A Comprehensive Geophysical Prospection Method Based on Gaussian Mixture Clustering and its Application in Karst Exploration

Abstract

Comprehensive geophysical prospection is an effective technique for karst exploration, but its prediction results usually suffer from significant artificial influence and fuzzy boundaries of karst caves. Based on the machine learning technology, a Gaussian mixture model is used to classify the exploration data of high-density electrical method and data of surface wave method respectively. Then, a Category-boundary algorithm is proposed to further subdivide the classification results, which improves the accuracy of Gaussian mixture model classification. Finally, the classification fusion rules are formulated based on expert experience and geological exploration data. Under the organic combination of survey data-driven and engineering geological knowledge guidance, a new set of high-precision classification and fusion methods is proposed for comprehensive geophysical exploration. By applying this new method to the karst exploration project in southern Zhejiang, a karst cave prediction is made with clearer boundaries. Compared with the actual drilling information, cave prediction and drilling information are highly consistent, which verifies the effectiveness of the method proposed.

Keywords

• comprehensive geophysical prospection
• gaussian mixture clustering
• classification fusion
• karst exploration
• machine learning