Keblbeck, Eric Mayotte, Uwe Greife et al.

We present a comprehensive Monte Carlo simulation of muon-induced backgrounds for the Colorado Underground Research Institute (CURIE), a shallow-underground facility with $\approx 415$~m.w.e. overburden. Using coupled \textsc{mute} and \textsc{geant4} frameworks, we characterize the production and transport of muon-induced secondaries through site-specific rock compositions and geometries, establishing a proof-of-concept for high-precision, end-to-end simulations. Our simulations employ angular-dependent muon energy distributions, which improve secondary flux accuracy. For the Subatomic Particle Hideout and Cryolab I research spaces, we predict total muon-induced neutron fluxes of $(8.52 \pm 1.30_{\text{sys}}) \times 10^{-3}$~m$^{-2}$s$^{-1}$ and $(8.86 \pm 1.62_{\text{sys}}) \times 10^{-3}$~m$^{-2}$s$^{-1}$, respectively. Additionally, we develop a Depth-Intensity Relation (DIR) to predict the muon-induced neutron flux as a function of facility depth, which is consistent with measurements across a broad range of underground depths. These results provide quantitative background predictions for experimental design and sensitivity projections at shallow- and deep-underground facilities. They further demonstrate that local geology and overburden geometry influence muon-induced secondary yields and energy spectra, emphasizing the need for site-specific simulations for accurate underground background characterization. Therefore, the simulation framework has been made publicly available at \href{https://doi.org/10.5281/zenodo.17196581}{https://doi.org/10.5281/zenodo.17196581}, for the broader low-background physics community to enable meaningful inter-facility comparisons.

GUAN Wenjie, PENG Xiaolong, ZHU Suyang, YANG Chen, PENG Zhen, MA Xiaoran

During the development of middle and deep gas reservoirs in South Sichuan, conventional reservoir engineering methods—such as fracture propagation, stress-induced analysis, and numerical simulation—render productivity prediction of infilling wells laborious and ineffective in addressing variations in production capacity across different production stages, with stringent application conditions. In order to quickly and accurately predict the production capacity of infilling wells, this study classifies the “three-stage” declining trend observed in the production pressure curves of existing wells into: (1) A drastic decline period, regarded as the initial water production stage; (2) a rapid decline period; and (3) a slow decline period, both considered part of the later gas production stage. The Grey Wolf Optimizer(GWO) algorithm, a fast optimization algorithm with adaptive capabilities and an information feedback mechanism, is applied for hyperparameter optimization of the Long Short-term Memory (LSTM) neural network. Two stage-specific models were constructed, with the number of hidden layer neurons, dropout rate, and batch size determined by the optimal solutions obtained via GWO. The number of iterations was selected based on the loss curve and performance metric curve, while a linear warm-up strategy was used to dynamically adjust the learning rate, facilitating high-speed training and the formation of a staged productivity prediction model. Example studies show that the GWO-optimised LSTM neural network model achieves rapid convergence with a preset learning rate of 0.002 and 450 iterations, ultimately reaching a performance index of 0.923. Compared to the conventional LSTM neural network model, the average absolute errors during the early and later stages are reduced by 1.290 m3/d and 0.213 × 104 m3/d, respectively. Compared with numerical simulation fitting results, the average absolute error in gas production prediction is reduced by 0.24 × 104 m3/d. Therefore, the improved LSTM neural network model demonstrates excellent performance in capacity prediction across different production stages, and the stage-specific productivity variations in infilling wells within middle and deep shale gas reservoirs in South Sichuan. This provides a theoretical foundation for productivity prediction methods of infilling wells.

Lianhe Wang, Guangfeng Liu, Zhan Meng et al.

