The time series of the total electron content (TEC) at a certain location derived by the local network of ground-based Global Navigation Satellite System (GNSS) receivers or extracted from the global ionosphere map (GIM) is useful for detecting seismo‐ionospheric anomalies at the regional level. When the detected anomalies are similar to those repeatedly appearing before large earthquakes in the same region, it might be perceived as a temporal seismo-ionospheric precursor (SIP). To discriminate the possible SIPs from global effects (such as solar disturbances, magnetic storms, etc.), a global search for anomalies using GIM TEC data is an ideal approach. Spatial analysis simultaneously detects anomalies similar to the temporal SIP at each lattice and indicates the global distribution or pattern of the detected anomalies. When the detected anomalies specifically and continuously appear within the monitoring region, we can observe spatial SIPs of the GIM TEC. To further study the fine structure and dynamics of the observed SIPs, a dense network of ground‐based GNSS receivers is required. By applying the residual minimization training neural network (RMTNN) tomographic approach to the TEC between the GNSS satellite and the network receivers, the three‐dimensional fine structure of the ionospheric electron density can be obtained.