Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix is eliminated and the principal components are using to construct the hyperplane to compensate the platform-generated magnetic fields. The algorithm was tested using a helicopter, and the obtained improvement ratio is 9.86. The compensated quality is almost the same or slightly better than the ridge regression. The validity of the proposed method was experimentally demonstrated.

Magnetic anomaly detection (MAD) is a passive method used to detect the ferromagnetic objects by revealing the anomalies in the ambient Earth magnetic field. This article proposed a novel MAD algorithm based on signal modulation for magnetic sensor array. The new method transforms the original magnetic anomaly into a modulated signal and then detects the target magnetic anomaly signal by using a time-frequency analysis method. Compared with traditional algorithms, the MAD algorithm proposed in this article has three advantages: first, the influence of geomagnetic background is suppressed by calculating the magnetic field difference of the array sensor, and the trend of magnetic field difference is removed by empirical mode decomposition. Second, it properly fuses the magnetic anomaly signal of the sensor array and uses the time-frequency analysis method, which can improve the detection ability of weak magnetic anomaly signals. Third, the new method does not depend on the motion state and characteristics of the target, and it has a good adaptability to the background magnetic field. The simulation results show that this novel detection method has a good ability in the low signal-to-noise ratio (SNR) anomalies detection. For weak magnetic anomaly with an SNR of -6.9 dB, the detection probabilities and false alarm rate are about 0.9648 and 0.001, respectively. The real-world detection experiment proved that this algorithm can detect the weak magnetic anomaly in the geomagnetic background.

An aeromagnetic survey is an important method in magnetic anomaly detection and geophysical prospecting. The magnetic field is typically measured by optically pumped magnetometers (OPM) installed on the aircraft. The measurement accuracy of the OPM is easily affected by the platform-generated magnetic fields. Therefore, aeromagnetic compensation is necessary. The traditional compensation model only considers the permanent, induced, and eddy current interference magnetic field of the aircraft platform. However, the interference field produced by the avionics system, and the relative motion between the aircraft and the magnetometer, are still not taken into account. To address this issue, we proposed a novel strategy to eliminate the additional interference of the platform with two OPMs. Among them, the OPM located farther away from the aircraft serves as a sensing magnetometer, whereas the near OPM serves as a reference magnetometer. The coherent noise suppression method is used to process the residual magnetic field interference after compensation. By establishing the interference magnetic transfer function between the two sensors, the interference field can be suppressed. The results of the experiments demonstrate the effectiveness of the novel strategy, and the standard deviation of residual interference drops from 0.065 nT to 0.045 nT.

Aeromagnetic surveys are important in geophysical prospecting and magnetic anomaly detection (MAD). Magnetic fields are typically measured by optically pumped magnetometers (OPMs) mounted on an aircraft. Platform-generated magnetic interference severely compromises the sensitivity of the OPMs. Therefore, aeromagnetic compensation is necessary. However, measurement uncertainties of three-axis magnetometers influence qualities of aeromagnetic compensation and there is no method can directly predict qualities of aeromagnetic compensation before calibration flights. To address this issue, we propose a method to calculate systematic errors of aeromagnetic compensation caused by measurement uncertainties of three-axis magnetometers and analyze performance of aeromagnetic compensation in advance of calibration flights. It can be used to estimate the residual errors of aeromagnetic compensation theoretically. Using this method, researchers can assess whether aircraft platforms are appropriate to implement aeromagnetic surveys or not before carrying out experiments in practice. Field experiments are carried out to exhibit how to use the method to calculate measurement uncertainties of aeromagnetic compensation in different conditions. The results of the experiments demonstrate the validity of the method.

As the integration of cultural industry and tourism turns to be a public trend in China, some scholars and agencies are paying attention to the methods to evaluate the degree or level of the integration. Shandong is a province with rich cultural resources, large-scale cultural industry and tourism. It has experienced the integration process of cultural industry and tourism for about 10 years. So the authors choose it as a case study area to evaluate the integration levels, classify the integration types, and try to forward practical policy measures. The authors establish a valuation system with 23 indicators, using a coupled coordinated dispatch model to quantify the integration of cultural industry and tourism in the province. The proposed policy measures aim to promote the development of cultural industrial and tourism as well as their substantial integration in the case area and other similar regions.

This paper builds a whole-process management system based on a computer big data algorithm for power system materials. The author uses the Markov algorithm to construct an inventory management model based on demand characteristics, taking the energy engineering material storage of the power distribution network as the research object. Inventory management and scheduling decisions for materials rely entirely on past observations. Finally, this paper conducts a simulation study on the optimization scheme of emergency material dispatching of power systems in areas affected by natural disasters. At the same time, the configuration of the system under various disaster conditions is verified. This paper verifies the effectiveness of this method in improving the scheduling of power resources through the simulation of the Markov method. The whole process management system of power materials proposed in this paper can quickly meet the dynamic management requirements of the power grid and materials.