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Total organic carbon (TOC), a critical geochemical parameter of organic shale reservoirs, can be used to evaluate the hydrocarbon potential of source rocks. However, getting TOC through core analysis of geochemical experiments is costly and time-consuming. Therefore, in this paper, a TOC prediction model was built by combining the data from a case study in the Ordos Basin, China and core analysis with artificial intelligence techniques. In the study, the data of samples were optimized based on annealing algorithm (SA) and genetic algorithm (GA), named SAGA-FCM method. Then, back propagation algorithm (BPNN), least square support vector machine (LSSVM), and least square support vector machine based on particle swarm optimization algorithm (PSO-LSSVM) were built based on the data from optimization. The results show that the intelligence model constructed based on core samples data after optimization has much better performance in both training and validation accuracy than the model constructed based on original data. In addition, R 2 and MRSE in PSO-LSSVM are 0.9451 and 1.1883, respectively, which proves that models established with optimal dataset of core samples have higher accuracy. This study shows that the quality of sample data affects the prediction of the intelligence model dramatically and the PSO-LSSVM model can present the relationship between well log data and TOC; thus, PSO-LSSVM is a powerful tool to estimate TOC.

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By using the Hirota bilinear method, we construct new lump-type solutions to an extended [Formula: see text]-dimensional Jimbo–Miwa equation, which describes certain [Formula: see text]-dimensional wave phenomena in physics. The presented solutions contain 10 arbitrary parameters and only need to satisfy four restrictive conditions to be analytic, thereby enriching the existing lump-type solutions. Moreover, we compute their interaction solutions with double exponential function waves, which include rogue wave solutions. Dynamical features of the obtained solutions are graphically exhibited.