The geothermal gradient in the eastern area of Liaoning Province is very low, but hot springs resources are variable. The reason is not clear till now but leads to the fact that a few strong influence factors can cause imbalances in the results of many prediction algorithms. It can be found as a black-box algorithm, deep learning will obtain a more unbalanced result with the fault influence factors. To tackle this issue, the role of preprocessing during the process of profound learning was enhanced and four comparative experiments were carried out. The results show that compared with the unprocessed experiment, the accuracy rate of the experiment with fully processed data increased by 11.9 p.p., and the area under the curve increased by 0.086 (0.796–0.882). This inspires us that even though the deep learning method can achieve high accuracy in the prediction of geological resources, we still need to pay attention to the analysis and pretreatment of data with expertise according to local conditions.

中文翻译：

---

考虑数据预处理中的地质意义，并通过深度学习提高温泉的预测精度

辽宁省东部地区的地热梯度非常低，但是温泉资源却是可变的。到目前为止，其原因尚不清楚，但会导致以下事实：一些强大的影响因素可能会导致许多预测算法的结果失衡。可以发现它是一种黑盒算法，深度学习将在故障影响因素的影响下获得更加不平衡的结果。为了解决这个问题，增强了预处理在深度学习过程中的作用，并进行了四个比较实验。结果表明，与未处理的实验相比，具有完全处理的数据的实验的准确率提高了11.9 pp，曲线下的面积增加了0.086（0.796–0.882）。