T p d e Ê 0 LKBD-T CHMEL-T p a e a o v a c d ls a f m i l t e t Ê g y a...
1
Structural models of the lithosphere based on P-wave velocities only lead to large ambiguities in terms of composition and physical parameters (e.g. fluid content, temperature). Additional information about S-wave velocities can put important constraints on composition and the physical state of the lithosphere, and improve the accuracy of the earthquake location. Since S-waves of local earthquakes are often contaminated by P-wave coda and S-P phase conversions, correct identification and timing of S-wave arrivals are difficult and sometimes ambiguous. To check the consistency of the picked S-wave arrivals obtained from the routine analysis of local earthquake data at the Swiss Seismological Service (SED) between 1999 and 2004, we apply the concept of minimum 1D models to P and S arrivals (independently and combined). In case of consistently picked S-waves we should observe only small deviations in hypocenters and residuals of relocated earthquakes between each model. Finally ,we compare the detected outliers with those derived from Wadati inversion, which is commonly used to assess the quality of S-arrivals. Assessment of Quality and Consistency of S-Wave Arrivals in Local Earthquake Data 1 2 1,2 1 T. Diehl , N. Deichmann , S. Husen and E. Kissling 1 Institute of Geophysics, ETH Zurich, Switzerland 2 Swiss Seismological Service, ETH Zurich, Switzerland Overview Introduction Minimum 1D P Minimum 1D S Minimum 1D P+S 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N 0 20 40 Depth (km) 5 6 7 8 9 10 11 Longitude (deg) 46 47 48 49 Latitude (deg) 0 20 40 Depth (km) 0 50 100 150 200 No. of Events 0 20 40 Depth (km) Hypocenters & Residuals Conclusions Wadati -15000 -10000 -5000 0 5000 10000 15000 Depth Shift (m) 0 50 100 150 200 250 300 350 400 450 Event sigma (m): 1971 mean (m): 315 -15000 -10000 -5000 0 5000 10000 15000 Latitude Shift (m) 0 50 100 150 200 250 300 350 400 450 Event sigma (m): 395 mean (m): 56 -15000 -10000 -5000 0 5000 10000 15000 Longitude Shift (m) 0 50 100 150 200 250 300 350 400 450 Event sigma (m): 477 mean (m): 23 -15000 -10000 -5000 0 5000 10000 15000 Depth Shift (m) 0 50 100 150 200 250 300 350 400 450 Event sigma (m): 1815 mean (m): 1015 -15000 -10000 -5000 0 5000 10000 15000 Latitude Shift (m) 0 50 100 150 200 250 300 350 400 450 sigma (m): 348 mean (m): 80 -15000 -10000 -5000 0 5000 10000 15000 Longitude Shift (m) 0 50 100 150 200 250 300 350 400 450 Event sigma (m): 444 mean (m): -86 Event -20 -10 0 10 20 Depth Difference (km) 0 50 100 150 200 Event sigma (m): 3669 mean (m): 883 -20 -10 0 10 20 Latitude Difference (km) 0 50 100 150 200 Event sigma (m): 1491 mean (m): -422 -20 -10 0 10 20 Longitude Difference (km) 0 50 100 150 200 Event sigma (m): 1433 mean (m): 760 -3 -2 -1 0 1 2 3 Origin Difference (s) 0 50 100 150 200 Event sigma (s): 0.34 mean (s): -0.28 Residuals in Minimum 1D Models 0 500 1000 1500 2000 2500 3000 Number of observations -2 -1 0 1 2 Residual (s) 0 500 1000 1500 2000 2500 3000 Number of observations -2 -1 0 1 2 Residual (s) 0 200 400 600 800 Number of observations -2 -1 0 1 2 Residual (s) 0 200 400 600 800 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 60 70 80 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 60 70 80 Number of observations -2 -1 0 1 2 Residual (s) 0 200 400 600 800 1000 1200 1400 Number of observations -2 -1 0 1 2 Residual (s) 0 200 400 600 800 1000 1200 1400 Number of observations -2 -1 0 1 2 Residual (s) 0 100 200 300 400 500 Number of observations -2 -1 0 1 2 Residual (s) 0 100 200 300 400 500 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 Number of observations -2 -1 0 1 2 Residual (s) Obs. Weight: 0 Obs. Weight: 1 Obs. Weight: 0 Obs. Weight: 1 Obs. Weight: 3 Obs. Weight: 3 P + S Preselection from P+S model (492 events) No preselection criteria (1588 events) 0 200 400 600 800 1000 Number of observations -2 -1 0 1 2 Residual (s) 0 100 200 300 400 500 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 Number of observations -2 -1 0 1 2 Residual (s) 0 200 400 600 800 1000 Number of observations -2 -1 0 1 2 Residual (s) 0 100 200 300 400 500 Number of observations -2 -1 0 1 2 Residual (s) 0 10 20 30 40 50 Number of observations -2 -1 0 1 2 Residual (s) S Events common in P and S data (238 events) Hypocenters P email contact: [email protected] We wish to thank INGV, LED, RENASS, RSNI, SISMALP and ZAMG for providing us with additional data. Acknowledgements 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N 0 20 40 Depth (km) 5 6 7 8 9 10 11 Longitude (deg) 46 47 48 49 Latitude (deg) 0 20 40 Depth (km) 0 50 100 150 200 No. of Events 0 20 40 Depth (km) 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N Station Corrections (S-Wave) 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N 0 20 40 Depth (km) 5 6 7 8 9 10 11 Longitude (deg) 46 47 48 49 Latitude (deg) 0 20 40 Depth (km) 0 50 100 150 200 No. of Events 0 20 40 Depth (km) 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N Station Corrections (P-Wave) Statistics 0 50 km LKBD BALST BNALP BOURR CHKAM DAVON HASLI LLS MMK MUO SIERE SLE CHE TORNY VDL WILA ZUR KAMOR 6°E 8°E 10°E 47°N 46°N 48°N France Austria Italy Germany Switzerland Beckenried earthquake: 2000/08/17 07:14 Z = 10 km, M = 3.0 Statistics Statistics Stability Tests for Min. 1D P+S Beckenried event Example waveforms of the Beckenried earthquake illustrate the difficulties in phase identification and picking of local S-wave data. The seismograms are plotted as time reduced Wadati diagrams. Stereographic diagrams for 3 Swiss stations demonstrate the distribution of azimuth, distance and focal depth for observed P and S arrivals. SLE shows prominent differences between P and S for larger epicentral distances. 0 0 2 40 60 0 8 100 120 140 160 180 200 20 2 240 260 280 300 20 3 4 30 P 0 0 2 40 60 80 00 1 120 140 10 6 180 200 20 2 20 4 260 280 0 30 20 3 4 30 S 0 10 20 30 Focal Depth (km) 0 30 60 90 120 # obs. 0 10 20 30 40 Focal Depth (km) 0 30 60 90 120 # obs. 0 100 200 Epicentral Distance (km) 0 0 2 40 60 80 100 120 140 10 6 180 20 0 20 2 240 6 20 280 300 320 340 P 0 20 40 60 80 100 120 140 160 180 0 20 20 2 240 6 20 0 28 300 320 30 4 S 0 10 20 30 Focal Depth (km) 0 30 60 # obs. 0 10 20 30 40 Focal Depth (km) 0 30 60 # obs. 0 100 200 Epicentral Distance (km) 0 20 40 60 80 100 10 2 140 6 10 180 00 2 220 240 260 280 300 320 30 4 P 0 20 0 4 60 80 100 10 2 40 1 6 10 180 0 20 220 0 24 260 280 0 30 30 2 30 4 S 0 10 20 30 Focal Depth (km) 0 30 60 90 120 150 180 # obs. 0 10 20 30 40 Focal Depth (km) 0 30 60 90 120 150 180 # obs. 0 100 200 Epicentral Distance (km) P S Distance (km) Azimuth (°) A) A) Selected data set to compute min. 1D P-wave model. Black dots: events common in P, S, P+S. B) Minimum 1D P-wave model, and final models from “high-low” test. C) Station corrections P-wave C) A) Selected data set to compute min. 1D S-wave model. Black dots: events common in P, S, P+S. B) Minimum 1D S-wave model, and final models from “high-low” test. C) Station corrections S-wave C) A) Random Mislocation Systematic Mislocation A) A) Selected