钾盐的紧缺严重制约了中国农业的发展，加大钾盐的勘探开发力度有助于提高我国钾盐的自给自足能力。四川盆地钾盐资源丰富，是我国目前重要的钾盐勘探开发研究区域之一。杂卤石作为四川盆地最重要的固态钾盐矿物，常夹杂在硬石膏、岩盐和白云岩等岩层中。针对常规测井解释方法难以精确识别杂卤石的问题，因此，提出一种新的基于粒子群算法（PSO）优化的支持向量机(SVM)杂卤石识别方法开展四川盆地杂卤石的分类识别研究。以PSO和SVM理论为基础，结合测井解释方法，选择对杂卤石测井响应灵敏的有效数据作为输入样本，随机产生训练集和测试集，并采用PSO优选出径向基核函数参数，建立杂卤石分类预测模型。与录井结果对比，基于PSO的SVM模型识别准确率达到了97.5758%，在识别精度和速度上明显优于交叉验证方法优化的SVM模型。结果表明，该模型在四川盆地钾盐勘探中具有广阔的应用前景。

The shortage of potassium salt seriously restricts the development of China's agriculture. Increasing the exploration and development of potash will help improve the self-sufficiency of potassium in China. With rich potassium salt resources, Sichuan basin is one of the most important research areas for potash exploration and development in China. Polyhalite is an important solid potassium salt mineral in Sichuan basin, often intercalated in rock minerals such as anhydrite, rock salt and dolomite. Aiming at the problem that conventional log interpretation methods are difficult to accurately identify polyhalites, this paper proposed a new Support Vector Machine (SVM) recognition method based on Particle Swarm Optimization (PSO) to classify polyhalites in Sichuan basin. Based on particle swarm optimization and support vector machine theory, combined with logging interpretation theory, the effective data sensitive to polyhalite logging response were selected as input samples to generate training sets and test sets randomly. The Radial Basis Function (RBF) parameters were optimized by particle swarm optimization, and the classification and prediction model of polyhalite was established. Compared with mud logging results, the recognition accuracy of SVM model based on particle swarm optimization reached 97.5758%, which is obviously better than that of SVM model optimized by cross validation method in recognition accuracy and speed. The results show that the model has broad application prospects in potash exploration in Sichuan basin.