Title: High-resolution exposure and sensor deployments: Chances for seismology and risk research
The substantial reduction of disaster risk and live losses, a major goal of the Sendai Framework by the United Nations Office for Disaster Risk Reduction (UNISDR) requires a clear understanding of the dynamics of the built environment and how it affects, in case of natural disasters, the life of communities, represented by local governments and individuals. Communities that participate in risk assessments increase their understanding of efficient risk mitigation measures. Our Global Dynamic Exposure model and its technical infrastructure build on the involvement of society in a crowd-sourced approach. Simultaneously, it helps educating communities in the risks they are facing and how they can prevent losses of lives.
We are in the process of creating super high-resolution data products for better assessment of seismic risk. Based on the plethora of open data and volunteer geographic information like OpenStreetMap, we have created a super high-resolution and dynamic exposure model that attempts to characterize probabilistically every building on Earth for better risk assessments. We are employing a crowd-sourced capturing of exposure indicators using OpenStreetMap (OSM), an ideal foundation with already more than 500 million building footprints (growing by ~150’000 per day), and information about school, hospital, and other critical facilities. With our OpenBuildingMap system, we are harvesting this dataset by processing every building in near-realtime. We are collecting exposure and vulnerability indicators from explicitly provided data (e.g. hospital locations), implicitly provided data (e.g. building shapes and positions), and semantically derived data, i.e. interpretation applying expert knowledge. The expert knowledge is needed to translate the simple building properties as captured by OpenStreetMap users into a probabilistic assessment of vulnerability and exposure indicators and subsequently into probabilistic building classifications as defined in the Building Taxonomy 2.0 developed by the Global Earthquake Model (GEM). With this approach, we increase the resolution of existing exposure models from aggregated exposure information to building-by-building vulnerability.
Simultaneously, we have started with dense acceleration-sensor deployments in the wider Tokyo area. We have explored the utility of the smartphone-type sensors built into small devices directly plugged to power outlets in the wall for the use by volunteers and companies. We deployed 10 devices to private people in the Zama region in 2021 and 50 devices in the larger Tokyo region in 2022 for about a half year each. Additionally, we have equipped the 48-story Tokyo Metropolitan government building in Shinjuku, Tokyo, with many devices on floors ranging from 1st to 44th, and one 6-story building of the Tokyo Narita International Airport with many devices on four floors. All devices have provided us with both three-component acceleration records and seismic intensities in the Japan Meteorological Agency (JMA) scale, which is familiar to non-professional Japanese people. The measured seismic intensities on different floors and within the same floor show a variety of values different to the reported JMA intensity for the wider area. Also, we have obtained human assessments of experienced seismic intensity.
We want to present our future plans for massive sensor deployments and their goals. We are planning to expand the experiment to more than 1000 households in the Tokyo area for better defining the needs of homeowners, tenants, or facility managers. Further plans include equipping neighboring buildings with sensors in order to estimate the ground-motion variability on small spatial scales to better understand the necessary measuring bandwidth for ground motions and intensities. A collaboration with ISC will show how the sensor data can be used for improving overall seismic networks in terms of location uncertainty and initial localization speed.