ABSTRACT
In recent years, predictions of damage from earthquakes have been made on a prefectural scale, and expectations exist that more detailed damage forecasts should be made even on a city/town/village scale. It is important to know detailed ground characteristics to do damage prediction on a fine scale. Using GIS is the best way to communicate this planar disaster prevention information to the general public. Yokohama City is the second largest city in Japan and developed as part of the capital region of Metropolitan Tokyo. Recently, the population of this city has reached about 3,000,000, and economic and cultural facilities, social infrastructure, and residential complexes are concentrated in this city. The capital region, including Yokohama City, was attacked by the 1923 Great Kanto Earthquake (M7.9) and Yokohama City was devastated by this earthquake. From the research so far, it is known that the H/V spectrum obtained from microtremor observation has a good correlation with the ground characteristics. The authors have been conducting high-density tremor observations that have been ongoing since the 1990s, mainly in Kanagawa Prefecture, Japan. Here, we have organized the predominant periods obtained from the observation results for Yokohama City. The entirety of Yokohama City was divided into 250 m × 250 m meshes and their centers were used as microtremor observation sites. Excluding sites that could not be used due to geographical conditions, observations were made at approximately 5700 sites. So, we compared the data obtained separately, such as the period, terrain classification, and amplification characteristics. The distribution maps of predominant periods in Yokohama City show that the city contains a lot of artificially transformed land, and consequently, the distribution of predominant periods is not uniform. However, it can be seen that the periods become gradually longer, moving from the higher elevation eastern part toward the lower elevation western part. Investigation of the site amplification factors and detailed topographical classifications indicates a clear correlation with the predominant period distribution.