This paper evaluates the carbon dioxide sequestration potential in the saline aquifers of the South Qiongdongnan–Yinggehai Basin. By using a hierarchical evaluation method, the assessment is divided into five stages: the basin level, the zone level, the target level, the site level, and the injection level. The study primarily focuses on evaluating the sequestration potential of and identifying favorable zones of saline aquifers at the basin and zone levels. The optimized volumetric method is adopted, based on the integration of multi-source data such as regional geological maps, seismic data, core porosity, and permeability. The results show that the estimated potential of the Yinggehai Basin is 60.6 billion tons at the basin level and 54.6 billion tons at the zone level. Additionally, the estimated potential of the South Qiongdongnan Basin is 261.5 billion tons at the basin level and 234.8 billion tons at the zone level. The suitability evaluation indicates that the Yinggehai Basin is moderately suitable overall, the northern depression of the South Qiongdongnan Basin is suitable, the central uplift is moderately suitable, and the central depression is not suitable. This study provides a scientific foundation for carbon dioxide sequestration in marine basins and introduces novel ideas and methods for future similar research. This is highly significant for subsequent engineering applications and decision-making processes.

To investigate the characteristics and generation potential of gas generated from over-mature shale, hydrous and anhydrous pyrolysis experiments were carried out on the Longmaxi Formation in the Anwen 1 well of the Sichuan Basin of China at temperatures of 400–598 °C and pressures of 50 Mpa, with (hydrous) and without (anhydrous) the addition of liquid water. The results show that in the presence of water, the total yield of carbon-containing gases (i.e., the sum of methane, ethane, and carbon dioxide) was increased by up to 1.8 times when compared to the total yield from the anhydrous pyrolysis experiments. The increased yield of carbon dioxide and methane accounted for 89% and 10.5% of the total increased yield of carbon-containing gases. This indicated that the participation of water could have promoted the release of carbon from over-mature shale, like we used in this study. The methane generated in the hydrous pyrolysis experiments was heavier, with a δ¹³C value of −21.27‰ (544 °C) compared to that generated in the anhydrous pyrolysis experiments, which showed a lighter δ¹³C of −33.70‰ (544 °C). It is noteworthy that the δ¹³C values of the methane from hydrous pyrolysis at >500 °C were even heavier than that of the kerogen from the over-mature shale, although the δ¹³C values of the methane show an overall increasing trend with increasing temperature both in hydrous and anhydrous pyrolysis. The carbon dioxide from hydrous pyrolysis was less enriched in ¹³C relative to that from anhydrous pyrolysis. Specifically, the δ ¹³C values of the carbon dioxide increased with the increasing temperature in anhydrous pyrolysis, whereas they remained nearly constant with increasing temperature in hydrous pyrolysis. The overall lighter δ¹³C values of the carbon dioxide generated in the hydrous pyrolysis experiments likely indicate that water tends to prompt the release of lighter carbon and/or suppress the release of heavier carbon from over-mature shale in the form of carbon dioxide, especially at higher temperatures, for example, of >510 °C.

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.