在建立层序地层格架的基础上，结合盆地的演化特征及岩性变化，做出了本区不同演化阶段的沉积模式图。
On the basis of sequence frame, combined with growing characteristics and lithologic feature of Beir depression, stratigraphic sedimentary pattern of different evolution stage are obtained.
本文根据测井资料所包含的丰富的地层沉积学信息，运用神经网络模式识别方法，进行沉积微相的识别。
Based on a number of stratigraphic sedimentary information included in log data, this paper carries out a recognition of sedimentary microfacies with a method of neutral network pattern recognition.
本文采用地层倾角方位频率图和蓝模式矢量方向研究古水流方向和沉积砂体延伸方向和沉积砂体的相带变化特征。
The dip frequency plot and vectograph of blue mode were used to study the paleocurrent, the extension o.
本文采用地层倾角方位频率图和蓝模式矢量方向研究古水流方向和沉积砂体延伸方向和沉积砂体的相带变化特征。
The dip frequency plot and vectograph of blue mode were used to study the paleocurrent, the extension o.
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