The 2016 7.0 Kumamoto earthquake resulted in exceptional datasets of Global Navigation Satellite Systems (GNSS) and seismic data. We explore the spatial similarity of the signals and investigate procedures for combining collocated sensor data. GNSS enables the direct observation of the long‐period ground displacements, limited by noise levels in regimes of millimeters to several centimeters. Strong‐motion accelerometers are inertial sensors and therefore optimally resolve middle‐ to high‐frequency strong ground motion. The double integration from acceleration to displacement amplifies long‐period errors introduced by tilt, rotation, noise, and nonlinear instrument responses and can lead to large nonphysical drifts. For the case study of the Kumamoto earthquake, 39 GNSS stations () with nearby located strong‐motion accelerometers () are investigated. The GNSS waveforms obtained by precise point positioning under real‐time conditions prove to be very similar to the postprocessed result. Real‐time GNSS and nearby located accelerometers show consistent observations for periods between and . The matching frequency range is defined by the long‐period noise of the accelerometer and the low signal‐to‐noise ratio (SNR) of GNSS, when it comes to small displacements close to its noise level. Current procedures in fusing the data with a Kalman filter are verified for the dataset of this event. Combined data result in a very broadband waveform that covers the optimal frequency range of each sensor. We explore how to integrate fused processing in a real‐time network, including event detection and magnitude estimation. Carrying out a statistical test on the GNSS records allows us to identify seismic events and sort out stations with a low SNR, which would otherwise impair the quality of downstream products. The results of this study reinforce the emerging consensus that there is real benefit to collocation GNSS and strong‐motion sensors for the monitoring of moderate‐to‐large earthquakes.