日本地球惑星科学連合2015年大会

講演情報

インターナショナルセッション(口頭発表)

セッション記号 M (領域外・複数領域) » M-IS ジョイント

[M-IS02] Interdisciplinary studies on pre-earthquake processes

2015年5月26日(火) 11:00 〜 12:45 201A (2F)

コンビーナ:*服部 克巳(千葉大学大学院理学研究科)、Dimitar Ouzonov(Chapman大学)、劉 正彦(台湾国立中央大学)、黄 清華(北京大学)、座長:服部 克巳(千葉大学大学院理学研究科)、劉 正彦(国立中央大学太空科学研究所)

12:33 〜 12:36

[MIS02-P02] 房総半島を対象としたMT探査と予察的考察

ポスター講演3分口頭発表枠

*小泉 直輝1Peng Han1山崎 智寛1吉野 千恵1服部 克巳1奥田 真央2茂木 透2 (1.千葉大学大学院理学研究科、2.北海道大学大学院理学研究院付属地震火山研究観測センター)

キーワード:地磁気地電流法, 房総半島

A magnetotelluric (MT) survey is one of the methods to understand the underground electric properties. In Boso area, Japan, there are three main topic to perform the MT survey; (1) to estimate underground resistivity structures related to the plate boundaries, seamount, asperities, and slow slip events, (2) to obtain a regional realistic resistivity structure for the numerical simulation in generation and propagation mechanisms of electromagnetic precursors, and (3) to develop a new MT technique to reduce the cultivated noises such as DC driven trains. In these aims, we decided to carry out the MT survey in Boso area, Japan during 2014-2016. Due to sensing down to 100 km depth, we use induction and fluxgate magnetometers. The first MT survey in 2014-2015 had 21 and 6 stations for induction and fluxgate type magnetometer, respectively. We checked the observed data and analyzed the local midnight time (02:00-04:00(JST)) data because of noises and performed 1D inversion.

The preliminarily results show that we can presume the resistivity structure about 80 m-2 km depth from the surface. A typical resistance down to 200 m depth was 1-10 ohm-m and below 200 m depth, a specific resistance was estimated at 0.1-1 ohm-m at many stations. This suggests that there is a geological boundary around 200 m depth. In comparison with the geologic structure interpreted by the reflection seismology data, the upper part seems to be the Shimousa Group, and the lower, the Kazusa Group (Earthquake Research Committee, 2005).

To presume resistivity structure at the deeper depth, it is necessary to remove the artificial noises from observed MT data. These observed noises have characteristics of transient signals and processes in time domain are required such as singular spectrum analysis and neural network analysis. Further preprocessing will be essential.