3:30 PM - 4:30 PM
[J06-P-01] Real-time slow slip monitoring with the Geodetic Data Stacking (GDS) method
Japan Meteorological Agency (JMA) has been operating a strainmeter network to detect precursory phenomena of anticipated Tokai megathrust earthquake south off the central Japan. The network consists of 17 volumetric strainmeters and 11 multi-component strainmeters. We are performing 24-hour monitoring of strain change due to possible precursory slow slip events, or episodic ones, with correcting strain changes from the effects of barometric pressure, earth and ocean tides and precipitation in real-time mode.
Miyaoka and Yokota(2012) developed a new analyzing method that enhances signal-noise ratio (S/N) by stacking strainmeter time-series data of plural components. We call this method Geodetic Data Stacking (GDS) method.
If a slip does not spread out or migrate widely, time-series data are similar to each other in shape except for their polarities and amplitudes. Some data indicate positive changes (+: extensional) and others indicate the negative (-: compressional). We reverse the polarity of changes for the components that should show negative changes so that all the data should indicate positive polarities, and stack them. The component to be reversed is selected from calculated polarity with assuming a slip of small fault at each grid points on the plate boundary.
As the result, the signal component will be emphasized and S/N will be improved. Using this method, we are monitoring small short-term slow slip events as well as a pre-slip event in real-time. In addition, we detected the long-term slow slip event going on since 2013 and are monitoring that.
In the presentation, we would like to show the outline of the GDS method and some products.
Miyaoka and Yokota(2012) developed a new analyzing method that enhances signal-noise ratio (S/N) by stacking strainmeter time-series data of plural components. We call this method Geodetic Data Stacking (GDS) method.
If a slip does not spread out or migrate widely, time-series data are similar to each other in shape except for their polarities and amplitudes. Some data indicate positive changes (+: extensional) and others indicate the negative (-: compressional). We reverse the polarity of changes for the components that should show negative changes so that all the data should indicate positive polarities, and stack them. The component to be reversed is selected from calculated polarity with assuming a slip of small fault at each grid points on the plate boundary.
As the result, the signal component will be emphasized and S/N will be improved. Using this method, we are monitoring small short-term slow slip events as well as a pre-slip event in real-time. In addition, we detected the long-term slow slip event going on since 2013 and are monitoring that.
In the presentation, we would like to show the outline of the GDS method and some products.