A novel real-time PPP-AR framework for continuous crustal deformation monitoring across Japan

Kinematic precise point positioning (PPP) is a powerful technique for Global Navigation Satellite System (GNSS)-based real-time crustal deformation monitoring. However, PPP float solutions are often affected by sub-daily periodic fluctuations, caused by orbit errors and atmospheric delays, thereby motivating the adoption of PPP with ambiguity resolution (PPP-AR). To enhance PPP-AR performance in the sub-daily domain, we investigated the optimized network configuration for phase bias estimation with respect to network scale and receiver type consistency. Using GEONET observations in Japan together with GFZ real-time orbit and clock products, we sequentially estimated satellite phase biases from receiver-type-specific regional networks and, by applying these estimates, processed 30-s GEONET data from approximately 1300 stations in a simulated real-time kinematic PPP-AR mode. Two weeks of data in 2025 were analyzed to assess noise characteristics, together with case studies of recent seismic and volcanic deformation events. The results demonstrate significant suppression of long-term (> 2–3 h) coordinate fluctuations associated with GPS satellite orbital resonance while retaining short-term (< 2–3 h) noise levels comparable to those of float solutions. With nearly complete ambiguity resolution, horizontal positioning precision reached the millimeter level (daily standard deviation), representing an improvement of up to 40% relative to float solutions. These improvements were achieved through enhanced phase bias estimates using regional networks and user-side PPP-AR that is consistent with the derived phase bias estimates. The enhanced performance enables the capture of not only co-seismic displacements but also deformations ranging from several centimeters to a decimeter that evolve over several hours to several days, including earthquake swarms, volcanic inflation, and very early post-seismic deformation. Based on these findings, we propose a novel PPP-AR framework in which phase biases are estimated from receiver-type-specific regional networks, followed by user-side PPP-AR employing the same receiver type as that used within the network. This framework extends the applicability of real-time PPP to the sub-daily domain and helps bridge the temporal gap in GNSS-based deformation monitoring.