The brain network is an effective tool and has been widely used in the field of brain neurodegenerative disease analysis. Due to the high cost of accessing medical image data, efforts have been devoted to investigating data augmentation. However, the brain network containing topological characteristics of non-European space is different from the traditional image data, which makes it challenging to synthesize brain structural connectivity and limits the application of brain network analysis. In this paper, a Hemisphere-separated Cross-connectome Aggregating Learning (HCAL) model is proposed to synthesize realistic and diverse brain structural connectivities. Specifically, the latent representation is transformed from structural connectivity by the graph variational autoencoder (GVAE). To generate more diverse and high-quality structural connectivities, the hemisphere-separated generator with a cross-connectome aggregating mechanism is developed to first learn local topological patterns by splitting the whole brain into inter- and intra-hemispheres, then capture global topological characteristics among all the neighbors for each brain region. Also, the connectivity-aware discriminator is devised to make the adversarial training stable and enhance the disease diagnosis. Evaluations of the public Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show that the proposed model generates brain structural networks with higher quality and more diversity than related methods. In addition, the classification performance using the augmented data achieved by our approach achieves an accuracy improvement of over 3% compared with the competing method. The proposed model provides an alternative way to synthesize brain networks for connectivity-based disease analysis.

The transient electromagnetic (TEM) method is a non-contact technique used to identify underground structures, commonly used in mineral resource exploration. However, the induced polarization (IP) will increase the nonlinearity of TEM inversion, and it is difficult to predict the geoelectric structure from TEM response signals in conventional gradient inversion. We select a heuristic algorithm suitable for nonlinear inversion—a whale optimization algorithm to perform TEM inversion with an IP effect. The inverse framework is optimized by opposition-based learning (OBL) and an adaptive weighted factor (AWF). OBL improves initial population distribution for better global search, while the AWF replaces random operators to balance global and local search, enhancing solution accuracy and ensuring stable convergence. Tests on layered geoelectric models demonstrate that our improved WOA effectively reconstructs geoelectric structures, extracts IP information, and performs robustly in noisy environments. Compared to other nonlinear inversion methods, our proposed approach shows superior convergence and accuracy, effectively extracting IP information from TEM signals, with an error of less than 8%.

Utilizing neural network models to inverse time-domain electromagnetic signals enables rapid acquisition of electrical structures, a non-intrusive method widely applied in geological and environmental surveys. However, traditional multi-layer perceptron (MLP) feature extraction is limited, struggling with cases involving complex electrical media with induced polarization effects, thereby limiting the inversion model’s predictive capacity. A graph-topology-based neural network model for strata electrical structure imaging with long-dependency feature extraction was proposed. We employ graph convolutional networks (GCN) for capturing non-Euclidean features like resistivity-thickness coupling and Long Short-Term Memory (LSTM) to capture long-dependency features. The LSTM compensates for GCN’s constraints in capturing distant node relationships. Using case studies with 5-strata and 9-strata resistivity models containing induced polarization effects, compared to traditional MLP networks, the proposed model utilizing time-domain features and graph-topology-based electrical structure extraction significantly improves performance. The mean absolute error in inversion misfit is reduced from 10–20% to around 2–3%.