data set to compute min. 1D P+S-wave model. Black dots: events common in P, S, P+S. B) Station corrections for P- and S-waves. C) Final minimum 1D model for P (blue, solid), S (red, solid) and vp/vs (black, solid). The initial models (independent models) are represented by the grey, dashed lines. D) To test the robustness of the final minimum model, we shift the hypocenters randomly (lat,lon,depth) and systematically (depth) by 10 km before introducing them in the joint inversion (grey dots). Velocities are overdamped for test with systematically shifted hypocenters. Hypocenters are relocated close to their original locations indicating a stable minimum (final hypocenters: black dots, final velocity model for random shift: solid green in C). A) Differences in hypocenter location obtained from relocation in min. 1D P-wave model (using only P arrivals) and S-wave model (using only S arrivals). B) Residuals of earthquakes relocated with P-arrivals only (in min. 1D P). C) Residuals of earthquakes relocated with S-arrivals only (in min. 1D S). D) Residuals of earthquakes relocated using P and S arrivals in min. 1D P+S wave model (preselected “high quality” data set from min. 1D P+S). E) Same as D) for unfiltered data set of 1588 earthquakes. 0 10 20 30 40 50 60 Depth (km) 4 5 6 7 8 9 v (km/s) P Initial: high/low Output (low-in) Output (high-in) Min 1D P-model well resolved B) 0 10 20 30 40 50 60 Depth (km) 2 3 4 5 v (km/s) S Initial: high/low Output (low-in) Output (high-in) Min 1D S-model well resolved B) D) 5°E 6°E 7°E 8°E 9°E 10°E 11°E 46°N 47°N 48°N 49°N P Delays S Delays B) P Ray S Ray Station Hypocentre P Ray Station Hypocentre S Ray Station Hypocentre 0 10 20 30 40 50 60 Depth (km) 2 3 4 5 6 7 8 9 Velocity (km/s) 0 10 20 30 40 50 60 1.6 1.7 1.8 1.9 v /v Ratio P S v S v P 1.73 well resolved Final min 1D P,S in (indi.) Final stabilty test C) A) B) C) D) E) A) Simultaneous Wadati inversion for the preselected P+S data set of 492 earthquakes. We define outliers (white dots) as residuals ≥ 1 s or £ -1 s. 0 5 10 15 20 25 T = T - T (s) S-P s p 0 10 20 30 40 50 60 70 T (s) P Vp/Vs = 1.699, +/- 0.001 rms = 0.422 T /T couple P S-P Outlier (residual ≥ 1 s or £ -1 s) vp/vs -1 C) A) Outliers: Wadati: 56 (S-P) Min. 1D P+S: 88 (P) + 37 (S) We derive minimum 1D models for P, S, and P+S. Within these models hypocenters and residuals of the relocated earthquakes show similar results for P, S, and P+S. Mean values and s of equal quality classes show no significant bias between P and S arrivals. Therefore we assume a comparable consistency of P and S arrivals in our data set. The amount of high-quality S arrivals is lower than for P. This can be explained by a higher conservatism in S arrival picking. In general we detect more and sometimes different outliers in the min. 1D model compared with the Wadati method. Since the later only detects T -T outliers, we can not distinguish S P between P or S mispick and have no information, if we observe only one of the two phases. 10 12 14 16 18 20 22 24 26 28 30 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 BNALP-T MUO-T HASLI-T LLS-T ZUR-T WILA-T CHMEL-T VDL-T DAVON-T BOURR-T TORNY-T T (s) p (T -T ) - (1.71-1)*(T -T ) (s) s p p 1 Sg Pg ? ? ? ? Beckenried event transverse, displacement 21 22 23 24 25 26 27 28 29 30 31 -6 -4 -2 0 2 4 6 8 10 12 14 CHE-T BALST-T KAMOR-T LKBD-T SIERE-T DAVON-T MMK-T T (s) p (T -T ) - (1.71-1)*(T -T ) (s) s p p 1 SmS PmP ? ? ? ? ? transverse, displacement
Transcript of T p d e Ê 0 LKBD-T CHMEL-T p a e a o v a c d ls a f m i l t e t Ê g y a...
