GSTDTAP  > 气候变化
DOI10.1029/2020GL088726
Global marine isochore estimates using machine learning
Taylor R. Lee; Benjamin J. Phrampus; Jeffrey Obelcz; Warren T. Wood; Adam Skarke
2020-08-21
发表期刊Geophysical Research Letters
出版年2020
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.

领域气候变化
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文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/291123
专题气候变化
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GB/T 7714
Taylor R. Lee,Benjamin J. Phrampus,Jeffrey Obelcz,et al. Global marine isochore estimates using machine learning[J]. Geophysical Research Letters,2020.
APA Taylor R. Lee,Benjamin J. Phrampus,Jeffrey Obelcz,Warren T. Wood,&Adam Skarke.(2020).Global marine isochore estimates using machine learning.Geophysical Research Letters.
MLA Taylor R. Lee,et al."Global marine isochore estimates using machine learning".Geophysical Research Letters (2020).
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