This paper uses the function of neural network-based nonlinear mapping and particle group optimization algorithm to optimize the system, and then uses the particle group optimization algorithm of inertia weight, so as to achieve the goal of optimizing the weight of neural network training. By choosing to encode the geological parameters of the coastline with mpeg-4 and obtain the relevant data, the relevant data of the beach motion image can be obtained, so that the simulation experiment can be carried out. This paper introduces the process of establishing a multi-modal pore size distribution fitting model based on probability density function, and conducts an experimental analysis on the geological parameters and pore structure of two typical shale storage layer samples in China. Finally, the multi-peak opening distribution fitting model and the pore size distribution are obtained through quantitative fitting curves, and the geological parameters of the marine continental shale that affect the pore size distribution are investigated, and the pore size distribution of the shale and the geology of the shale are investigated. In-depth parameter analysis is to explore the thermal maturity of organic matter, and the relationship between its content and mineral composition. In addition, the coastal image simulation is researched focusing on the characteristics of coastal beach recreational sports, the problems existing in the development process, and future development countermeasures. Finally, it is pointed out that the future development trend of coastal beach leisure sports, through the integration of local leisure sports-related characteristics and professional development, so as to achieve the implantation of culture in sports, and the organic combination of the two promotes the economic and ecological environment.
Coastline geological parameters and beach motion image simulation based on particle swarm optimization