Structural models of the lithosphere based on P-wave velocities only lead to large ambiguities in terms of composition and physical parameters (e.g. fluid content, temperature). Additional information about S-wave velocities can put important constraints on composition and the physical state of the lithosphere, and improve the accuracy of the earthquake location. Since S-waves of local earthquakes are often contaminated by P-wave coda and S-P phase conversions, correct identification and timing of S-wave arrivals are difficult and sometimes ambiguous. To check the consistency of the picked S-wave arrivals obtained from the routine analysis of local earthquake data at the Swiss Seismological Service (SED) between 1999 and 2004, we apply the concept of minimum 1D models to P and S arrivals (independently and combined). In case of consistently picked S-waves we should observe only small deviations in hypocenters and residuals of relocated earthquakes between each model. Finally ,we compare the detected outliers with those derived from Wadati inversion, which is commonly used to assess the quality of S-arrivals.
Assessment of Quality and Consistency of S-Wave Arrivals in Local Earthquake Data
12
1,21
T. Diehl, N. Deichmann, S. Husen and E. Kissling1Institute of Geophysics, ETH Zurich, Switzerland 2Swiss Seismological Service, ETH Zurich, Switzerland
OverviewIntroduction
Minimum 1D P
Minimum 1D S
Minimum 1D P+S5°E
6°E7°E
8°E9°E
10°E11°E
46°N 47°N 48°N 49°N02040Depth (km)
56
78
910
11Longitude (deg)
46 47 48 49
Latitude (deg)
020
40Depth (km)
0 50 100 150 200
No. of Events0
2040
Depth (km)
Hypocenters & Residuals
Conclusions
Wadati
-15000-10000
-5000 0 50001000015000
Depth Shift (m)
050100150200250300350400450
Eventsigma (m): 1971
mean (m): 315-15000-10000
-5000 0 50001000015000
Latitude Shift (m)
050100150200250300350400450
Eventsigma (m): 395
mean (m): 56-15000-10000
-5000 0 50001000015000
Longitude Shift (m)
050100150200250300350400450
Eventsigma (m): 477
mean (m): 23
-15000-10000
-5000 0 50001000015000
Depth Shift (m)
050100150200250300350400450
Eventsigma (m): 1815
mean (m): 1015-15000-10000
-5000 0 50001000015000
Latitude Shift (m)
050100150200250300350400450
sigma (m): 348mean (m): 80
-15000-10000
-5000 0 50001000015000
Longitude Shift (m)
050100150200250300350400450
Eventsigma (m): 444
mean (m): -86
Event
-20 -10 0 10 20Depth Difference (km)
050
100150
200Event
sigma (m): 3669mean (m): 883
-20 -10 0 10 20Latitude Difference (km)
050
100150
200Event
sigma (m): 1491mean (m): -422
-20 -10 0 10 20Longitude Difference (km)
050
100150
200Event
sigma (m): 1433mean (m): 760
-3 -2 -1 0 1 2 3Origin Difference (s)
050
100150
200Event
sigma (s): 0.34mean (s): -0.28
Residuals in Minimum 1D Models
0 500 1000 1500 2000 2500 3000
Number of observations
-2-1
01
2Residual (s)
0 500 1000 1500 2000 2500 3000
Number of observations
-2-1
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2Residual (s)
0 200 400 600 800Number of observations
-2-1
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2Residual (s)
0 200 400 600 800Number of observations
-2-1
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2Residual (s)
0 10 20 30 40 50 60 70 80Number of observations
-2-1
01
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0 10 20 30 40 50 60 70 80Number of observations
-2-1
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2Residual (s)
0 200 400 600 800 1000 1200 1400
Number of observations
-2-1
