气候变化领域集成服务门户(CCPortal): Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm

CCPortal

DOI	10.1029/2020JB020135
	Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm
	Graw J.H.; Wood W.T.; Phrampus B.J.
发表日期	2021
ISSN	21699313
卷号	126 期号:1
英文摘要	Marine sediment density is of vital importance due to its influence on many geoacoustic and nongeoacoustic parameters, including but not limited to: acoustic impedance, sediment grain size, sediment attenuation, gravity, and isostatic and dynamic responses of oceanic crust. Additionally, it plays a fundamental role on geomechanical behavior which is important for pore pressure generation, seafloor slope stability, and seafloor infrastructure. Subsurface drilling from the Deep Sea Drilling Project, Ocean Drilling Program, International Ocean Discovery Program, a myriad of scientific research cruises, and a number of petroleum companies have amassed large quantities of invaluable seafloor geologic parameters. These point sampling location data yield accurate vertical constraints at a given location, but extrapolating that information away from the location is very difficult. To address this, we have taken a machine learning approach (the random forest regressor [RFR] in this instance) to predict global seafloor density and its associated uncertainty, both at a 5 × 5-arc minute resolution. The RFR algorithm accepts a sparsely sampled observational data set with densely gridded relatable predictors and predicts statistically optimal estimates where no physical measurements have been made. The final prediction has median, mean, and root-mean-square errors of 0.058, 0.076, and 0.1009 g/cm3, respectively. Results show density predictions that coincide with expected lateral density variation (albeit at a very coarse spatial scale) with respect to ocean depth and uncertainties that are low for most of the seafloor. Finally, a parametric isolation indicates locations where additional samples are needed to improve the overall prediction. © 2020. American Geophysical Union. All Rights Reserved.
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187406
作者单位	U.S. Navy Research Laboratory, John C. Stennis Space Center, NRC Postdoctoral Research Program, Hancock County, MS, United States; U.S. Navy Research Laboratory, John C. Stennis Space Center, Hancock County, MS, United States
推荐引用方式 GB/T 7714	Graw J.H.,Wood W.T.,Phrampus B.J.. Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm[J],2021,126(1).
APA	Graw J.H.,Wood W.T.,&Phrampus B.J..(2021).Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm.Journal of Geophysical Research: Solid Earth,126(1).
MLA	Graw J.H.,et al."Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm".Journal of Geophysical Research: Solid Earth 126.1(2021).