Presentation is loading. Please wait.

Presentation is loading. Please wait.

Earthquake source parameters inferred from teleseismic source time functions Orfeus Workshop “Waveform Inversion” June, 19th, 2008 Martin Vallée and Jean.

Similar presentations


Presentation on theme: "Earthquake source parameters inferred from teleseismic source time functions Orfeus Workshop “Waveform Inversion” June, 19th, 2008 Martin Vallée and Jean."— Presentation transcript:

1 Earthquake source parameters inferred from teleseismic source time functions Orfeus Workshop “Waveform Inversion” June, 19th, 2008 Martin Vallée and Jean Charléty

2 Low frequency surface waves are generally considered as the most reliable data for retrieving focal mechanism and moment : - Their low frequency content gives an easier access to the global parameters of the source - When earthquakes are complex (i.e. multiple subevents with different mechanisms), surface waves are able to give an average focal mechanism (example 2002 Denali earthquake) - For exceptional events (i.e. Sumatra), surface waves are the only adapted waves because of the mixing of the different body waves Surface waves are routinely used by Global CMT: - Body waves are also included for moderate to large earthquakes, (Mw<7.5) but surface waves are likely to control the inversion for larger magnitudes Identification of large earthquake source parameters

3 Illustration of body waves mixing for the 2004 Sumatra event Earthquake duration is longer than time difference between arrival of body waves Velocity (m/s) Time (x100s) P P S S? Mainshock (2004/12/26) Nearby Mw 7.2 earthquake (2002/11/02) Vertical seismic recordings at CAN station (Geoscope, Australia)

4 Principle of Surface wave analysis Kanamori and Given, 1982 The vertical displacement Ur(r,t) is related with the independent components of the moment tensor using known excitation functions Equation governing the low frequency Rayleigh waves radiation (similar for Love waves) Using stations in different azimuths, the inverse problem should simultaneously retrieve Mxx, Myy, Mzz, Mxy, Myz, Mxz

5 Limitation of surface waves - Late arrivals ( problem for very rapid information / tsunami alert ) - Trade-off between Dip and Moment Surface wave radiation X U r (r,t) ~ sin(2xdip) M 0 Consider a superficial inverse earthquake (example: subduction interplate event) : - Qr ~ 0 - λ = 90° Moment tensor components

6 Catalog of large subduction interplate earthquakes (1990-2007; inverse mechanism; depth<50km; 7.7<Mw Global CMT<8.9)

7 1) Comparison with aftershocks Clues of dip determination problems with surface waves Small blue star: mean dip of aftershocks (Global CMT) Number of used aftershocks is written for each earthquake Large red star: Global CMT dip of the mainshock Dip is significantly different and almost always (15 of 17) smaller for the mainshock

8 2) Comparison with more detailed studies Need to constrain with other data: - Geodesy (but what about rigidity?) - Other wave types which do not suffer from the same trade-off between dip and moment 1994 Java earthquake : 7° -> 12° (Abercrombie et al., 2001) BW+SW 1995 Jalisco earthquake : 9° -> 14° (Mendoza & Hartzell,1999) BW 2001 Peru earthquake :18 ->23° (Bilek and Ruff, 2002) BW 2003 Hokkaido earthquake : 11° -> 20° (Yagi, 2004) BW +SM 11° -> 20°; Mw=8.3->8.1 (Miyazaki et al. 2004) GPS - Most studies take Global CMT for further analyses - Some examples of studies searching a refinement of Global CMT mechanism:

9 What about Body waves ? Advantages - Arrive before - No trade-off between focal mechanism and moment - High frequency body waves much easier to model than high frequency surface waves better to explain rupture “details” Drawbacks - Low frequency content more difficult to retrieve for superficial events - Limitations for giant or very complex earthquakes

10 Example of low frequency effects seen by body waves For a deep event Modele de glissement Clear effect of doubling the seismic moment DEEP EARTHQUAKE Depth ~ 155km Mo = 3.1x10 21 N.m ; M w = 8.25 Mo = 6x10 21 N.m ; M w = 8.45 Dist=82.5° Az=10° Slip model (strike,dip,rake = 318, 20,65°) Same slip model contaminated by large constant slip area Teleseismic P-wave displacement

11 For a superficial event Are we able to detect these small differences in the global network seismograms? If yes, moment can be retrieved, and body waves are useful from low to high frequencies For a deep event Modele de glissement SUPERFICIAL EARTHQUAKE Depth ~ 25km Why such small differences? pP and sP reflected phases arrive just after P phase and have generally an opposite polarity Destruction of the low frequency part Dist=82.5° Az=10° Mo = 2.8 10 21 N.m Mo = 5.3 10 21 N.m

12 Goal of the method: Quasi-automatic technique for retrieving simultaneously the first order parameters (focal mechanism and depth) and finer details (duration and shape of the source time function).

13 = H (φ,δ,λ,z h,Z 1,Z 2,Vr z ) We can numerically determine G 0 and hence H as a function of the 7 parameters ( φ,δ,λ,z h,Z 1,Z 2,Vr z ). The deconvolution of H from U gives the horizontal apparent source time function, equal to : Principle of the inverse problem: what is the set of the 7 parameters which simultaneously: - minimizes the variance of M 0 computed at each station - best explains the waveforms of U, when reconvolving H with F F has a simple physical property, independent of the station: We use the stabilized deconvolution method of Vallée (2004), which imposes the causality and positivity of F

14 Practical implementation - First step (signal duration) - Define the duration of the P wave signal - We use the 1Hz duration of the velocity seismograms (eg. Ni et al., 2005; Lomax et al., 2006) Inversion program: optimization of function H (in terms of (φ,δ,λ,z h,Z 1,Z 2,Vr z )), so that the moments defined by function F at all stations remain as stable as possible. - Example for the 2005 Northern Sumatra earthquake :119s * = Filtered P wave signal Function HApparent source time function F - Second step (P and SH waves optimized deconvolution) Use of Neighborhood algorithm (Sambridge, 1999) Example for one P- signal

15 Obtained Focal mechanisms and moment Focal mechanism results compared with Global CMT Very good general agreement between this study and global CMT

16 Dip results compared with Global CMT and aftershocks The mainshock dip is generally closer from the aftershocks dip The tendency of underestimating the aftershocks dip has disappeared This study

17 Moment results compared with Global CMT and aftershocks Moment is found sometimes close but generally smaller than global CMT moment

18 Do our results agree with the M sin(2xdip) “rule” ? This study The results of this study are validated by the fact that the product M sin(2xdip) is very similar the one deduced from global CMT

19 Further analyses are possible, using the apparent source time functions retrieved by this analysis Peru earthquake, 23/06/2001 As shown by more detailed studies, this earthquake is made of two subevents, the second one being much larger than the first

20 Conclusions 1) We have shown that the low frequency content of large earthquakes can be retrieved by body waves analysis - Potential for reliable rapid information - The difficulty is related to the reduction of low frequency energy due to reflected phase interactions. 2) As theoretically known, we show that global CMT is likely to lack resolution for dip and moment separation. This trade-off generally leads to a dip underestimate and a moment overestimate. Perspectives 1)It is important to check if the focal mechanisms we propose here would be “accepted” by surface wave analysis 2)The apparent source time functions should allow, quickly after an earthquake, to define its length (useful for quick information/ tsunami alert). 3)Further analysis of apparent source time functions should give information on the degree of complexity of large earthquakes


Download ppt "Earthquake source parameters inferred from teleseismic source time functions Orfeus Workshop “Waveform Inversion” June, 19th, 2008 Martin Vallée and Jean."

Similar presentations


Ads by Google