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2Residual (s)
0 200 400 600 800 1000 1200 1400
Number of observations
-2-1
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2Residual (s)
0 100 200 300 400 500Number of observations
-2-1
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2Residual (s)
0 100 200 300 400 500Number of observations
-2-1
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2Residual (s)
0 10 20 30 40 50Number of observations
-2-1
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2Residual (s)
0 10 20 30 40 50Number of observations
-2-1
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2Residual (s)
Obs. Weight: 0
Obs. Weight: 1
Obs. Weight: 0
Obs. Weight: 1
Obs. Weight: 3Obs. Weight: 3
P + S
Preselection from P+S model (492 events)No preselection criteria (1588 events)
0 200 400 600 800 1000
Number of observations
-2-1
01
2Residual (s)
0 100 200 300 400 500Number of observations
-2-1
01
2Residual (s)
0 10 20 30 40 50Number of observations
-2-1
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2Residual (s)
0 200 400 600 800 1000
Number of observations
-2-1
01
2Residual (s)
0 100 200 300 400 500Number of observations
-2-1
01
2Residual (s)
0 10 20 30 40 50Number of observations
-2-1
01
2Residual (s)
S
Events common in P and S data (238 events)
HypocentersP
email contact: [email protected] wish to thank INGV, LED, RENASS, RSNI, SISMALP and ZAMG for providing us with additional data. Acknowledgements
5°E6°E
7°E8°E
9°E10°E
11°E
46°N 47°N 48°N 49°N02040Depth (km)
56
78
910
11Longitude (deg)
46 47 48 49
Latitude (deg)
020
40Depth (km)
0 50 100 150 200
No. of Events
020
40Depth (km)
5°E6°E
7°E8°E
9°E10°E
11°E
46°N 47°N 48°N 49°N
Station Corrections (S-Wave)
5°E6°E
7°E8°E
9°E10°E
11°E
46°N 47°N 48°N 49°N02040Depth (km)
56
78
910
11Longitude (deg)
46 47 48 49
Latitude (deg)
020
40Depth (km)
0 50 100 150 200
No. of Events
020
40Depth (km)
5°E6°E
7°E8°E
9°E10°E
11°E
46°N 47°N 48°N 49°N
Station Corrections (P-Wave)
Statistics
050
km
LKBD
BALST
BNALP
BOURRCHKAMDAVON
HASLILLS
MMK
MUO
SIERE
SLE
CHE
TORNY
VDL
WILAZUR
KAMOR
6°E8°E
10°E
47°N46°N 48°N
FranceAustria
Italy
Germany
Switzerland
Beckenried earthquake:2000/08/17 07:14Z = 10 km, M = 3.0
Statistics
Statistics
Stability Tests for Min. 1D P+SBeckenried event
Example waveforms of the Beckenried earthquake illustrate the difficulties in phase identification and picking of local S-wave data. The seismograms are plotted as time reduced Wadati diagrams.Stereographic diagrams for 3 Swiss stations demonstrate the distribution of azimuth, distance and focal depth for observed P and S arrivals. SLE shows prominent differences between P and S for larger epicentral distances.
0 02
40
600 8100 1 20
140 160
180 200
20 2
240 260 280 300 203 43 0 P
0 02 40 6080 001 12 0
140 1 0 6
180 200
20 2
20 4 260 280 030 203 43 0 S
010
2030
Focal Depth (km)
0 30 60 90 120# obs.
010203040
Focal Depth (km)
0 30 60 90 120# obs.
0100
200Epicentral Distance (km)
0 02 40 6 080 100 120
140 1 0 6
180 20 0
20 2
240 620 280 3 00 320 340 P
0 20 40 60 80 100 120
140 160
180 020
20 2
240 620 028 300 320 3 04 S
010
2030
Focal Depth (km)
0 30 60# obs.
010203040
Focal Depth (km)
0 30 60# obs.
0100
200Epicentral Distance (km)
0 20 40 60 80 100 1 0 2
140 61 0
180 00 2
220
240 260 2 80 30 0 320 3 04 P
0 20
04 6 080 100 1 0 2
40 16 1 0
180 020
220
0 24 26 0 28 0 03 0 3 0 23 04
S
010
2030
Focal Depth (km)
0 30 60 90 120 150 180# obs.
010203040
Focal Depth (km)
0 30 60 90 120 150 180# obs.
