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.

Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma’anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection.

The three-dimension (3D) inversion of magnetic data is an effective method of recovering underground magnetic susceptibility distributions using magnetic anomaly data. The conventional regularization inversion method has good data fitting; however, its inversion model has the problem of a poor model-fitting ability due to a low depth resolution. The 3D inversion method based on deep learning can effectively improve the model-fitting accuracy, but it is difficult to guarantee the data-fitting accuracy of the inversion results. The loss function of traditional deep learning 3D inversion methods usually adopts the metric of the absolute mean squared error (MSE). In order to improve the accuracy of the data fitting, we added a forward-fitting constraint term (FFit) on the basis of the MSE. Meanwhile, in order to further improve the accuracy of the model fitting, we added the Dice coefficient to the loss function. Finally, we proposed a multi-constraint deep learning 3D inversion method based on UNet++. Compared with the traditional single-constraint deep learning method, the multi-constraint deep learning method has better data-fitting and model-fitting effects. Then, we designed corresponding test models and evaluation metrics to test the effectiveness and feasibility of the method, and applied it to the actual aeromagnetic data of a test area in Suqian City, Jiangsu Province.

Edge detection is a critical technology in processing potential field data, enabling the rapid identification of geological body edges using magnetic anomaly data. Traditional methods for detecting edges in magnetic data are known for their simplicity and efficiency, however, they suffer from low resolution, poor robustness, and a lack of depth information. In recent years, the application of deep learning (DL) to edge detection in field data has enhanced both the resolution and robustness of these methods. Nonetheless, these approaches still fail to determine the buried depths of geological body edges. To address this issue, the present study has developed an edge detection method called multi constraint DL based on UNet++, which not only identifies the edge positions but also ascertains the buried depths of geological bodies. The study proposes the multi-constraint loss function for edge detection, and employs the Dice loss function and the mean squared error (MSE) loss function to jointly supervise the network’s training. Subsequently, the label design was revised to include depth information on the geological body, enabling the DL method to accurately determine the buried depths of geological bodies. Analysis of the test model’s detection results reveals that the edge detection method based on UNet++ can precisely identify both the edge positions and the exact buried depths of geological bodies, which makes up for the shortcoming that the traditional edge detection method does not contain the depth information. Moreover, this method resolves the issue of discontinuity found in traditional DL edge detection. Lastly, the method was applied to actual aeromagnetic data from the Dandong area in Liaoning Province, successfully identifying the edge positions of the main mining area and providing targeted regions for further exploration.

Muon detection technology is an innovative type of geophysical exploration method that uses the penetrating ability of cosmic ray muons to detect and image the internal density structure of targets, offering the advantage of non-destructive detection. However, the applied research on muon detection technology is still in its initial stage, with research gaps existing in aspects such as the selection of optimal field observation parameters for muon detection instruments and muon inversion theory. To improve observation efficiency, this paper studies how to select optimal observation parameters in muon detection technology and proposes a method for selecting optimal observation parameters based on FreeCAD modeling and the energy attenuation formula of muon rays after penetrating matter. Additionally, a density-length product calculation method based on the muon survival rate formula is established, using the muon survival rate formula to reflect muon flux attenuation and thereby perform density inversion of objects. For the first time, muon imaging technology is applied to the detection of the No. 2 Mausoleum of the XiXia Imperial Tombs, verifying that muon imaging technology can effectively identify density anomalies inside the mausoleum tower, providing key data support for the structural analysis and protection of the XiXia Imperial Tombs. This paper systematically studies muon observation and inversion theories, laying a foundation for relevant researchers conducting muon detection work in the future.