Lithology prediction is a fundamental problem because the outcome of lithology prediction is the critical underlying data for some basic geological work, e.g., establishing stratigraphic framework or analyzing distribution of sedimentary facies. As the geological formation generally consists of many different lithologies, the lithology prediction is always viewed as a tough work by geologists. Probabilistic neural network (PNN) shows high efficiency when solving pattern recognition problem since learning data do not need to do any pre-training of learning data and calculation results are universally reliable, and then, this model could be considered as an effective solution. However, there are two factors that seriously limit the PNN’s performance: One is existence of the interference variables of learning samples, and the other is selection of the window length of probability density distribution. In view of adverse impact of those two factors, two techniques, mean impact value (MIV) and particle swarm optimization (PSO), are introduced to improve the PNN’s calculation capability. Thus, a new prediction method referred as MIV–PSO–PNN is proposed in this paper. The proposed method is validated by three well-designed experiments, and the corresponding experiment data are recorded by two cored wells of the LULA oilfield. For the three experiments, prediction accuracies of the results provided by the proposed method are 81.67%, 73.34% and 88.34%, respectively, all of which are higher than those provided by other comparative approaches including backpropagation (BP), PNN, and MIV-PNN. The experiment results strongly demonstrate that the proposed method is capable to predict complex lithology.

岩性预测是一个基本问题，因为岩性预测的结果是某些基础地质工作（例如，建立地层框架或分析沉积相分布）的关键基础数据。由于地质构造通常由许多不同的岩性组成，因此地质学家总是将岩性预测视为艰巨的工作。概率神经网络（PNN）在解决模式识别问题时表现出很高的效率，因为学习数据不需要对学习数据进行任何预训练，并且计算结果普遍可靠，因此该模型可以被认为是一种有效的解决方案。但是，有两个因素严重限制了PNN的性能：其一是学习样本的干扰变量的存在，另一个是选择概率密度分布的窗口长度。针对这两个因素的不利影响，引入了两种方法：平均影响值（MIV）和粒子群优化（PSO），以提高PNN的计算能力。因此，本文提出了一种称为MIV–PSO–PNN的新预测方法。通过三个精心设计的实验验证了该方法的有效性，并通过LULA油田的两个岩心井记录了相应的实验数据。对于这三个实验，该方法提供的结果的预测准确度分别为81.67％，73.34％和88.34％，均高于其他比较方法（包括反向传播（BP），PNN和MIV- PNN。