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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.

Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.