气候变化领域集成服务门户(CCPortal): Global Marine Isochore Estimates Using Machine Learning

CCPortal

DOI	10.1029/2020GL088726
	Global Marine Isochore Estimates Using Machine Learning
	Lee T.R.; Phrampus B.J.; Obelcz J.; Wood W.T.; Skarke A.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:18
英文摘要	The thickness normal to deposition (isopachs) and vertical thickness (isochores) of geological units is important for assessing various geologic processes. We present the first marine global sediment isochore estimates for five geological periods dating from middle Miocene (15.97 Ma) to present. We use sparsely distributed sediment depth vs. age observations from the Deep Sea Drilling Project and global maps of biological, oceanographic, geographic, and geological variables as training features in a k-nearest neighbor regressor to estimate isochores. Results are compared to isochore estimates generated by applying a constant depositional rate from recent estimates of global total sediment thicknesses. Both models of isochore thickness exhibit consistent error. Results from a machine learning approach show major advantages, including results that are quantitative, easily updatable, and accompanied with uncertainty estimation. Final predictions can provide first-order constraints on sediment deposition with geologic time, which is of timely importance for assessing past climate variability. ©2020. The Authors.
英文关键词	Deepwater drilling; Deposition; Geology; Nearest neighbor search; Sediments; Turing machines; Climate variability; Deep sea drilling; Geologic process; K-nearest neighbors; Machine learning approaches; Sediment deposition; Training features; Uncertainty estimation; Machine learning; age determination; dating method; Deep Sea Drilling Project; deposition; estimation method; geological survey; global change; machine learning; marine environment; marine sediment; Miocene
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169721
作者单位	U.S. Naval Research Laboratory, Stennis Space Center, MS, United States; Department of Geosciences, Mississippi State University, Mississippi State, MS, United States
推荐引用方式 GB/T 7714	Lee T.R.,Phrampus B.J.,Obelcz J.,et al. Global Marine Isochore Estimates Using Machine Learning[J],2020,47(18).
APA	Lee T.R.,Phrampus B.J.,Obelcz J.,Wood W.T.,&Skarke A..(2020).Global Marine Isochore Estimates Using Machine Learning.Geophysical Research Letters,47(18).
MLA	Lee T.R.,et al."Global Marine Isochore Estimates Using Machine Learning".Geophysical Research Letters 47.18(2020).