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The extremely thick dark mudstone of the Permian Linxi Formation in the Kundu-Taohaiyingzi area of northeastern China is a promising potential area for shale gas prospecting in the periphery of the Songliao Basin. This study involved a geological field survey, outcrop sample collection, and comprehensive geochemical analysis to conduct an in-depth analysis of the organic matter enrichment and hydrocarbon generation potential of the dark mudstone of the target zone, using organic petrology and organic geochemistry. The results show that the thermal maturity of the dark mudstone of the Linxi Formation is in the mature to high-mature stage. The organic matter is dominated by type II kerogen and supplemented by type III kerogen, and the average total organic carbon content is 0.76%, classifying it as a medium-good source rock. The main peak of carbon numbers is primarily distributed between nC17 and nC19, with a smaller amount at nC21. The distribution of n-alkanes follows a pre-peak pattern characterized by medium and low carbon numbers, suggesting that the organic matter originates from aquatic plants, bacteria, and algae. Combined with the fact that the ratios of pristine and phytane (Pr/Ph) are mainly distributed in the range of 0.99â1.33, the sedimentary environment is a weakly reduced to weakly oxidized lacustrine bay. The systematic analysis of the source rocks of the Upper Permian Linxi Formation in the Kundu-Taohaiyingzi area indicates favorable prospects for shale gas exploration. Utilizing a comprehensive superposition method of geological information, this article identified the Yamen GachaâSaihan Tala area as a potential area for shale gas exploration.

The imbalanced regional financial development usually hinders the healthy development of the national economy. In the past, scholars focused on the study of indicators affecting financial development and put forward suggestions to improve financial development, but few proposed the idea of predicting the future financial development level of a certain region through integrated learning. This paper aims to solve this problem by constructing regional financial development system. Taking Beijing as an example, the author firstly constructs the index system of regional financial development, and uses principal component analysis method to reduce the dimension of 41 financial indexes. Then, the index system is coupled with the attribute hierarchy model and the importance of criteria through the inter-criteria correlation algorithm. Finally, Bagging weighted ensemble learning is used to integrate artificial neural network model, multivariable linear regression model and Holt-Winters prediction algorithm to predict the future financial development level of the region with high precision. Calculate the financial development index of Beijing from 2021 to 2025, and the financial development index of Beijing in 2025 reaches 9.891. Through empirical analysis, this paper verifies the practicality of the ANN-MLR-Holt-Winters integrated learning model, which can not only predict the data of future indicators and their proportion in the overall level of financial development, but also help departments predict the future of some indicators.