Geoscience frontiers最新文献

In the gneiss terrane on the south side of the Eoarchean Isua supracrustal belt, ultramafic rocks with relict abyssal peridotite mineralogy (Bennett et al., 2002, Friend et al., 2002, Nutman et al., 2007, Rollinson, 2007, van de Löcht et al., 2020), layered gabbros with cumulate ultramafic rocks, basalts and associated siliceous sedimentary rocks were tectonically-imbricated, prior to and during intrusion of ca. 3800 Ma tonalites. Together with ≥ 3800 Ma basalts in the Outer Arc Group of the nearby Isua supracrustal belt, the composition of all these mafic rocks (e.g., Th–Hf–Nb systematics, high Th/Yb, Ba/Nb, Ba/Yb ratios and negative Nb and Ti anomalies) shows affinity with modern suprasubduction rocks whose genesis involved fluid fluxing of the upper mantle. However, the majority of these samples have Ba/Nb and Ba/Yb values less than in modern island arc magmas, but similar to many backarc basin magmas (e.g., Pearce and Stern, 2006). It is unknown whether these ca. 3800 Ma mafic rocks are, (i) arc rocks where the Ba/Nb and Ba/Yb signatures reflect lower surficial Ba in Eoarchean oceanic settings, or (ii) in direct comparison with Phanerozoic suites, these signatures reflect a back-arc setting with interplay between fluid fluxing and decompressional melting. The tectonic intercalation of upper mantle with lower and upper crustal rocks, combined with the fluid-fluxing influences seen in chemistry of all the mafic rocks is best accommodated in a compressional Eoarchean convergent plate boundary setting within a mobile-lid regime. Thus stagnant lid scenarios of crust formation, if operative, must have co-existed or alternated with mobile-lid regimes by  3800 Ma.

Regional landslide susceptibility mapping (LSM) is essential for risk mitigation. While deep learning algorithms are increasingly used in LSM, their extensive parameters and scarce labels (limited landslide records) pose training challenges. In contrast, classical statistical algorithms, with typically fewer parameters, are less likely to overfit, easier to train, and offer greater interpretability. Additionally, integrating physics-based and data-driven approaches can potentially improve LSM. This paper makes several contributions to enhance the practicality, interpretability, and cross-regional generalization ability of regional LSM models: (1) Two new hybrid models, composed of data-driven and physics-based modules, are proposed and compared. Hybrid Model I combines the infinite slope stability analysis (ISSA) with logistic regression, a classical statistical algorithm. Hybrid Model II integrates ISSA with a convolutional neural network, a representative of deep learning techniques. The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples. The data-driven module captures the relation between explanatory factors and landslide inventory. (2) A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance. (3) Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood. (4) The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored. Typical landslide-prone regions in the Three Gorges Reservoir, China, are used as the study area. The results show that, in the testing region, by using local-area samples to account for pixel spatial neighborhoods, Hybrid Model I achieves roughly a 4.2% increase in the AUC. Furthermore, models with 30 m resolution land-cover data surpass those using 1000 m resolution data, showing a 5.5% improvement in AUC. The optimal set of explanatory factors includes elevation, land-cover type, and safety factor. These findings reveal the key elements to enhance regional LSM, offering valuable insights for LSM practices.

Re-examination of three specimens from the Kanchenjunga Himal of Nepal via in situ Lu-Hf garnet geochronology yields evidence of multiple garnet growth events. Spot analyses from grain cores in two specimens define Paleozoic regressions whereas analyses from grain rims in the same specimens define low-precision regressions consistent with the timing of Himalayan orogenesis. These dates contrast with previously published low dispersion, ca. 290 Ma isotope dissolution (ID) Lu-Hf garnet dates for the same rocks. Modelling of Lu and spot age distribution in representative grains from the specimens examined yields calculated dates that approximate the Permian-age regressions through the original ID data. These findings demonstrate that it is possible to generate low dispersion ID Lu-Hf data from multi-generational garnet with significantly different-age growth events when approximately equal proportions of the different age reservoirs are included in multi-component aliquots.

