Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.
The characteristics of interannual fluctuations of the surface air temperature over North America are investigated by using the surface air temperature data of 130 stations during 1941 through 1980. It is found that the surface air temperature bears about ten-year time scale oscillation over the southeastern and northwestern North America and along the west coast of the United States, and it has the characteristics of quasibiennial oscillation over the eastern North America. The ten-year scale oscillation of the surface air temperature is related to that of the sea surface temperature (SST) of North Pacific through the PNA pattern atmospheric circulation anomaly over North Pacific through North America. It is shown that the North Pacific SST has a closer association with the surface air temperature over North America than the central and eastern equatorial Pacific SST. The characteristics of the seasonal variations of the relationship between the North Pacific SST and the surface air temperature over No 相似文献
This study explores the characteristics of high temperature anomalies over eastern China and associated influencing factors using observations and model outputs. Results show that more long-duration (over 8 days) high temperature events occur over the middle and lower reaches of the Yangtze River Valley (YRV) than over the surrounding regions, and control most of the interannual variation of summer mean temperature in situ. The synergistic effect of summer precipitation over the South China Sea (SCS) region (18°-27°N, 115°-124°E) and the northwestern India and Arabian Sea (IAS) region (18°-27°N, 60°-80°E) contributes more significantly to the variation of summer YRV temperature, relative to the respective SCS or IAS precipitation anomaly. More precipitation (enhanced condensational heating) over the SCS region strengthens the western Pacific subtropical high (WPSH) and simultaneously weakens the westerly trough over the east coast of Asia, and accordingly results in associated high temperature anomalies over the YRV region through stimulating an East Asia-Pacific (EAP) pattern. More precipitation over the IAS region further adjusts the variations of the WPSH and westerly trough, and eventually reinforces high temperature anomalies over the YRV region. Furthermore, the condensational heating related to more IAS precipitation can adjust upper-tropospheric easterly anomalies over the YRV region by exciting a circumglobal teleconnection, inducing cold horizontal temperature advection and related anomalous descent, which is also conducive to the YRV high temperature anomalies. The reproduction of the above association in the model results indicates that the above results can be explained both statistically and dynamically. 相似文献