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Solid earth sciences
Spatio-temporal changes in the seismic velocity induced by the 2011 Tohoku-Oki earthquake and slow slip event revealed from seismic interferometry, using Ocean Bottom Seismometer's records
Uemura M, Ito Y, Ohta K, Hino R, Shinohara M
Ambient noise, Autocorrelation function, Seismic interferometry, Slow slip event
Variations in cross-correlation coefficient averages for each period at a lag time of zero between the 15-day ACFs and the reference ACF, using a 15 s time window from 2.5 s to 17.5 s at each OBS. These figures show variations in the cross-correlation coefficient averages (a) from period I to period II, (b) from period II to period III, (c) from period III to period IV, (d) from period IV to period V, (e) from period V to period VI, and (f) from period VI to period VII. The SSE occurred from period III to period VII, the low-frequency tremors occurred during period III, V, and VII, and period II was the period just before the SSE occurred.
Seismic interferometry is one of the most effective techniques for detecting temporal variations in seismic velocity caused by large earthquakes. Before the 2011 Tohoku-Oki earthquake (Mw9.0) near the Japan Trench, a slow slip event (SSE, Mw7.0) and low-frequency tremors were observed near the trench. Here, we applied a seismic interferometry technique using ambient noise to data from 17 ocean bottom seismometers (OBSs) installed above the focal region before the main shock. We used our technique to detect temporal variations in seismic velocity caused by the main shock, SSE, and low-frequency tremors. In the region above the large coseismic slip area, we detected a 1–2% seismic velocity decrease after the main shock. In addition, we observed very small temporal increases in seismic velocity near the SSE fault during the initial SSE stage. Moreover, for most of the OBSs, we observed temporal variations in the autocorrelation functions (ACFs) during the low-frequency tremors. These may have been caused by temporal variations in the ambient noise source distributions, resulting from low-frequency tremors. These results suggest the possibility of detecting low-frequency tremors using ACF monitoring.
