Advances in Earth Science ›› 2019, Vol. 34 ›› Issue (7): 757-764. doi: 10.11867/j.issn.1001-8166.2019.07.0757

Previous Articles     Next Articles

Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits

Fuqiang Yang 1( ),Kegui Chen 1( ),Changbing Huang 2,Yuanyuan Chen 3,Jin Li 1,Xiaolin Ma 1   

  1. 1. School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    2. Sinopec Group Zhongyuan Oilfield, Henan Puyang 457000, China
    3. Geophysical Exploration Company, Chuanqing Drilling Engineering Company Limited, Chengdu 610213, China
  • Received:2019-01-15 Revised:2019-05-10 Online:2019-07-10 Published:2019-07-29
  • Contact: Kegui Chen;
  • About author:Yang Fuqiang (1994-), male, Chongqing City, Master student. Research areas include logging interpretation. E-mail:
  • Supported by:
    ect supported by the National Natural Science Foundation of China “The study on geophysical evaluation method of oil and potash in Sichuan Basin"(41372103)

Fuqiang Yang,Kegui Chen,Changbing Huang,Yuanyuan Chen,Jin Li,Xiaolin Ma. Application of PSO-LIBSVM in Recognition of Potassium Salt Deposits[J]. Advances in Earth Science, 2019, 34(7): 757-764.

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.

No related articles found!
Full text