Flash floods (FFs) are amongst the most devastating hazards in arid regions in response to climate change and can cause the loss of agricultural land, human lives and infrastructure. One of the major challenges is the high-intensity rainfall events affecting low-lying areas that are vulnerable to FF. Several works in this field have been conducted using ensemble machine learning models and geohydrological models. However, the current advancement of eXtreme deep learning, which is named eXtreme deep factorisation machine (xDeepFM), for FF susceptibility mapping (FSM) is lacking in the literature. The current study introduces a new model and employs a previously unapplied approach to enhance FSM for capturing the severity of floods. The proposed approach has three main objectives: (i) During- and after-flood effects are assessed through flood detection techniques using Sentinel-1 data. (ii) Flood inventory is updated using remote sensing-based methods. The derived flood effects are implemented in the next step. (iii) An FSM map is generated using an xDeepFM model. Therefore, this study aims to apply xDeepFM to estimate susceptible areas using 13 factors in the emirates of Fujairah, UAE. The performance metrics show a recall of 0.9488), an F1-score of 0.9107), precision of (0.8756) and an overall accuracy of 90.41%. The accuracy of the applied xDeepFM model is compared with that of traditional machine learning models, specifically the deep neural network (78%), support vector machine (85.4%) and random forest (88.75%). Random forest achieves high accuracy, which is due to its strong performance that depends on factors contribution, dataset size and quality, and available computational resources. Comparatively, the xDeepFM model works efficiently for complicated prediction problems having high non-collinearity and huge datasets. The obtained map denotes that the narrow basins, lowland coastal areas and riverbank areas up to 5 km (Fujairah) are highly prone to FF, whilst the alluvial plains in Al Dhaid and hilly regions in Fujairah show low probability. The coastal city areas are bounded by high-rise steep hills and the Gulf of Oman, which can elevate the water levels during heavy rainfall. Four major synchronised influencing factors, namely, rainfall, elevation, drainage density, distance from drainage and geomorphology, account for nearly 50% of the total factors contributing to a very high flood susceptibility. This study offers a platform for planners and decision makers to take timely actions on potential areas in mitigating the effects of FF.

To ensure long-run sustainability, it is imperative to achieve the goal of zero-carbon emissions without compromising economic growth. Identifying whether BRICS economies, which are an attractive set of countries due to their rapid economic growth and high emissions, can shift towards sustainability with the support of policy measures, is a question which needs to be addressed. This article investigates the impact of emission trading schemes, energy innovation, technology transfer, population growth, and inflation on the economic performance of BRICS economies (2001–2020). The outcomes of the CS-ARDL and PMG estimators reveal that carbon taxes, carbon finance, energy innovation, technology transfer, population growth, and inflation have positive effects on economic performance. In light of the evidence, policy insights are recommended to achieve a win–win situation for economic and environmental performance.

Geophysical well logs are widely used in geological fields, however, there are considerable incompatibilities existing in solving geological issues using well log data. This review critically fills the gaps between geology and geophysical well logs, as assessed from peer reviewed papers and from the authors’ personal experiences, in the particular goal of solving geological issues using geophysical well logs. The origin and history of geophysical logging are summarized. Next follows a review of the state of knowledge for geophysical well logs in terms of type of specifications, vertical resolution, depth of investigations and demonstrated applications. Then the current status and advances in applications of geophysical well logs in fields of structural geology, sedimentary geology and petroleum geology are discussed. Well logs are used in structural and sedimentary geology in terms of structure detection, in situ stress evaluation, sedimentary characterization, sequence stratigraphy division and fracture prediction. Well logs can also be applied in petroleum geology fields of optimizing sweet spots for hydraulic fracturing in unconventional oil and gas resource. Geophysical well logs are extending their application in other fields of geosciences, and geological issues will be efficiently solved via well logs with the improvements of advanced well log suits. Further work is required in order to improve accuracy and diminish uncertainties by introducing artificial intelligence. This review provides a systematic and clear descriptions of the applications of geophysical well log data along with examples of how the data is displayed and processed for solving geologic problems.