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首页> 外文期刊>Geophysical Prospecting >Application of spectral decomposition and neural networks to characterise deep turbidite systems in the outer fold and thrust belt of the Niger Delta
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Application of spectral decomposition and neural networks to characterise deep turbidite systems in the outer fold and thrust belt of the Niger Delta

机译:光谱分解和神经网络在表征尼日尔三角洲外褶皱和逆冲带深层浊积岩系统中的应用

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摘要

We have applied a wavelet-based spectral decomposition scheme and a multi-layered feed-forward neural network to interpret turbidite depositional systems from three-dimensional reflection seismic data and well logs for a prospective hydrocarbon zone in the outer fold and thrust belt of the Niger Delta. The goal was to overcome difficulties in interpreting depositional systems from deep sections of the Field, occasioned by loss of seismic resolution with depth and the sparse distribution of wells.
机译:我们已经应用了基于小波的频谱分解方案和多层前馈神经网络,从三维反射地震数据和尼日尔外褶皱和逆冲带潜在油气区的测井资料解释浊积岩沉积系统。三角洲。目的是克服在解释油田深层沉积系统时遇到的困难,这种困难是由于地震分辨率随深度而损失以及井的稀疏分布而引起的。

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