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19 20 Advancing Research and Development for the Future Unveiling the Mechanism of the Nankai Trough Earthquake GSI Advances Research and Development with an Eye on the Near Future ■ Research and Technology Development Challenge in AI-Using Automatic Mapping Analysis Room in Research Building Temporal change of asperities and sliding zones in the Nankai Trough area (Blue: asperity; Red: sliding zone) Global network of GNSS observation stations Estimation of precise orbit of GNSS satellite and correction information for Precise Point Positioning (PPP) Enhancement of crustal movement monitoring by Precise Point Positioning (PPP) (Changes in coordinates of GNSS CORS “Jonan” before and after the 2016 Kumamoto Earthquake) Grasp What Is Happening Now Real-time orthoimage taken from disaster-prevention helicopter (Red line is the flight route) Elevation data Grasping the inundation condition 2015 −0.08 −0.04 0.00 0.04 0.08 −0.08 −0.04 0.00 0.04 0.08 2016/10-2017/10 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 4/14 18:00 4/15 0:00 Largest aftershock M6.4 （ ：Current analysis －：PPP） Foreshock M6.5 6:00 12:00 East-West (m) Determining Accurate Positions of GNSS CORSs in More Detail The widespread diffusion of mobile devices including smart phones has diversified map utilization in daily life and increased the need for high-accuracy maps with updated information. Meanwhile, map main- tenance and updating that entail many processes performed by engi- neers require time and costs. Thus, a new breakthrough is demanded to overcome this problem and sophisticate and speed up the process- es for map maintenance and updating. Recently, Artificial Intelligence (AI) technology has been increasingly utilized with big data and deep learning in various fields, along with dramatic advances made in spatial awareness technology and com- puter processing capability. GSI has been advancing studies for establishing an AI system for map- ping in order to increase efficiency in mapping, achieve automatic update of the existing maps, and speed up the grasping and sharing of disaster situations in the future. In addition, GSI is conducting research and development of new tech- nology for its practical use in the near future, such as research to reveal the crustal movement mechanism and the development of more sophisticated space survey technology. Previous studies have revealed that there are areas stuck on a fault (asperities) and areas that move slowly without generating seismic waves (sliding zones) on a plate boundary which is the source of the occurrence of subduction-zone megathrust earthquakes, and their ranges change with time. GSI is working on a research for estimating temporal changes of asperities and sliding zones by combining GNSS CORSs on land and control points on the sea bottom, established by the Japan Coast Guard, to unveil the mechanism of occurrence of the megathrust earthquakes. The crustal movement has been monitored by calculating positional relationship among GNSS CORSs with high-precision. This method, called relative positioning, requires an enormous amount of calculation as the analysis is based on the combination of GNSS CORSs. Moreover, once a trouble occurs in a part of GNSS CORSs, the continuous crustal monitoring may not be possible. To overcome this weakness, GSI has been studying precise and rapid crustal movement monitoring technique based on directly calculat- ing the position of each GNSS CORS, not relative position, using the data of worldwide GNSS ob- servation stations (Precise Point Positioning (PPP)). The prompt and efficient grasping of the disaster situations is the key to disaster response. To speed up grasping of a flood- caused inundation condition, GSI is developing a system that enables real-time estimation of the volume of inundation water using the automati- cally measured inundation range and area based on video images taken by disaster-prevention heli- copters of Regional Development Bureaus of MLIT and other agencies. GSI is also examining ways to identi- fy an inundation range by using AI technology. Illustrated image of deep learning with large training data Input Inference Road Pathway Railway River Building Buildings Roads Deep Learning Training data Weighting 1 Weighting 2 Input layer Hidden layer Output layer Neural Network Estimated inundated depth (m) Advancing Research and Development for the Future
