• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Geosciences >Optimizing an Inner-Continental Shelf Geologic Framework Investigation through Data Repurposing and Machine Learning
【24h】

Optimizing an Inner-Continental Shelf Geologic Framework Investigation through Data Repurposing and Machine Learning

机译：通过数据重用和机器学习优化内部大陆架地质框架调查

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The U.S. Geological Survey (USGS) and the National Oceanic Atmospheric Administration (NOAA) have collected approximately 5400 km 2 of geophysical and hydrographic data on the Atlantic continental shelf between Delaware and Virginia over the past decade and a half. Although originally acquired for different objectives, the comprehensive coverage and variety of data (bathymetry, backscatter, imagery and physical samples) presents an opportunity to merge collections and create high-resolution, broad-scale geologic maps of the seafloor. This compilation of data repurposes hydrographic data, expands the area of geologic investigation, highlights the versatility of mapping data, and creates new geologic products that would not have been independently possible. The data are classified using a variety of machine learning algorithms, including unsupervised and supervised methods. Four unique classes were targeted for classification, and source data include bathymetry, backscatter, slope, curvature, and shaded-relief. A random forest classifier used on all five source data layers was found to be the most accurate method for these data. Geomorphologic and sediment texture maps are derived from the classified acoustic data using over 200 ground truth samples. The geologic data products can be used to identify sediment sources, inform resource management, link seafloor environments to sediment texture, improve our understanding of the seafloor structure and sediment pathways, and demonstrate how ocean mapping resources can be useful beyond their original intent to maximize the footprint and scientific impact of a study.
机译：在过去的十五年中，美国地质调查局（USGS）和国家海洋大气管理局（NOAA）收集了特拉华州和弗吉尼亚州之间大西洋大陆架上大约5400 km 2的地球物理和水文数据。尽管最初是出于不同的目的而获得的，但是全面的覆盖范围和各种数据（测深，反向散射，图像和物理样本）为合并集合并创建高分辨率的海底大规模地质图提供了机会。这种数据汇编可重新利用水文数据，扩大了地质调查的范围，突出了制图数据的多功能性，并创建了不可能独立实现的新地质产品。使用多种机器学习算法对数据进行分类，包括无监督和受监督的方法。有四个独特的类别用于分类，并且源数据包括测深，反向散射，坡度，曲率和阴影起伏。发现在所有五个源数据层上使用的随机森林分类器是这些数据的最准确方法。地貌和沉积物纹理图是使用200多个地面真实样本从分类的声学数据中得出的。地质数据产品可用于识别沉积物来源，通知资源管理，将海底环境与沉积物质地联系起来，增进我们对海底结构和沉积物路径的理解，并展示海洋制图资源如何超出其最初意图最大程度地利用海洋资源。研究的足迹和科学影响。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号