A robust and pragmatical technique was developed to classify flow pathways during long-term waterflooding operations in a hydrocarbon reservoir. More specifically, pore structure analysis, wettability tests, relative permeability tests, and long-term waterflooding experiments were conducted and integrated. Then, effects of pore-throat structures, displacement rates, crude oil viscosities, and wettability on the oil displacement efficiency across different flow pathways were systematically investigated, allowing us to classify flow pathways into the primary and secondary ones. For the former, pore-throat structure significantly affects the efficiency of displacement: for mouth-bar microfacies, cores with larger pore-throat radii and lower fractal dimensions exhibit superior displacement performance, whereas, for point-bar microfacies, it exhibits greater sensitivity to variations in injection parameters. Increasing the injection rate from 0.2 mL/min to 0.5 mL/min can lead to a 7.31% improvement in oil recovery. Also, high-viscosity crude oil leads to an overall decline in displacement efficiency, with a more pronounced reduction observed in the point-bar microfacies, suggesting that complex pore-throat structures are more sensitive to viscous resistance. For the latter, wettability shows its dominant impact with an increase in oil recovery to 7.12% if the wettability index is increased from 0.17 to 0.21 in the point-bar microfacies.

Qinglong Cao, Yuntian Chen, Lu Lu et al.

Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to industry, yet hindering VLM research in academia. To address this challenge and foster sustainable and equitable VLM research, we present the Generalized Domain Prompt Learning (GDPL) framework. GDPL facilitates the transfer of VLMs' robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources. By leveraging small-scale domain-specific foundation models and minimal prompt samples, GDPL empowers the language branch with domain knowledge through quaternion networks, uncovering cross-modal relationships between domain-specific vision features and natural vision-based contextual embeddings. Simultaneously, GDPL guides the vision branch into specific domains through hierarchical propagation of generated vision prompt features, grounded in well-matched vision-language relations. Furthermore, to fully harness the domain adaptation potential of VLMs, we introduce a novel low-rank adaptation approach. Extensive experiments across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, validate the efficacy of GDPL, demonstrating its ability to achieve state-of-the-art domain recognition performance in a prompt learning paradigm. Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry.

Riley C. W. O'Neill, Katrina Yezzi-Woodley, Jeff Calder et al.

Modern archaeological methods increasingly utilize 3D virtual representations of objects, computationally intensive analyses, high resolution scanning, large datasets, and machine learning. With higher resolution scans, challenges surrounding computational power, memory, and file storage quickly arise. Processing and analyzing high resolution scans often requires memory-intensive workflows, which are infeasible for most computers and increasingly necessitate the use of super-computers or innovative methods for processing on standard computers. Here we introduce a novel protocol for en-masse micro-CT scanning of small objects with a {\em mostly-automated} processing workflow that functions in memory-limited settings. We scanned 1,112 animal bone fragments using just 10 micro-CT scans, which were post-processed into individual PLY files. Notably, our methods can be applied to any object (with discernible density from the packaging material) making this method applicable to a variety of inquiries and fields including paleontology, geology, electrical engineering, and materials science. Further, our methods may immediately be adopted by scanning institutes to pool customer orders together and offer more affordable scanning. The work presented herein is part of a larger program facilitated by the international and multi-disciplinary research consortium known as Anthropological and Mathematical Analysis of Archaeological and Zooarchaeological Evidence (AMAAZE). AMAAZE unites experts in anthropology, mathematics, and computer science to develop new methods for mass-scale virtual archaeological research. Overall, our new scanning method and processing workflows lay the groundwork and set the standard for future mass-scale, high resolution scanning studies.

Hang Li, Guichun Luo, Mianshui Rong et al.

针对当前北京地区缺乏适用的覆盖层厚度与场地自振频率经验关系的现状，首先利用广泛收集的北京市地震安全性评价工作中获取的1 142个钻孔数据资料，采用幂函数模型对其进行回归拟合分析，获得了土体剪切波速随深度变化的关系式，然后对该关系式进行推导建立了适合北京地区场地特征的覆盖层厚度与场地自振频率之间的经验关系，藉此为北京市无剪切波速数据的工程场地提供vS20和vS30参考，并对土层剪切波速和覆盖层厚度进行预估。

