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Dataset . 2022
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Data sources: Datacite
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Dataset . 2022
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2022
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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SMR algorithm, dataset and training loss

Research datakeyboard_double_arrow_right Dataset 23 Jun 2022Publisher:Zenodo
Authors: Tao Liu;

doi: 10.5281/zenodo.6685416 , 10.5281/zenodo.6685417 , 10.5281/zenodo.6689840

SMR algorithm, dataset and training loss

Abstract

这是我们提出的SMR方法的数据,算法和训练损失的概况。数据集来自中国两个致密储集层:松辽盆地三豑凹陷的扶余储集层(SZS数据集)和鄂尔多斯盆地林兴区块的上古生界(实况成套数据集)。

{"references": ["China National Standard GB/T 7714-2015 (note)"]}

Keywords

Tight Oil Reservoir; Rock Thin Slices; Deep Learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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