Conventional neural networks (NNs) have been extensively used to model the spatial heterogeneity of rock properties from seismic inversion. Nevertheless, these generic NNs have a single network structure, which leads to overfitting and convergence difficulties. Furthermore, for stable predictions, conventional NNs highly depend on the initial weights and bias values. This research focuses on resolving the key problems of the existing NNs. In this paper, we propose and apply a novel neural network based on a multilayer linear calculator (MLLC) to estimate seismic anisotropy and fracture porosity in structurally complex and deeply buried carbonate reservoirs. This method, unlike conventional NNs, develops a nonlinear projection relationship between seismic and well log parameters to predict the spatial variation of seismic anisotropy and fracture porosity. We evaluate inversion effectiveness further by optimizing the MLLC with the particle swarm optimization (PSO) algorithm. We evaluate this new kind of MLLC neural network using computer-based simulations of complex models. After verifying the model′s reliability, we used it to estimate anisotropy and fracture porosity in two case studies from separate regions of China. Focused on anisotropy and fracture porosity estimations, the MLLC neural network outperformed the conventional NNs using backpropagation (BP) neural networks in simulation and field studies. The results indicate that the proposed methodology is considered valid for the anisotropic and porosity prediction of fractured reservoirs in other basins in China with similar geological settings and analogous basins anywhere in the world.