An unmanned aerial vehicle (UAV) aeromagnetic measurement system can precisely obtain large-scale data with high efficiency and low cost in regions with complex exploration conditions, which has aroused interest in the field of aerial survey. In this article, based on the unmanned helicopter platform, an aeronautical magnetic field measurement system is integrated to complete the integration of a rotor UAV, magnetometers, and data recorders. Equipped with a three-axis fluxgate magnetometer for real-time flight compensation, the compensation deviation is ±0.052 nT, which meets the aeromagnetic survey specifications. The UAV can fly autonomously with a maximum load of 45 kg at a speed of 60 km/h for 2 h, and the wind resistance level is level 7. The system has completed a field test of 111.9 km in the Ma'anshan crisis mine region of China and collected 145,495 data points. Then, the adaptive down-sampling method is used to compress the data (to 1.7% of the original data), and it is applied to the three-dimensional magnetic inversion, which successfully obtains the spatial distribution characteristics of the underground orebody, improving the inversion efficiency while ensuring the target area resolution. Six favorable magnetite prospecting sites are delineated in the survey area through inversion results, which demonstrates the potential of the developed UAV aeromagnetic system in the exploration over the areas with complex terrain.

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.

Three-dimensional gravity inversion is an effective method for restoring underground density distribution from gravity anomaly data. Conventional regularization inversion has good data fitting, but its inversion model has insufficient model fitting capabilities due to its low-depth resolution. Although data-driven deep learning-based gravity inversion results significantly improve depth resolution and physical property distribution, it is difficult to ensure the data fitting of the inversion results. Accordingly, this study proposes a three-dimensional gravity data inversion based on enhanced dual U-Net framework (EdU-Net) to solve the above problems, making the inversion results have good model and data fitting performance. The proposed EdU-Net consists of two parts: first, training a large generalization pre-trained network Net I, and then quickly generating an enhanced Net II for the target data through fine-tuning. Additionally, this study adds forward-fitting constraints in the framework’s loss function to reduce the problem of large data-fitting errors in traditional data-driven deep learning inversion. The trained Net II inversion result has better model and data fitting accuracy than Net I. Moreover, by comparing the inversion results of synthetic models, this study demonstrates that the EdU-Net method performs better than traditional deep learning. Finally, this method is applied to the measured data of the Gonghe Basin in Qinghai Province, China, and provides a reasonable explanation for the distribution of hot dry rocks.

The joint inversion of gravity and magnetic data can reduce the nonuniqueness problem of potential field data inversion. We propose a gravity and magnetic joint inversion method based on deep learning (DL) combined with measurement data constraint. The framework obtains the gravity and magnetic dataset required for network training by randomly generating the underground structural consistency model and then inputs the dataset into the network for training. Moreover, we add constraints to the measurement data in the training of the network, that is, fitting the data anomalies obtained by the inversion model through forward calculation with the real anomalies, which makes the network more consistent with geophysical theory. In the test phase, the trained network can obtain the inversion results rapidly, and the inversion results of the testing dataset show that this method can obtain better results when applied to the joint inversion of gravity and magnetic fields than the conventional regularized inversion and cross-gradient joint inversion methods. In addition, our method can also distinguish the anomaly conditions in the case of structural inconsistency. Furthermore, we apply this method to actual gravity and magnetic data of Gonghe Basin, Qinghai Province, China, and predict the distribution of dry hot rock related to geothermal resources.

Magnetic anomaly detection often encounters high false alarm rates or detection failures due to the rapid decay of magnetic anomaly data with distance and its susceptibility to environmental interference. A fully parameter-adaptive magnetic anomaly detection method based on a multi-stable stochastic resonance system is proposed to address this issue. This multi-stable stochastic resonance model exhibits higher noise utilization than mono-stable and bi-stable models, demonstrating superior detection performance for weak signals under strong background noise. However, as the complexity of the well function increases, the severe coupling of system parameters complicates the adjustment of the potential well function. This study proposes an adaptive parameter selection method, which transforms the signal into the frequency domain using continuous wavelet transform (CWT). We propose a combined fitness function comprising peak signal-to-noise ratio (PSNR) and kurtosis index, calculated between the two-dimensional frequency domain image of the magnetic anomaly signal and the reference frequency domain image. Particle swarm optimization (PSO) is then employed to optimize the system parameters, achieving optimal alignment between the multi-stable stochastic resonance system and the input signal, improving the detection probability. The experimental section tests the proposed method on synthetic and field data and compares it to traditional detection methods. The results indicated that the method maintains a high detection probability when the signal-to-noise ratio (SNR) is below -10 dB with colored noise condition and outperforms traditional methods in detection performance.

Gravity inversion can be used to obtain the spatial structure and physical properties of subsurface anomalies through gravity observation data. With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area.