0100
200Epicentral Distance (km)
PS
Distance (km)Azimuth (°)
A)
A) Selected data set to compute min. 1D P-wave model. Black dots: events common in P, S, P+S.B) Minimum 1D P-wave model, and final models from “high-low” test.C) Station corrections P-wave
C)A) Selected data set to compute min. 1D S-wave model. Black dots: events common in P, S, P+S.B) Minimum 1D S-wave model, and final models from “high-low” test.C) Station corrections S-wave
C)
A)
Random MislocationSystematicMislocation
A)A) Selected data set to compute min. 1D P+S-wave model. Black dots: events common in P, S, P+S.B) Station corrections for P- and S-waves. C) Final minimum 1D model for P (blue, solid), S (red, solid) and vp/vs (black, solid). The initial models (independent models) are represented by the grey, dashed lines.
D) To test the robustness of the final minimum model, we shift the hypocenters randomly (lat,lon,depth) and systematically (depth) by 10 km before introducing them in the joint inversion (grey dots). Velocities are overdamped for test with systematically shifted hypocenters.Hypocenters are relocated close to their original locations indicating a stable minimum (final hypocenters: black dots, final velocity model for random shift: solid green in C).
A) Differences in hypocenter location obtained from relocation in min. 1D P-wave model (using only P arrivals) and S-wave model (using only S arrivals).B) Residuals of earthquakes relocated with P-arrivals only (in min. 1D P).C) Residuals of earthquakes relocated with S-arrivals only (in min. 1D S).D) Residuals of earthquakes relocated using P and S arrivals in min. 1D P+S wave model (preselected “high quality” data set from min. 1D P+S).E) Same as D) for unfiltered data set of 1588 earthquakes.
0102030405060 Depth (km)
45
67
89
v (km/s)P
Initial: high/low Output (low-in) Output (high-in) Min 1D P-model
well resolved
B)0102030405060 Depth (km)
23
45
v (km/s)S
Initial: high/low Output (low-in) Output (high-in) Min 1D S-model
well resolved
B)
D)
5°E6°E
7°E8°E
9°E10°E
11°E
46°N 47°N 48°N 49°NP DelaysS Delays
B)P RayS RayStationHypocentre
P RayStationHypocentre
S RayStationHypocentre
0102030405060 Depth (km)
23
45
67
89
Velocity (km/s)0102030405060
1.61.71.81.9v/vRatio
PS
vSvP
1.73
well resolved
Final min 1DP,S in (indi.)Final stabiltytest
C)
A)B)
C)
D)E)
A) Simultaneous Wadati inversion for the preselected P+S data set of 492 earthquakes. We define outliers (white dots) as residuals ³ 1 s or £ -1 s.
0 5 10 15 20 25T = T - T (s)S-P s p010
2030
4050
6070
T (s)P Vp/Vs = 1.699, +/- 0.001rms = 0.422
T/T coupleP
S-POutlier (residual ³ 1 s or £ -1 s) vp/vs -1
C)
A)Outliers:Wadati: 56 (S-P)Min. 1D P+S: 88 (P) + 37 (S)
We derive minimum 1D models for P, S, and P+S. Within these models hypocenters and residuals of the relocated earthquakes show similar results for P, S, and P+S. Mean values and s of equal quality classes show no significant bias between P and S arrivals. Therefore we assume a comparable consistency of P and S arrivals in our data set. The amount of high-quality S arrivals is lower than for P. This can be explained by a higher conservatism in S arrival picking. In general we detect more and sometimes different outliers in the min. 1D model compared with the Wadati method. Since the later only detects T-T outliers, we can not distinguish
SP
between P or S mispick and have no information, if we observe only one of the two phases.
1012
1416
1820
2224
2628
30-14 -12 -10 -8 -6 -4 -2 0 2 4 6
BNALP-T MUO-T
HASLI-T
LLS-T ZUR-T
WILA-T CHMEL-T
VDL-T
DAVON-T BOURR-T
TORNY-T
T (s)p
(T -T ) - (1.71-1)*(T -T ) (s)s p p 1
SgPg
???
?
Beckenried eventtransverse, displacement
2122
2324
2526
2728
2930
31-6 -4 -2 0 2 4 6 8 10 12 14
CHE-T BALST-T
KAMOR-T
LKBD-T
SIERE-T
DAVON-T
MMK-T
T (s)p
(T -T ) - (1.71-1)*(T -T ) (s)s p p 1
SmS
PmP
??
???
transverse, displacement
Poster presented at EGU 2005, Vienna, Austria
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