| 1 | Application of isolation forest to extract multivariate anomalies from geochemical exploration data显示文摘Constructing a statistical model that best fits the background is a key step in geochemical anomaly identification. But the model is hard to be constructed in situations where the sample population has unknown and/or complex distribution. Isolation forest is an outlier detection approach that explicitly isolates anomaly samples rather than models the population distribution. It can extract multivariate anomalies from huge-sized high-dimensional data with unknown population distribution. For this reason,we tentatively applied the method to identify multivariate anomalies from the stream sediment survey data of the Lalingzaohuo district,an area with a complex geological setting,in Qinghai Province in China. The performance of the isolation forest algorithm in anomaly identification was compared with that of a continuous restricted Boltzmann machine. The results show that the isolation forest model performs superiorly to the continuous restricted Boltzmann machine in multivariate anomaly identification in terms of receiver operating characteristic curve,area under the curve,and data-processing efficiency. The anomalies identified by the isolation forest model occupy 19% of the study area and contain 82% of the known mineral deposits,whereas the anomalies identified by the continuous restricted Boltzmann machine occupy 35% of the study area and contain 88% of the known mineral deposits. It takes 4. 07 and 279. 36 seconds respectively handling the dataset using the two models. Therefore,isolation forest is a useful anomaly detection method that can quickly extract multivariate anomalies from geochemical exploration data. | WU Wei CHEN Yongliang | 2018 | Global Geology2018，21，1： | 9 |
| 2 | Identification model of geochemical anomaly based on isolation forest algorithm显示文摘The methods for geochemical anomaly detection are usually based on statistical models, and it needs to assume that the sample population satisfies a specific distribution, which may reduce the performance of geochemical anomaly detection. In this paper, the isolation forest model is used to detect geochemical anomalies and it does not require geochemical data to satisfy a particular distribution. By constructing a tree to traverse the average path length of all data, anomaly scores are used to characterize the anomaly and background fields, and the optimal threshold is selected to identify geochemical anomalies. Taking 1∶200 000 geochemical exploration data of Fusong area in Jilin Province, NE China as an example, Fe2O3 and Pb were selected as the indicator elements to identify geochemical anomalies, and the results were compared with traditional statistical methods. The results show that the isolation forest model can effectively identify univariate geochemical anomalies, and the identified anomalies results have significant spatial correlation with known mine locations. Moreover, it can identify both high value anomalies and weak anomalies. | SHANG Yinmin LU Laijun KANG Qiankun | 2019 | Global Geology2019，22，3： | 1 |
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