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A machine-learning derived model of seafloor sediment accumulation
Marine Geology ( IF 2.9 ) Pub Date : 2021-07-28 , DOI: 10.1016/j.margeo.2021.106577
Giancarlo A. Restreppo 1, 2 , Warren T. Wood 2 , Jordan H. Graw 1, 2 , Benjamin J. Phrampus 2
Affiliation  

Previous studies regarding the depositional pattern and quantity of accumulated seafloor sediment tend to be regional, limited in scope and involving costly and time-consuming geologic field campaigns and laboratory work. Presented herein is a global map of predicted modern (postindustrial, 20th and 21st century) oceanic mass accumulation rates of 5-arc-minute pitch and in log10-space, trained on observed marine mass accumulation rates from 43 peer reviewed sources (n = 1744) and predicted using a k-nearest neighbor geospatial algorithm. The resultant model predicts ~3.3 × 104 Mt. yr−1 of sediment accumulating onto the sea floor (R2 = 0.88). Most sediment accumulates proximal to major river outlets and deltas. Continental regions with the highest sediment accumulation are Asia and Oceania. This model is the first of its kind to predict the rate and quantity of sediment accumulating on to the ocean floor, globally, using decades of regional real-world observations. The generated global map of modern, benthic mass accumulation rates also serves to highlight areas of interest for future study in related fields, such as sediment dynamics and seafloor stability.



中文翻译:

海底沉积物堆积的机器学习派生模型

以前关于沉积模式和累积海底沉积物数量的研究往往是区域性的,范围有限,涉及昂贵且耗时的地质野外活动和实验室工作。此处展示的是预测的现代(后工业时代、20 世纪和 21 世纪)海洋质量积累率的全球地图,其间距为 5 角分间距和 log 10空间,根据来自 43 个同行评审来源(n  = 1744) 并使用 k 最近邻地理空间算法进行预测。由此产生的模型预测~3.3 × 10 4 Mt。yr -1沉积到海底的沉积物 (R 2 = 0.88)。大多数沉积物聚集在主要河流出口和三角洲附近。沉积物积累量最高的大陆地区是亚洲和大洋洲。该模型是同类模型中第一个使用数十年的区域真实世界观测来预测全球海底沉积物堆积速率和数量的模型。生成的现代底栖物质积累率全球地图也有助于突出相关领域未来研究的兴趣领域,例如沉积动力学和海底稳定性。

更新日期:2021-08-05
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