气候变化领域集成服务门户(CCPortal): Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach

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DOI	10.1016/j.tecto.2020.228541
	Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach
	Kumar P.C.; Sain K.
发表日期	2020
ISSN	00401951
卷号	789
英文摘要	In sedimentary basins, emplacement of sheet intrusions such as sill complexes significantly contributes to the transport and storage of hot magma at shallow level. Such emplacement at shallow depth leads in doming of the overburden, which acts as plausible structural traps useful for hydrocarbon exploration. The petroliferous Canterbury Basin, SE offshore New Zealand, is a classic example of such phenomena, where saucer-shaped magmatic sills are emplaced within the Cretaceous to Eocene succession. This has resulted into forced folds and hydrothermal vents above the sill terminations within the Eocene sequences. The present study attempts to capture this scenario through a neural network by designing meta-attributes, called as the Sill Cube (SC) and Fluid Cube (FC). The meta-attributes are computed by unifying different seismic attributes that are trained over interpreter's knowledge on the geologic targets following a supervised scheme of neural learning. The approach prominently arrests the structural geometry of sill complexes and fluxed-out magmatic fluids within the Cretaceous to Eocene strata from 3D seismic reflection data of the Waka prospect, offshore Canterbury Basin. Based on the meta-attribute interpretation, the individual sills within the Waka prospect cover areas of ~1.5 km2 to 17 km2, where the principal sills namely the Sill W1 and Sill W2 spread over an area of ~12 km2 and 17 km2, respectively. Moreover, the fluxed out magmatic fluids vertically rise to a height of ~800 m through hydrothermal vents from the tip of the principal sills, and uplift the overburden. Such approach is automated and incorporates interpreter's acquaintances to effectively capture subsurface magmatic activities for better interpretation of 3D seismic data. © 2020 Elsevier B.V.
关键词	Canterbury Basin Meta-attributes Neural network Sills
英文关键词	Complex networks; Digital storage; Hot springs; Machine learning; Offshore oil well production; Sedimentology; Seismic prospecting; Seismic waves; Seismology; Automated machines; Hydrocarbon exploration; Hydrothermal vent; Magmatic activity; Sedimentary basin; Sedimentary strata; Seismic attributes; Structural geometry; Offshore petroleum prospecting; data interpretation; emplacement; hydrothermal vent; machine learning; magma; overburden; sedimentary basin; sill; transport process; uplift; Canterbury Basin; Canterbury [South Island]; New Zealand; South Island
语种	英语
来源期刊	Tectonophysics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/207798
作者单位	Wadia Institute of Himalayan Geology, Dehradun, 248001, India; CSIR-National Geophysical Research Institute, Hyderabad, 500007, India
推荐引用方式 GB/T 7714	Kumar P.C.,Sain K.. Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach[J],2020,789.
APA	Kumar P.C.,&Sain K..(2020).Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach.Tectonophysics,789.
MLA	Kumar P.C.,et al."Interpretation of magma transport through saucer sills in shallow sedimentary strata using an automated machine learning approach".Tectonophysics 789(2020).