Geological storage of carbon dioxide in faulted sedimentary basins requires suitability evaluation methods that can address uncertainty, indicator interaction, and limited data availability. This study develops an integrated evaluation framework that combines the Analytic Hierarchy Process, triangular fuzzy numbers, and the Choquet fuzzy integral to assess basin-scale geological carbon dioxide storage suitability. The framework enables structured weight determination, explicit representation of expert uncertainty, and non-additive aggregation of interacting indicators. The evaluation focuses on deep saline aquifers in the Jianghan Basin and is based on seventeen indicators covering geological, structural, hydrogeological, and socio-economic conditions. The assessment integrates seismic interpretation, geological mapping, logging data, and published datasets, and is conducted at the level of tectonic units to support basin-scale screening. The method is applied to the Jianghan Basin using seventeen geological, structural, hydrogeological, and socio-economic indicators. The results indicate that burial depth primarily acts as a threshold condition, whereas caprock sealing capacity, fault system development, and hydrogeological stability dominate suitability differentiation. Interaction analysis reveals pronounced substitution effects among geological indicators, indicating that strong performance in key safety-related factors can compensate for less favorable secondary constraints during early-stage screening. The Qianjiang Sag and Jiangling Sag are identified as the most suitable storage units. The proposed framework provides a transparent and robust tool for basin-scale screening in structurally complex, data-limited sedimentary basins.

Introduction: Seismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot topic in recent years, and multi-task learning has also demonstrated excellent data representation capabilities in several areas.Methods: To improve accuracy, we combine the FB picking task with the seismic data reconstruction task, and propose an enhanced FB picking training method based on multi-task learning network. Specifically, we use two decoding branches of the same size in the network, which are the ISS decoding branch for the FB picking task, and the seismic feature learning decoding branch for the reconstruction task. The introduction of the seismic feature learning decoding branch will further help the network encoder to extract seismic effective features more efficiently, which will improve the accuracy of the ISS decoding branch, and ultimately improve the accuracy of the FB picking. During the training process, we use different loss functions for different decoding branches, and guide the network fitting through joint loss. In addition, we randomly add noise as well as random elimination to the seismic data to simulate the low SNR trace sets and bad traces that may exist in seismic data acquisition, and discuss the impact of different cases on the training results.Results and discussion: The experimental results show that this method achieves more accurate FB picking results than the existing single-branch ISS methods, with an average picking error as low as 3.08 ms in the field data, and the percentage of traces with a picking error higher than 15 samples is as low as 0.03%, which is far superior to the network methods such as UNet, STUNet, SegNet, and Res-Unet, and effectively realizes the overall high-quality FB picking.