2011年3月11日東北沖地震発生前の地殻変動の異常について、JpGU 2023 (幕張メッセ)-2023年5月23日(水)-24日(木)にて発表します。

VLBIの発想を基礎に、我々が開発した相関解析(CRA: CoRrelation Analysis)は、今まで電離圏異常の検出のみに使われてきましたが、初めて地殻変動データに適用しました。2011年3月11日東北沖地震発生前の地殻変動データの相関解析による異常検出に関する結果をJpGU 2023の5月23日(水曜日)ポスターセッションで発表します。我々は地震発生直前の電離圏異常と対応する地殻変動の異常の検出を通してその物理メカニズム(電離圏異常現象に伴う因果関係)を明らかにしたいと考えております。

発表タイトル: Detection of Anomalous Crustal Deformation by CoRrelation Analysis

発表者: 田中宏樹 (京都大学大学院情報学研究科数理工学コース　博士課程学生)

共著者: 梅野健 (京都大学大学院情報学研究科数理工学コース長・教授)

発表番号: SGD02-P16

-Abstract-

We report the results of detecting anomalies in crustal deformation by applying the CoRrelation Analysis (CRA) method to the time series of crustal deformation and statistical study of its relationship to the seismic activity before and after the anomalies. Some studies have pointed out that anomalous change in the ionosphere’s total electron content (TEC) sometimes precedes significant earthquakes [1]. CRA has been used to detect such TEC anomalies because it can reduce the influence of noise by using time series of TEC at a single station in conjunction with its surrounding stations [2-4]. Time series of crustal deformation also includes a lot of noise and trends. Eliminating them is essential in detecting characteristic crustal deformation that can be related to significant seismic activities.

In this study, we apply CRA to two kinds of data series of crustal deformation: F5 solution from 2004 to 2021, recording daily station positions published by GSI [5], and PPP solution less than two months before the Tohoku earthquake (M9) in 2011, at 30-minute intervals by NASA [6]. These time series data contain some missings, and we select the stations for analysis and calculate CRA with these missings in mind. We apply CRA to the relative variations from the reference stations throughout Kyushu. The use of relative variation removes the global trend while suppressing the fluctuations in the reference station group. The correlation values calculated by CRA for these relative variations show how the crustal deformation at the target station differs from those of reference stations while reducing noise. We performed CRA for target stations in Japan, except for the reference stations in Kyushu and nearby prefectures, shifting the time window. Therefore, time series of correlation values are obtained for target stations throughout Japan except for Kyushu and nearby regions.

We first examine the distribution of correlation values throughout Japan without Kyushu and nearby prefectures for each time window. The results show that the distribution varies for each time window; in some cases, the correlation values are globally high. However, these distributions become similar functions after being rescaled by the average of each time window. Thus, we define the degree of the anomaly in crustal deformation at a given station in a time window by (the correlation value)/(the average)-1. This enables us to evaluate the degree of crustal deformation anomaly quantitatively and to discuss the relationship between characteristic crustal deformation and active seismicity statistically. We examine the conditional probabilities between the degree of crustal deformation anomalies at a station in a time window and the occurrence rate of earthquakes around the station before and after that time window. Statistical results suggest a correlation between high occurrence rates and large crustal deformation anomalies before and after the seismic activity in the time series of the F5 solution. However, in the time series of the PPP solution, such a tendency is observed only between crustal deformation and following seismic activity.

[1] Kosuke Heki. Ionospheric electron enhancement preceding the 2011 tohoku-oki earthquake. Geophysical Research Letters, Vol. 38, No. 17, 2011.
[2] Takuya Iwata and Ken Umeno. Correlation analysis for preseismic total electron content anomalies around the 2011 tohoku-oki earthquake. Journal of Geophysical Research: Space Physics, Vol. 121, No. 9, pp. 8969–8984, 2016.
[3] Takuya Iwata and Ken Umeno. Preseismic ionospheric anomalies detected before the 2016 Kumamoto earthquake. Journal of Geophysical Research: Space Physics, Vol. 122, No. 3, pp. 3602–3616, 2017.
[4] Shin-itiro Goto, Ryoma Uchida, Kiyoshi Igarashi, Chia-Hung Chen, Minghui Kao, and Ken Umeno. Preseismic ionospheric anomalies detected before the 2016 Taiwan earthquake. Journal of Geophysical Research: Space Physics, Vol. 124, No. 11, pp. 9239–9252, 2019.
[5] https://terras.gsi.go.jp
[6] https://gipsy-oasis.jpl.nasa.gov/index.php?page=pppdata