The Chagan Depression in the Yingen-Ejinaqi Basin, located at the intersection of the Paleo-Asian Ocean and the Tethys Ocean domains is an important region to gain insights on terrestrial heat flow, lithospheric thermal structure and deep geodynamic processes. Here, we compute terrestrial heat flow values in the Chagan Depression using a large set of system steady-state temperature data from four representative wells and rock thermal conductivity. We also estimate the “thermal” lithospheric thickness, mantle heat flow, ratio of mantle heat flow to surface heat flow and Moho temperature to evaluate the regional tectonic framework and deep dynamics. The results show that the heat flow in the Chagan Depression ranges from 66.5 to 69.8 mW/m2, with an average value of 68.3 ± 1.2 mW/m2. The Chagan Depression is characterized by a thin “thermal” lithosphere, high mantle heat flow, and high Moho temperature, corresponding to the lithospheric thermal structure of “cold mantle and hot crust” type. We correlate the formation of the Yingen-Ejinaqi Basin to the Early Cretaceous and Cenozoic subduction of the western Pacific Plate and the Cenozoic multiple extrusions. Our results provide new insights into the thermal structure and dynamics of the lithospheric evolution in central China. 相似文献
Phytolith study is a new branch of micropaleontology with an increasingly important role in geology, archaeology, and plant taxonomy. Phytoliths have several advantages considering their characteristics of small particle size, high production, wide distribution, anti-decomposition, in situ deposition, distinctive morphologies, and element sequestrating capacity. Phytolith assemblages in modern soil have been found to be closely related to modern vegetation types and climate conditions, which forms the basis for the quantitative study of paleoecology, paleoclimate, and bio-geochemical cycles. At present, phytolith studies generally focus on the following four aspects: (1) Morphology: about 260 unduplicated types of phytoliths have been identified in modern soil, of which 110 types are from grasses, 50 types from ferns, woody plants and other angiosperms, whereas the origin plants of the remaining 100 types are still under investigation. (2) Soil phytolith assemblages and vegetation: phytolith assemblages from the topsoil have been used to distinguish surface vegetation types including different forests and grasslands over a typical region. This model has been applied to restore past vegetation conditions and monitor the dynamic evolution of specific vegetation types at different temporal and spatial scales. (3) Soil phytolith assemblages and climate: quantitative and semi-quantitative relationships between phytolith assemblages and a series of climate parameters, such as annual mean temperature, annual mean precipitation and altitude, have been established through mathematical analysis. In this manner, quantitative reconstruction of paleoclimatic parameters has been achieved through the phytolith-climate transfer function model. (4) Soil phytolith and its sequestered elements: in this topic, the content of soil PhytOC (Phytolith-occluded Organic Carbon) and the importance of PhytOC in the bio-geochemical cycle have been the focus. The study of modern soil phytoliths has provided new approaches and many successful cases for solving specific problems in various fields, such as Earth science and archaeology. This study analyzed existing issues in addition to the abovementioned significant progresses, and provides directions for future research on modern soil phytoliths. 相似文献
Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.
Natural Resources Research - Fractal and multifractal models, including the concentration-area (C–A) fractal model, spectrum-area (S–A) multifractal model, and local singularity... 相似文献