Arkajyoti Pathak, Shikha Sharma

Large-scale subsurface hydrogen storage is critical for transitioning towards renewable, economically viable, and emission-free energy technologies. Although preliminary studies on geochemical interactions between different minerals, aqueous ions, and other dissolved gasses with H₂ have helped partially quantify the degree of hydrogen loss in the subsurface, the long-term changes in abiotic hydrogen–brine–rock interactions are still not well understood due to variable rates of mineral dissolution/precipitation and redox transformations under different conditions of reservoirs. One of the potentially understudied aspects of these complex geochemical interactions is the role of iron on the redox interactions and subsequent impact on long-term (100 years) hydrogen cycling. The theoretical modeling conducted in this study indicates that the evolution of secondary iron-bearing minerals, such as siderite and magnetite, produced after H₂-induced reductive dissolution of primary Fe³⁺-bearing phases can result in different degrees of hydrogen loss. Low dissolved Fe²⁺ activity (<10⁻⁴) in the formation water can govern the transformation of secondary siderite to magnetite within 100 years, eventually accelerating the H₂ consumption through reductive dissolution. Quantitative modeling demonstrates that such secondary iron mineral transformations need to be studied to understand the long-term behavior of hydrogen in storage sites.

A. Emran, C. M. Dalle Ore, C. J. Ahrens et al.

We map the surface of Pluto using an unsupervised machine learning technique using the near-infrared observations of the LEISA/Ralph instrument onboard NASA's New Horizons spacecraft. The principal component reduced Gaussian mixture model was implemented to investigate the geographic distribution of the surface units across the dwarf planet. We also present the likelihood of each surface unit at the image pixel level. Average I/F spectra of each unit were analyzed -- in terms of the position and strengths of absorption bands of abundant volatiles such as N${}_{2}$, CH${}_{4}$, and CO and nonvolatile H${}_{2}$O -- to connect the unit to surface composition, geology, and geographic location. The distribution of surface units shows a latitudinal pattern with distinct surface compositions of volatiles -- consistent with the existing literature. However, previous mapping efforts were based primarily on compositional analysis using spectral indices (indicators) or implementation of complex radiative transfer models, which need (prior) expert knowledge, label data, or optical constants of representative endmembers. We prove that an application of unsupervised learning in this instance renders a satisfactory result in mapping the spatial distribution of ice compositions without any prior information or label data. Thus, such an application is specifically advantageous for a planetary surface mapping when label data are poorly constrained or completely unknown, because an understanding of surface material distribution is vital for volatile transport modeling at the planetary scale. We emphasize that the unsupervised learning used in this study has wide applicability and can be expanded to other planetary bodies of the Solar System for mapping surface material distribution.

Kevin Fry, Jonathan E. Taylor

Many modern datasets, such as those in ecology and geology, are composed of samples with spatial structure and dependence. With such data violating the usual independent and identically distributed (IID) assumption in machine learning and classical statistics, it is unclear a priori how one should measure the performance and generalization of models. Several authors have empirically investigated cross-validation (CV) methods in this setting, reaching mixed conclusions. We provide a class of unbiased estimation methods for general quadratic errors, correlated Gaussian response, and arbitrary prediction function $g$, for a noise-elevated version of the error. Our approach generalizes the coupled bootstrap (CB) from the normal means problem to general normal data, allowing correlation both within and between the training and test sets. CB relies on creating bootstrap samples that are intelligently decoupled, in the sense of being statistically independent. Specifically, the key to CB lies in generating two independent "views" of our data and using them as stand-ins for the usual independent training and test samples. Beginning with Mallows' $C_p$, we generalize the estimator to develop our generalized $C_p$ estimators (GC). We show at under only a moment condition on $g$, this noise-elevated error estimate converges smoothly to the noiseless error estimate. We show that when Stein's unbiased risk estimator (SURE) applies, GC converges to SURE as in the normal means problem. Further, we use these same tools to analyze CV and provide some theoretical analysis to help understand when CV will provide good estimates of error. Simulations align with our theoretical results, demonstrating the effectiveness of GC and illustrating the behavior of CV methods. Lastly, we apply our estimator to a model selection task on geothermal data in Nevada.

