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.

Magnetic anomaly detection (MAD) serves as a method for detecting ferromagnetic objects via magnetic data. However, MAD typically confronts issues such as low signal-to-noise ratio (SNR) data and lack of prior information, resulting in the failure of conventional anomaly detection methods. Additionally, distinguishing the effective signal from the noise proves challenging, leading to difficulties in subsequent target location and further interpretation. Most of the current denoising algorithms are only applicable to the Gaussian noise, and their performance is poor for geomagnetic noise (generally colored noise). To solve this issue, we propose a new adaptive denoising method for magnetic anomaly data based on continuous wavelet transform (CWT), which elevates the SNR of magnetic anomaly data by integrating the stochastic resonance (SR) and pixel connectivity thresholding. For the problem that the noise wavelet coefficients are difficult to separate from the effective signal in the existing denoising methods in the wavelet domain, we present an adaptive pure background noise estimation method based on SR, which can adaptively remove the noise wavelet coefficients in the wavelet domain. After that, the residual noise wavelet coefficients that still exist are suppressed by using the method of pixel connectivity thresholding in view of the poor continuity of the residual noise wavelet coefficients. In experiments, we compare our proposed method with traditional denoising algorithms on simulated and real data. The results show that our method has better denoising performance with a high SNR, structural similarity (SSIM), and correlation coefficient (CC).

Grotto rock structure detection is precondition of the grotto protection and restoration. As a stable and non-destructive geophysical method, electrical resistivity tomography (ERT) is extensively used to detect the geological structure of the grottoes. However, the traditional ERT measurement scheme is challenging to satisfy the requirements of high accuracy and high resolution in the cliff region. Therefore, we propose a new borehole-to-surface-to-cliff ERT (BSC-ERT) scheme for grotto detection. The sensitivity patterns of five ERT schemes using three configurations (A-MN array, AB-MN array, and AM-BN array) are tested to show the detection capability of different configurations. Two numerical experiments show that the combination of borehole-to-surface ERT (BS-ERT) and surface-to-cliff ERT (SC-ERT) schemes using AM-BN array have better imaging effects and a more reasonable detection range, which is recommended to be implemented for future work. In addition, the numerical experiment also proves that the symmetric effect generated in the AM-BN array in BS-ERT scheme can be suppressed with the combination of the SC-ERT scheme.

The Gonghe Basin is one of the most important regions for the exploration and development of hot dry rock geothermal resources in China. However, there is still some controversy about the main heat source of hot dry rock geothermal resources in the Gonghe Basin. Combined with previous research results including three-dimensional magnetotelluric imaging and linear inversion of Rayleigh wave group and phase velocity result, we obtained a high-resolution underground spatial density distribution model of the Gonghe Basin based on satellite gravity data by using 3-D gravity focusing inversion method. According to the results, there are widely distributed low density anomalies relative to surrounding rock in the middle crust of the study area. The low-density layer is speculated to be a low-velocity, high-conductivity partial melting layer in the crust of the Gonghe Basin. The inversion result confirms for the first time the existence of a partial melt layer from the gravity point of view, and this high temperature melt layer may be the main heat source of the hot dry rock geothermal resources in the Gonghe Basin. It can provide a new basis for further research on the genesis of the hot dry rock geothermal system in the Gonghe Basin.

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.