DOI: 10.1002/2015GL065279
论文题名: A global prediction of seafloor sediment porosity using machine learning
作者: Martin K.M. ; Wood W.T. ; Becker J.J.
刊名: Geophysical Research Letters
ISSN: 0094-8327
EISSN: 1944-8058
出版年: 2015
卷: 42, 期: 24 起始页码: 10640
结束页码: 10646
语种: 英语
英文关键词: interpolation techniques
; machine learning
; marine sediment properties
; random forests
; seafloor porosity
Scopus关键词: Artificial intelligence
; Decision trees
; Interpolation
; Porosity
; Regression analysis
; Submarine geology
; Acoustic propagation
; Global predictions
; Interpolation techniques
; Machine learning techniques
; Marine sediments
; Random forests
; Sea floor
; Seafloor phenomena
; Learning systems
; accuracy assessment
; estimation method
; global perspective
; interpolation
; machine learning
; marine sediment
; pixel
; porosity
; prediction
; regression analysis
; seafloor
; sediment property
; void ratio
; Arctic
英文摘要: Porosity (void ratio) is a critical parameter in models of acoustic propagation, bearing strength, and many other seafloor phenomena. However, like many seafloor phenomena, direct measurements are expensive and sparse. We show here how porosity everywhere at the seafloor can be estimated using a machine learning technique (specifically, Random Forests). Such techniques use sparsely acquired direct samples and dense grids of other parameters to produce a statistically optimal estimate where direct measurements are lacking. Our porosity estimate is both qualitatively more consistent with geologic principles than the results produced by interpolation and quantitatively more accurate than results produced by interpolation or regression methods. We present here a seafloor porosity estimate on a 5 arc min, pixel registered grid, produced using widely available, densely sampled grids of other seafloor properties. These techniques represent the only practical means of estimating seafloor properties in inaccessible regions of the seafloor (e.g., the Arctic). © 2015. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954562767&doi=10.1002%2f2015GL065279&partnerID=40&md5=acd7c5ad1a2869308185cea10545db0c
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/7943
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: NRC Postdoctoral Program, Naval Research Laboratory, John C. Stennis Space CenterMS, United States
Recommended Citation:
Martin K.M.,Wood W.T.,Becker J.J.. A global prediction of seafloor sediment porosity using machine learning[J]. Geophysical Research Letters,2015-01-01,42(24).