The exploration and development of marine geothermal energy is a field with significant potential, but it is also one that presents considerable challenges and costs. The assessment of marine geothermal reservoir potential is currently based on subjective analysis, and this study proposes an innovative clustering-based method to classify marine geothermal reservoirs systematically. The Yingqiong Basin was analysed to develop a machine learning framework to predict the potential of marine geothermal reservoirs (CPPOGR). The study integrated eight key geothermal features into a unified dataset, employing dimensionality reduction techniques (principal component analysis and sparse autoencoder) and SMOTE to balance the sample size. Machine learning classifiers, including XGBoost, BP Neural Networks, Support Vector Machines, K-Nearest Neighbours, and Random Forests, were utilised for prediction. The experimental results demonstrate that XGBoost is the most suitable classifier, achieving an excellent performance of 0.96 precision, 0.9556 recall, 0.9528 F1 score, and 0.9623 accuracy. These results demonstrate the effectiveness of the proposed CPPOGR in accurately classifying marine geothermal reservoirs based on intrinsic features. This study underscores the potential of integrating cluster analysis with machine learning for efficient reservoir characterisation, thereby offering a novel approach for marine geothermal resource assessment.

The increasing tensions between urban development and ecological environments in China, as evidenced by the country’s rapidly globalising and urbanising society, pose significant challenges to the implementation of sustainable development strategies. In order to evaluate the coupled coordination degree (CCD) between regional development (RD) and ecological carrying capacity (ECC) within the Chengdu-Chongqing Twin Cities Economic Circle, this study proposes a comprehensive CCD evaluation framework. This framework includes indicator analysis, quantitative measurement of coupling coordination, identification of coupling types, and evaluation of coordination impacts. The results indicate that regional development is on a growth trajectory while ecological carrying capacity lags behind. The coupling coordination has shifted from a state of ’basically uncoordinated’ to ’basically coordinated’, exhibiting a decreasing gradient in spatial distribution, with core cities demonstrating leadership. The study thus provides a set of optimised strategies tailored to cities at different stages of development, offering a theoretical foundation and decision-making framework for promoting quality development within the Chengdu-Chongqing Twin Cities Economic Circle.