Complex Lithofacies Identification Using Improved Probabilistic Neural Networks
Complex Lithofacies Identification Using Improved Probabilistic Neural Networks
复制标题
DOI：
10.30632/pjv59n2-2018a9
复制
发表时间：
2018-04-01
期刊：
影响因子：
0.9
通讯作者：
Rui, Zhenhua
中科院分区：
文献类型：
--
作者：
Gu, Yufeng;Bao, Zhidong;Rui, Zhenhua
概率神经网络（PNN）在不需要对源数据进行预训练的情况下，具有识别复杂模式的功能。然而，对于某些数据簇，学习样本中变量的独立性和共线性特性会严重扭曲其概率密度分布的窗口长度，从而导致计算出的概率值不正确或完全错误，从而导致最终的识别结果。针对这些缺点，提出了一种结合平均冲击值（MIV）和相关分析两种技术的改进PNN，通过去除源数据中的干扰和共线性变量，完善原PNN的计算机制。用于验证该方法的数据来自Iara油田的两口井。改进后的网络在4个实验中的识别准确率分别为74.05%、71.7%、83.02%和88.24%，均为最高准确率。验证结果表明，该网络具有识别复杂碳酸盐岩岩相的能力，结果可靠，可作为分析沉积过程、建立层序格架等其他地质工作的参考数据。
The probabilistic neural network (PNN) is functional in recognizing complex patterns without doing any pretraining of source data. However, for some data clusters, independence and colinearity characteristics of the variables in learning samples can seriously distort the window lengths of their probability density distributions, then leading to the incorrect or totally wrong calculated probability values and nal recognition results. In view of such drawbacks, an improved PNN that incorporates two techniques of mean impact value (MIV) and correlation analysis is proposed in order to perfect the original PNN's calculation mechanism by removing those interference and colinear variables from the source data. The data used to validate the method are from two wells in the Iara oil eld. Recognition accuracies of the improved network in four experiments are, 74.05%, 71.7%, 83.02% and 88.24%, respectively, each of which is the highest accuracy. The validation results demonstrate that the new network has the capability of recognizing complex carbonate lithofacies and the results are reliable enough to serve as the reference data for other geological efforts, such as analyzing sedimentary process and building a sequence framework.