A geological anomaly is the basis of mineral deposit prediction. Through the study of the knowledge and characteristics of geological anomalies, the category of extreme value theory (EVT) to which a geological anomaly belongs can be determined. Associating the principle of the EVT and ensuring the methods of the shape parameter and scale parameter for the generalized Pareto distribution (GPD), the methods to select the threshold of the GPD can be studied. This paper designs a new algorithm called the EVT model of geological anomaly. These study data on Cu and Au originate from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed EVT model of the geological anomaly is applied to identify anomalies in the Jiguanzui Cu-Au mining area. The results show that the model can effectively identify the geological anomaly region of Cu and Au. The anomaly region of Cu and Au is consistent with the range of ore bodies of actual engineering exploration. Therefore, the EVT model of the geological anomaly can effectively identify anomalies, and it has a high indicating function with respect to ore prospecting.

Geochemical anomalies are the basis of mineral deposit prediction. Through the study of the characteristics of geochemical anomalies, we found that their distribution was consistent with a generalized Pareto distribution (GPD). In the present study, we designed a model for geochemical anomaly extraction via a GPD. In the designed GPD model, we used the kurtosis method to estimate the threshold value of the GPD. Furthermore, a principal component analysis (PCA) was used to extract comprehensive information of different geochemical elements in which minerals are enriched. On this basis, a new algorithm named the GPDA model was designed for deep mineral prediction by using the GPD and PCA, and the methods of the GPDA for selecting parameters were studied. The study data for Ba, Pb, As, Cu, Au, Mo, Co, and Zn originated from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed GPDA model was applied to deep mineral prediction in the study area. We estimated the parameters of the GPDA model, and the thresholds of Ba, Pb, As, Cu, Au, Mo, Co, and Zn were 457.8612, 56.1823, 28.8454, 910.1272, 89.4283, 34.5267, 84.9445, and 121.4863, respectively. The comprehensive information threshold value was 0.4551. The comprehensive abnormal distribution area of geochemical element contents was obtained from thresholds. The results showed that the method used to identify abnormal areas was consistent with the range of ore bodies identified by actual engineering exploration, demonstrating that the GPDA model was effective. Finally, we predicted that there was a new blind ore body located at a depth of about 1100 m below ground between drill holes KZK10 and KZK11. The results have important theoretical and practical significance for deep ore prospecting.

The study on brain-computer interface technology to achieve the automatic control of unmanned vehicles can help people with disabilities to realize self-service travel, thus attracting more and more attention from scholars and manufacturers. In this paper, the visual evoked potentials of human brain are extracted by visual stimulator of FPGA, and the evoked potential vector by waveform matching recognition algorithm on Labview platform, which are used as the control signals of brain-computer interface to realize automatic control of unmanned vehicle. The article explains the basis of related technologies, based on which, the signal processing flow of unmanned vehicle control system is introduced. Finally, experiment on the automatic system control of unmanned vehicle based on visual evoked potentials is designed. The experiment shows that the average time for sending instructions is less than 3s, and the average correct recognition rate of instructions is higher than 90%. The present research has opened up the research on the brain-computer interface controlled unmanned vehicle field, and will have a positive effect for the ultimate realization of autonomous travel for patients with limited mobility.