Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the $l_p$ norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.

Due to the influence of both the observation environment and the instruments themselves, Ground Penetrating Radar (GPR) data are often contaminated by random noise, which degrades data quality. Salt-and-pepper noise is a common type of such noise. Adaptive median filtering is an effective technique for removing this noise. However, it has the drawback of replacing original values that are not affected by noise with the median, which can lead to a degradation in image quality. In this paper, we propose an improved adaptive median filtering method. First, we assess whether the original value is contaminated by salt-and-pepper noise. If the value is affected, filtering is applied. The window size is adaptively increased, and the window is subdivided into smaller sections. Multiple median calculations are then performed on the segmented windows to ensure the validity of the median. When the noise density is high, the median of the non-noise points within the largest window is selected as the output, thereby minimizing the negative impact of noise on the median calculation. Both synthetic and real-world data validations demonstrate that the improved method significantly outperforms traditional adaptive median filtering, conventional median filtering, and other filtering methods, particularly in high-noise scenarios, thus confirming the superiority of the proposed algorithm.

Black Huangjiu (BH) is a traditional alcoholic beverage in China, which is very popular among people. The different methods (simultaneous inoculation, sequential inoculation), were applied to ferment BH in this study, which were investigated the changes in the composition of fungal communities and non-volatile flavor compounds (nVFCs) by high-throughput sequencing (HTS) and ultraperformance liquid chromatography–tandem mass spectrometer (UPLC MS/MS). The results showed that Rhizopus and Saccharomyces were the predominant fungal genera throughout fermentation, and 471 nVFCs were detected in BH after fermentation. Compared to that observed simultaneous inoculation, Rhizopus increased at the end of sequential fermentation, and the contents of the organic acids and their derivatives increased significantly [variable importance in the projection (VIP) > 1.0, p < 0.05, fold change (FC) > 2], while that of lipids and lipid-like molecules decreased significantly (VIP > 1.0, p < 0.05, FC < 0.5). Through the correlation analysis of 32 nVFCs with significant differences (VIP > 1.0, p < 0.05, FC >32 or < 0.03) and the community, it was found that lipids and lipid-like molecules (12) and organic acids and their derivatives (10) were significantly (p < 0.05) negatively correlated with Saccharomyces, but they were significantly (p < 0.05) positively correlated with Rhizopus. Compared with simultaneous inoculation, BH fermented by sequential inoculation, the taste was stronger, sweeter, mellow, and softer. Our findings provide information on nVFC dynamics and will aid in the selection of beneficial strains to improve BH quality.

As a popular population based heuristic evolutionary algorithm, differential evolution (DE) has been widely applied in various science and engineering problems. Similar to other global nonlinear algorithms, such as genetic algorithm, simulated annealing, particle swarm optimization, etc., the DE algorithm is mostly applied to resolve the parametric inverse problem, but has few applications in physical property inversion. According to our knowledge, this is the first time DE has been applied in obtaining the physical property distribution of gravity data due to causative sources embedded in the subsurface. In this work, the search direction of DE is guided by better vectors, enhancing the exploration efficiency of the mutation strategy. Besides, to reduce the over-stochastic of the DE algorithm, the perturbation directions in mutation operations are smoothed by using a weighted moving average smoothing technique, and the Lp-norm regularization term is implemented to sharpen the boundary of density distribution. Meanwhile, in the search process of DE, the effect of Lp-norm regularization term is controlled in an adaptive manner, which can always have an impact on the data misfit function. In the synthetic anomaly case, both noise-free and noisy data sets are considered. For the field case, gravity anomalies originating from the Shihe iron ore deposit in China were inverted and interpreted. The reconstructed density distribution is in good agreement with the one obtained by drill-hole information. Based on the tests in the present study, one can conclude that the Lp-norm inversion using DE is a useful tool for physical property distribution using gravity anomalies.

Magnetic anomaly pole transformation converts magnetic field data into an equivalent response at the true magnetic pole, eliminating shifts and distortions, simplifying the interpretation of subsurface magnetic bodies, and improving data interpretation and inversion accuracy. However, the main challenge in magnetic anomaly pole transformation lies in the non-linear nature of the signals, making traditional methods difficult to apply. The interaction between the shape, depth, and magnetic inclination of magnetic bodies, especially in high and low latitude regions, can cause distortion in the transformed signal, leading to unclear causal relationships. To address this, this paper proposes a deep learning-based approach that automatically extracts high-dimensional features and establishes nonlinear mappings to enhance the correlation between magnetic anomaly signals and geological structures. Deep learning does not require explicit physical models and, through training with large datasets, demonstrates stronger robustness and accuracy, especially in areas where traditional methods fail. The proposed method is validated using both synthetic and measured data. Synthetic data simulates magnetic bodies of various shapes, depths, and magnetic inclinations, confirming the method’s stability and accuracy in handling complex non-linear signals. The measured data evaluates its pole transformation advantages in typical ore deposit regions. The results indicate that the deep learning model significantly enhances the accuracy of pole transformation, particularly in areas with complex magnetic anomaly signals, effectively preventing signal distortion and demonstrating exceptional generalization capabilities.