日本地球惑星科学連合2019年大会

講演情報

[J] ポスター発表

セッション記号 S (固体地球科学) » S-SS 地震学

[S-SS16] 地殻変動

2019年5月26日(日) 15:30 〜 17:00 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:大園 真子(北海道大学大学院理学研究院附属地震火山研究観測センター)、落 唯史(国立研究開発法人産業技術総合研究所 地質調査総合センター 活断層・火山研究部門)、加納 将行(東北大学理学研究科)

[SSS16-P14] 東海地域のひずみ観測網によるゆっくり滑りの検知能力に関する研究

*楠城 一嘉1 (1.静岡県立大学)

キーワード:ひずみ観測網、ゆっくり滑り、東海地域

We studied the ability of the Tokai Strainmeter Network (hereinafter referred to as TSN) to detect slow slips on a plate boundary where the Nankai trough earthquakes occur. Referring the method of probability-based magnitude of completeness (hereinafter referred to as PMC) that has been applied to several seismic networks (e.g., [1,2,3]), we modified it to be applicable to TSN. While previous studies on slow-slip detection capability of TSN [4] and the network covering a wider region [5] were based on the model assumption that the medium is elastic and the strainmeters record elastic strain changes caused by slow slips, our study is not based on such model assumption, but uses only empirical data: slow-slip catalog, decfiles, and station information. Using slow-slip events with magnitude M5.1-5.8 during 2012-2016, we found spatial variability of network detection probability. In general, the detection probability for slow slips of any given M is high within the network or inland, whereas it decreases with distance from the coast to offshore. In more details, the detection probability for M5.1 in Suruga Bay is above 90%. Although no strainmeter included in TSN has been installed in Suruga Bay, operating strainmeters on land are located so as to surround this Bay. If we consider a large magnitude such as M5.8, slow slips are detectable even in far offshore regions: for example, slow slips with M5.8 at the southern edge of the anticipated source region of the Tokai earthquake can be detected with high (>90%) detection probabilities, a consistent result with [5]. We further explored the possible use of our method as a network planning tool with simulation computations of installations of one or more virtual stations to identify appropriate locations for new station installations [2]. Our results show an illustrative example of the applicability of the PMC method to strainmeter networks.
Acknowledgements: This work was partially supported by JSPS KAKENHI Grant Number JP 17K18958.
References: [1] Schorlemmer and Woessner, 2008, Bull. Seism. Soc. Am. 98(5), 2103-2217; [2] Nanjo et al., 2010, Geophys. J. Int. 81(3), 1713-1724; [3] Schorlemmer et al., 2018, Bull. Seism. Soc. Am. 108 (2): 702-717; [4] Kobayashi, 2000, Quart. J. Seism. 63, 17-33; [5] http://www.bousai.go.jp/jishin/nankai/tyosabukai_wg/pdf/h281013shiryo06.pdf.