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Applied ichnology in sedimentary geology: Python scripts as a method to automatize ichnofabric analysis in marine core images

机译:在沉积地质学中应用鱼类学:Python脚本作为一种自动实现海洋核心图像中鱼类织物分析的方法

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摘要

Image analysis has been succesfully applied in core research, especially in studies from modern deposits, to enhance the visibility of ichnological features and characterize ichnoassemblages and ichnofabrics. Its application to ichnological research provides useful information for marine core studies, hence sedimentary geology, but also for hydrocarbon exploration. Here we develop a new methodology, using Python programming language, which significantly improve the ichnological analysis. The method automatizes the process of obtaining continuous ichnological information, in this case about the percentage of bioturbation as a key aspect of the ichnofabric approach. The method affords the possibility of automatically generating continuous percentage and other index records using pixel counts in previously treated images. The resulting data sets are easy to correlate with the information usually obtained from cores (e.g., geochemical and mineralogical data). Such an integration of different proxies for to the field of sedimentary geology especially in the use of ichnological analysis, making it easier for the researcher, less time consuming, and more likely to be undertaken. The coding and sharing of open software tools allow for great flexibility, giving researchers in ichnology or related fields the option to implement new features, develop more complex tools to improve the package, and share findings with the scientific community.
机译:图像分析已成功地应用于核心研究中,尤其是在现代矿床的研究中,以增强鱼眼学特征的可见性并表征鱼眼组合物和鱼眼织物。它在水文研究中的应用为海洋核心研究,沉积地质学以及油气勘探提供了有用的信息。在这里,我们使用Python编程语言开发了一种新的方法,可以显着改善病原学分析。该方法使获取连续鱼类学信息的过程自动化,在这种情况下,有关生物扰动的百分比是鱼类织物方法的关键方面。该方法提供了使用先前处理的图像中的像素计数自动生成连续百分比和其他索引记录的可能性。所得数据集易于与通常从岩心获得的信息(例如地球化学和矿物数据)相关联。对于沉积地质领域的不同代理的这种整合,尤其是在水墨特征分析中的应用,使研究人员更容易,耗时更少并且更有可能进行研究。开放软件工具的编码和共享提供了极大的灵活性,为鱼类学或相关领域的研究人员提供了实现新功能,开发更复杂的工具以改进软件包以及与科学界共享发现的选择。

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