Simone Marchi, Carol A. Raymond, Christopher T. Russell

The NASA Dawn spacecraft took off from Cape Canaveral in September 2007 atop a Delta II rocket starting an ambitious journey to Vesta and Ceres, the two most massive worlds in the largest reservoir of asteroids in the Solar System, the Main Belt. Prior to the Dawn launch, Earth-bound observations of Vesta and Ceres revealed intriguing features--from Vesta's rugged shape to Ceres' tenuous water exosphere--, but these objects remained fuzzy speckles of light even through the lenses of the most powerful telescopes. With Dawn's exploration of Vesta (2011-2012) and Ceres (2015-2018) these two worlds came into focus. Breath-taking details emerged of how large collisions sculpted Vesta liberating massive amounts of material in the inner Main Belt, providing the source of an important family of meteorites recovered on Earth. Ceres' complex geology, which may rival that of the Earth and Mars, unveiled recent cryovolcanic activity. This book is dedicated to these highlights, and many more discoveries of the Dawn mission. By the time Dawn completed its mission in 2018, our understanding of the formation of the Solar System had greatly evolved thanks to new theoretical models and to a new trove of meteorite geochemical data, and Dawn observations of Vesta and Ceres provide new, vital constraints to synergistically interpret models and data. The broader implications of the Dawn legacy are presented in a series of dedicated chapters. The editors hope this book will serve as a solid reference for the younger generations as well as for more seasoned researchers to successfully pursue future exploration of the Main Belt. We certainly have learned a lot thanks to Dawn, and yet we know that we have barely scratched the surface of what Main Belt asteroids can tell us about the dawn of our Solar System.

Jian HUANG, Xin LI, Fang CHEN et al.

The traditional high-level remote landslide recognition efficiency which relies on the artificial discrimination of geological experts is low. In this paper, an automatic landslide terrain recognition model based on deep learning is developed to improve the efficiency of the screening of potential landslide hazard in a large area. The model takes remote sensing images, DEM data, geological zones, river system and other geological observation data of the target area as input. For the huge difference of different types of observation data, a feature branch network is designed and constructed to accurately extract the corresponding landslide features: Among them, deep network architecture is used to extract complex features from optical image data, and shallow network architecture is used to extract features from structured data such as altitude, geological composition, river and fault zone distribution. Subsequently, a feature fusion module was designed to fuse the extraction results of the two networks to obtain a comprehensive landslide hazard feature. The model performs semantic segmentation of the landslide area based on the extracted landslide features, and achieves accurate pixel-level landslide terrain classification and positioning. The experimental results show that the recognition accuracy(ACC) of the model reaches 0.85, which can provide technical support for automatic landslide identification.

Gang Cheng, Wentao Xu, Bin Shi et al.

Understanding the spatiotemporal evolution of overburden deformation during coal mining is still a challenge in engineering practice due to the limitation of monitoring techniques. Taking the Yangliu Coal Mine as an example, a similarity model test was designed and conducted to investigate the deformation and failure mechanism of overlying rocks in this study. Distributed fiber optic sensing (DFOS), high-density electrical resistivity tomography (HD-ERT) and close-range photogrammetry (CRP) technologies were used in the test for comprehensive analyses. The combined use of the three methods facilitates the investigation of the spatiotemporal evolution characteristics of overburden deformation, showing that the mining-induced deformation of overburden strata was a dynamic evolution process. This process was accompanied by the formation, propagation, closure and redevelopment of separation cracks. Moreover, the key rock stratum with high strength and high-quality lithology played a crucial role in the whole process of overburden deformation. There were generally three failure modes of overburden rock layers, including bending and tension, overall shearing, and shearing and sliding. Shear failure often leads to overburden falling off in blocks, which poses a serious threat to mining safety. Therefore, real-time and accurate monitoring of overburden deformation is of great significance for the safe mining of underground coal seams.

Cedou Matthieu, Gloaguen Erwan, Blouin Martin et al.

Airborne magnetic data are commonly used to produce preliminary geological maps. Machine learning has the potential to partly fulfill this task rapidly and objectively, as geological mapping is comparable to a semantic segmentation problem. Because this method requires a high-quality dataset, we developed a data augmentation workflow that uses a 3D geological and magnetic susceptibility model as input. The workflow uses soft-constrained Multi-Point Statistics, to create many synthetic 3D geological models, and Sequential Gaussian Simulation algorithms, to populate the models with the appropriate magnetic distribution. Then, forward modeling is used to compute the airborne magnetic responses of the synthetic models, which are associated with their counterpart surficial lithologies. A Gated Shape Convolutional Neural Network algorithm was trained on a generated synthetic dataset to perform geological mapping of airborne magnetic data and detect lithological contacts. The algorithm also provides attention maps highlighting the structures at different scales, and clustering was applied to its high-level features to do a semi-supervised segmentation of the area. The validation conducted on a portion of the synthetic dataset and data from adjacent areas shows that the methodology is suitable to segment the surficial geology using airborne magnetic data. Especially, the clustering shows a good segmentation of the magnetic anomalies into a pertinent geological map. Moreover, the first attention map isolates the structures at low scales and shows a pertinent representation of the original data. Thus, our method can be used to produce preliminary geological maps of good quality and new representations of any area where a geological and petrophysical 3D model exists, or in areas sharing the same geological context, using airborne magnetic data only.

