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In the traditional data-driven data mining process, there are huge gaps between the efficient algorithms and intelligent tools as well as the invalidity of knowledge, which is obtained by traditional data-driven data mining. Meanwhile, each data in the earth science field contains a solid physical meaning. If there is no corresponding domain knowledge involved in the mining process, the information explored by data-driven data mining will be lack of practicability and not able to effectively solve problems in the earth science area. Therefore, the task-driven data mining is proposed. Additionally, task-driven data mining concepts and principles are elaborated with the help of data mining concepts and techniques. It is divided into seven elements such as data warehousing, data preprocessing, feature subset selecting, modeling, model evaluating, model updating and model releasing. Those constitute a cyclic and iterative process until the appearance of a predictive model, which is capable of effectively achieving the objectives. The task-driven data mining is applied to recognizing the complex lithologies and the low resistivity oil layer, and the whole mining process is elaborated. Their accuracy rates are more than 90%. Finally, the paper puts forward the understandings, development prospects and key challenges of task-driven data mining facing.