Gravity inversion has become the main method to obtain the 3D density distribution in the exploration of petroleum and mineral resources as well as the study of geological structure. In recent years, with rapid development of deep learning algorithms, gravity intelligent inversion based on convolutional neural networks have also achieved certain good results. However, in the previous study on 3D gravity intelligent inversion, some issues in "data-driven" inversion still need to be faced, that is, insufficient number and lack of the diversity of data sets. This paper selects a suitable U-net network with four-layer down-sampling structure, designs typical block and inclined plate and their combined models to construct the data sets. In order to reduce the overfitting of the network, the data augmentation is introduced to improve the training process for datasets. The data augmentation method greatly alleviates the limited number and types of samples, allowing the network to learn more situations and improve the generalization ability. Theoretical model data tests show that the data augmentation method of Mixup and flip can effectively improve the generalization ability of U-net network, and the noise-adding augmentation has good inversion result for noisy data. The tests on the measured gravity data also verify the effectiveness of the proposed method, which indicates that data augmentation does play a significant role in the improvement of gravity intelligent inversion.

On-chip spectrometers are key components in many spectral sensing applications owing to their unique advantages in size and in situ detection. In this work, we propose and demonstrate a class of thermally tunable spectrometers by utilizing topological miniaturized bound states in the continuum (mini-BIC) cavities in a photonic crystal (PhC) slab combined with a metal micro-ring heater. We achieve a resolution of 0.19 nm in a spectral range of ∼6 nm, while the device’s footprint is only 42×42μm². The mini-BIC spectrometer works in nearly vertical incidence and is compatible with array operation. Our work sheds light on the new possibilities of high-performance on-chip spectrometers for applications ranging from bio-sensing to medicine.

Utilizing magnetic anomaly data for effective edge detection of source bodies can provide crucial evidence for the delineation of geological units and the division of fault structures. However, the existing edge detection methods of source bodies from magnetic anomalies are influenced by factors such as the source bodies’ burial depth, magnetization direction, and mutual interference of magnetic anomalies, leading to errors in subsequent interpretation tasks. The advanced convolutional neural network possesses robust capabilities for feature representation and deep learning, prompting this paper to introduce an edge detection method for source bodies based on convolutional neural networks. The issue is initially framed as a semantic segmentation problem, and four network architectures aimed at edge detection of a source body from magnetic anomaly are designed and modified based on the U-Net and ResNet. Subsequently, a multitude of high-quality sample data sets are constructed using models with varying locations, scales, quantities, and physical properties to train the network. This paper then details model experiments that escalate from simple to complex, taking into account the combined effects of burial depth and inclined magnetization on edge detection. Compared to conventional edge detection methods, the method proposed in this paper is shown to accurately identify edges of source bodies at various depths with little impact from inclined magnetization and can automatically extract edge information without manual intervention. The method’s efficacy is corroborated through real data tests.

The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. We designed a hybrid network architecture (ResNet + ViT) that combines the local feature extraction strengths of Convolutional Neural Networks with the global context modeling capabilities of Vision Transformer. By combining the complementary information from DEM and gravity anomaly data, it achieves comprehensive detection of lunar craters—from those visible on the surface to buried subsurface structures. To mitigate the inherent sample imbalance in both gravity anomaly and DEM training data, we employ a U-Net architecture augmented with residual blocks and train it using a Focal Loss function with dynamic focusing parameters. Experimental results show that: (1) The proposed method attains high segmentation accuracy, achieving a mean Intersection over Union of 81.3% on the DEM test set and 82.6% on the gravity anomaly test set, respectively. (2) Our method outperforms U-Net and its mainstream variants, achieving a precision of 89.48% and superior detection completeness. (3) Application to representative geological units, including the Wugang Basin, Archimedes Crater, and Mare Moscoviense, validates the robustness and practical utility of our method. This study, thus, provides a novel technical framework for global-scale mapping of lunar impact craters and yields new insights into the evolutionary history of the lunar surface.

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