Alcides Nóbrega Sial, Jiubin Chen, Christoph Korte et al.

High-resolution organic carbon isotope (δ13C), Hg concentration and Hg isotopes curves are presented for the Permian-Triassic boundary (PTB) sections at Guryul Ravine (India) and Meishan D (China). The total organic carbon (TOC)-normalized Hg concentrations reveal more intense environmental changes at the Latest Permian Mass Extinction (LPME) and the earliest Triassic Mass Extinction (ETME) horizons coinciding with major δ13C shifts. To highlight palaeoredox conditions we used redox-sensitive elements and Rare Earth Element distribution. At Meishan, three Hg/TOC spikes (I, II, and III) are observed. Spike I remains after normalization by total aluminum (Al), but disappears when normalized by total sulfur (TS). Spike III, at the base of Bed 26, corresponds with excursions in the Hg/TS and Hg/Al curves, indicating a change in paleoredox conditions from anoxic/euxinic in the framboidal pyrite-bearing sediments (Bed 26) to oxygenated sediments (Bed 27). At Guryul Ravine, four Hg/TOC spikes were observed: a clear spike I in Bed 46, spike II at the base of the framboidal pyrite-rich Bed 49, spike III at the PTB, and spike IV at the LPME horizon. Some of these Hg/TOC spikes disappear when TS or Al normalization is applied. The spike I remains in the Hg/TS and Hg/Al curves (oxic conditions), spike II only in the Hg/TS curve (anoxic/euxinic), and spikes III and IV only in Hg/Al curves (oxic). In both sections, Hg deposition was organic-matter bound, the role of sulfides being minor and locally restricted to framboidal pyrite-bearing horizons. Positive mass-independent fractionation (MIF) for Hg odd isotopes (odd-MIF) was observed in pre-LPME samples, negative values in the LPME–PTB interval, and positive values above the ETME horizon. Most Hg-isotope patterns are probably controlled by the bathymetry of atmospheric Hg-bearing deposits. The source of Hg can be attributed to the Siberian Traps Large Igneous Province (STLIP). In the LPME-PTB interval, a complex of STLIP sills (Stage 2) intruded coal-bearing sediments. The negative δ202Hg, the mercury odd-MIF Δ201Hg patterns, and the Δ199Hg–Hg plot in both sections are compatible with volcanic mercury deposition. Our study shows the strength of Hg/TOC ratios as paleoenvironmental proxy and as a tool for stratigraphic correlation.

Maria A. Gómez, Gabriela G. Puebla, Mercedes B. Prámparo et al.

In a study of fossil seeds recovered from the La Cantera Formation, Early Cretaceous, San Luis Basin, we establish a new species, Carpolithus volantus, and describe other specimens attributed to Carpolithus spp. and Ephedra canterata. The botanical affinity of winged seeds assigned to Carpolithus volantus is discussed in relation to the fossil flora recovered from this formation. Based on the abundance of Gnetales in the San Luis Basin (pollen grains, reproductive and vegetative structures assigned to Ephedra), we propose that Carpolithus volantus is affiliated with Gnetales (Weltwitschia). We suggest that Carpolithus spp. seeds may be angiospermous, because this group, represented by leaves and flowers, dominates the fossil macroflora of the La Cantera Formation. Micro- and macrofloral analyses of the La Cantera Formation and an assessment of available dispersal vectors suggests that wind (anemochory) and water (hydrochory) may have been the most important dispersal strategies for these seeds. The abundance and small size of seeds recovered from the La Cantera Formation, together with their morphological characters, such as the presence of wings in Carpolithus volantus, also favour abiotic mechanisms of dispersal such as anemochory or hydrochory.