Attempts of Some Machine Learning Algorithms in Geology
Geochemical anomaly identification and gold deposits prospect prediction based on coding theory
At present, the use of geological big data and machine learning methods to achieve mineralization related geochemical anomaly identification and gold deposits prospect prediction has achieved some results, but many potential machine learning methods have not yet been explored.
We use machine learning methods such as automatic encoder, variational autoencoder, dictionary learning and sparse coding model based on coding ideas to extract anomalies from geochemical data in Jiaodong, Shandong Province. These methods learn the partial characteristics of the data, encoding and reconstruct the original data, and then identify the multivariate geochemical anomalies related to the gold deposits. The conclusion shows that the method based on the coding theory can be used to identify geochemical anomalies. Most known gold deposits are located in the areas with high reconstruction error or high feature numbers, indicating that these anomalies are closely related to gold deposits.
By comparing and discussing these methods of coding theory, it is considered that the performance of geochemical anomaly detection using variational autoencoder model is relatively the best. Using this method to carry out regional gold deposits prospect prediction has more reliable results. At the end of the study, we combined with the geological background of the study area and the results of anomaly detection, then put forward a prediction of the gold deposits prospects.
ROC curve
一些机器学习算法在地质学中的尝试
基于编码理论的地球化学异常识别与金矿远景预测
目前，利用地质大数据和机器学习方法实现成矿相关地球化学异常识别和金矿远景预测已取得一定成果，但许多潜在的机器学习方法尚未探索。
本文基于编码思想，采用自动编码器、变分自动编码器、字典学习和稀疏编码模型等机器学习方法对山东胶东地区地球化学数据进行异常提取。这些方法学习数据的部分特征，对原始数据进行编码和重建，然后识别与金矿床相关的多变量地球化学异常。结果表明，基于编码理论的方法可用于地球化学异常的识别。大多数已知的金矿床都位于重建误差高或特征数高的区域，表明这些异常与金矿床密切相关。
通过对这些编码理论方法的比较和讨论，认为利用变分自动编码模型进行地球化学异常检测的性能相对最好。利用该方法进行区域金矿床远景预测具有更可靠的结果。在研究的最后，我们结合研究区的地质背景和异常探测结果，提出了金矿床的前景预测。
一些机器学习算法在地质学中的尝试
基于编码理论的地球化学异常识别与金矿远景预测
目前，利用地质大数据和机器学习方法实现成矿相关地球化学异常识别和金矿远景预测已取得一定成果，但许多潜在的机器学习方法尚未探索。
本文基于编码思想，采用自动编码器、变分自动编码器、字典学习和稀疏编码模型等机器学习方法对山东胶东地区地球化学数据进行异常提取。这些方法学习数据的部分特征，对原始数据进行编码和重建，然后识别与金矿床相关的多变量地球化学异常。结果表明，基于编码理论的方法可用于地球化学异常的识别。大多数已知的金矿床都位于重建误差高或特征数高的区域，表明这些异常与金矿床密切相关。
通过对这些编码理论方法的比较和讨论，认为利用变分自动编码模型进行地球化学异常检测的性能相对最好。利用该方法进行区域金矿床远景预测具有更可靠的结果。在研究的最后，我们结合研究区的地质背景和异常探测结果，提出了可以应用于金矿床的前景预测机器学习模型。