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.

Mantle plumes have played a key role in tectonic events such as continental break-up and large magmatic events since at least the formation of Gondwana. However, as their signatures on Earth’s surface, many of large igneous provinces have disappeared into the mantle during Earth’s long-term evolution, meaning that plume remnants in the mantle are crucial in advancing mantle plume theory and accurately reconstructing Earth history. Here we present an electrical conductivity model for North Asia constructed from geomagnetic data. The model shows a large high-electrical-conductivity anomaly in the mantle transition zone beneath the Siberian Traps at the time of their eruption that we interpret to be a thermal anomaly with trace amounts of melt. This anomaly lies almost directly over an isolated low-seismic-wave-velocity anomaly known as the Perm anomaly. The spatial correlation of our anomaly with the Siberian Traps suggests that it represents a remnant of a superplume that was generated from the Perm anomaly. This plume was responsible for the late Permian Siberian large igneous province. The model strengthens the validity of the mantle plume hypothesis.

Electrical resistivity tomography (ERT) is a key geophysical technique that provides detailed information on subsurface structures by measuring the distribution of electrical resistivity underground. ERT suffers from limitations in electrode arrangement, interference from environmental and instrument noise, and existing data processing algorithms that fail to adequately consider geological heterogeneity and uncertainty, resulting in insufficient inversion resolution. Traditional ERT methods rely on simplified algorithms and a limited number of observation points, which smooths model details and further reduces resolution. To address the resolution issues in ERT, this article proposes a deep learning inversion method that integrates prior physical information. This method uses low-resolution inversion results as prior knowledge to provide the deep learning algorithm with a constrained initial model, thereby combining the physical basis of traditional methods with the data-driven advantages of deep learning. The method not only retains the strengths of traditional inversion but also enhances the resolution and imaging efficiency of the inversion model using deep learning technology. Synthetic data experiments demonstrate that integrating deep learning significantly improves the model’s ability to detail subsurface structures, especially in the transition zones of shallow structures and the recovery of deep anomalies. Results from measured data indicate that the proposed method not only achieves high-resolution inversion but also maintains good consistency with prior information.

Water in the mantle controls mantle convection and chemical transport processes within Earth's interior. However, whether the water in the lower mantle is primordial or recycled remains an open question. Here we report an electrical structure consisting of two stepped high‐conductivity anomalies crossing the 660‐km interface beneath northeastern Asia. The sheet‐like first‐step conductor locates in the mantle transition zone that can be best interpreted as hydrated stagnant Pacific slab, whereas the funnel‐like second‐step anomaly occurs in the uppermost lower mantle that is inferred to be a water reservoir replenished by the collapsed stagnant slab. The pivotal mechanism of this water transport process could be the dehydration reaction of dense hydrous magnesium silicates and the rehydration of stishovite and liebermannite in the transition zone. Our findings provide evidence for recycled lower‐mantle water reservoirs, and together with seismological and mineralogical studies, illustrate the deep‐mantle water plumbing system operated by cold slab subduction.

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.