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Usage of joint rating functions for seismic phase association
and event location
Asming VE Prokudina AV Nakhshina LP
Kola Regional Seismological CentreRussia
MAIN IDEAWhen a seismic phase is detected a set of azimuth-dependent rating functions for the phase can be computed
a) Polarization functions
PP(α) ndash estimation of hypothesis that the phase is P coming from αPS(α) ndash estimation of hypothesis that the phase is S coming from α
b) Beamforming (sum of shifted array channels)
B(αV) ndash estimation of hypothesis that the phase is coming from α with velocity V
The functions are usually used to determine phase types
We propose to use combinations of the function for several phases simultaneously to check hypothesis that the phases are of the same seismic event
The approach can be useful for single stations (3C and arrays) as well as for sparse networks
Joint analysis of couples of phases detected at the same station
The idea Detect some phases (say using STALTA) For each couple of phases check a hypothesis that the 1st one is P and the 2nd one is S from the same event
If the estimation is greater than a threshold then locate the event by the backazimuth and S-P time difference
ImplementationJoint polarization analysisJoint beamforming
Usage some penalties and Bayesian belief networks to take into account recording (envelope) shapes
Joint polarization analysis
P S
Red line R(α) is normalized horizontal motion Blue line Cz(α) is correlation between horizontal and vertical motion
Estimations of phase kinds PP()=(1 + R())(1+CZ())4
PS()=(1 + R(+90))(1-CZ(+90))2
(0360)( ) max ( )AB PASBR Penalty A B P
Estimation of the hypothesis that phase A is P and B is S from the same event
where Penalty is some functional dependent on the phases
( ) ( ) ( ) (1 ( ) )PASB PA SB PBP P P P
Joint estimation for backazimuth α
Joint beamforming for an array
Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors
PS
ΔTpΔTs = ΔTp middot(VpVs) upper layer
The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
MAIN IDEAWhen a seismic phase is detected a set of azimuth-dependent rating functions for the phase can be computed
a) Polarization functions
PP(α) ndash estimation of hypothesis that the phase is P coming from αPS(α) ndash estimation of hypothesis that the phase is S coming from α
b) Beamforming (sum of shifted array channels)
B(αV) ndash estimation of hypothesis that the phase is coming from α with velocity V
The functions are usually used to determine phase types
We propose to use combinations of the function for several phases simultaneously to check hypothesis that the phases are of the same seismic event
The approach can be useful for single stations (3C and arrays) as well as for sparse networks
Joint analysis of couples of phases detected at the same station
The idea Detect some phases (say using STALTA) For each couple of phases check a hypothesis that the 1st one is P and the 2nd one is S from the same event
If the estimation is greater than a threshold then locate the event by the backazimuth and S-P time difference
ImplementationJoint polarization analysisJoint beamforming
Usage some penalties and Bayesian belief networks to take into account recording (envelope) shapes
Joint polarization analysis
P S
Red line R(α) is normalized horizontal motion Blue line Cz(α) is correlation between horizontal and vertical motion
Estimations of phase kinds PP()=(1 + R())(1+CZ())4
PS()=(1 + R(+90))(1-CZ(+90))2
(0360)( ) max ( )AB PASBR Penalty A B P
Estimation of the hypothesis that phase A is P and B is S from the same event
where Penalty is some functional dependent on the phases
( ) ( ) ( ) (1 ( ) )PASB PA SB PBP P P P
Joint estimation for backazimuth α
Joint beamforming for an array
Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors
PS
ΔTpΔTs = ΔTp middot(VpVs) upper layer
The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Joint analysis of couples of phases detected at the same station
The idea Detect some phases (say using STALTA) For each couple of phases check a hypothesis that the 1st one is P and the 2nd one is S from the same event
If the estimation is greater than a threshold then locate the event by the backazimuth and S-P time difference
ImplementationJoint polarization analysisJoint beamforming
Usage some penalties and Bayesian belief networks to take into account recording (envelope) shapes
Joint polarization analysis
P S
Red line R(α) is normalized horizontal motion Blue line Cz(α) is correlation between horizontal and vertical motion
Estimations of phase kinds PP()=(1 + R())(1+CZ())4
PS()=(1 + R(+90))(1-CZ(+90))2
(0360)( ) max ( )AB PASBR Penalty A B P
Estimation of the hypothesis that phase A is P and B is S from the same event
where Penalty is some functional dependent on the phases
( ) ( ) ( ) (1 ( ) )PASB PA SB PBP P P P
Joint estimation for backazimuth α
Joint beamforming for an array
Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors
PS
ΔTpΔTs = ΔTp middot(VpVs) upper layer
The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Joint polarization analysis
P S
Red line R(α) is normalized horizontal motion Blue line Cz(α) is correlation between horizontal and vertical motion
Estimations of phase kinds PP()=(1 + R())(1+CZ())4
PS()=(1 + R(+90))(1-CZ(+90))2
(0360)( ) max ( )AB PASBR Penalty A B P
Estimation of the hypothesis that phase A is P and B is S from the same event
where Penalty is some functional dependent on the phases
( ) ( ) ( ) (1 ( ) )PASB PA SB PBP P P P
Joint estimation for backazimuth α
Joint beamforming for an array
Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors
PS
ΔTpΔTs = ΔTp middot(VpVs) upper layer
The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Joint beamforming for an array
Checking the hypothesis that the 1st phase is P and the 2nd one is S from the same event by an array sensors
PS
ΔTpΔTs = ΔTp middot(VpVs) upper layer
The idea is to make beamforming simultaneously for P and S fragments and use related time delays for P and S
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
1 2 1 2
1 2 1 2
( ) ( )
( ) ( )
max ( ( ( )) max ( ( ( ))( )
max ( ) max ( )A A B B
A A B B
i i p p upper i i P S uppert t t t t t
i ip
i it t t t t t
i i
Z t t V V Z t t V R VJB V
Z t Z t
Joint beamforming for an array
The estimation of the hypothesis that the 1st phase (A) is P and the second one (B) is S from the same event coming from the backazimuth α with apparent P velocity Vp is
Where (tA1tA2) is time interval for P candidate (tB1tB2) is for S candidateZi(t) samples of i-th sensor
Δti(αVVupper) is time difference between arrivals of a wave coming from backazimuth α with velocity V to the array centre and to i-th sensor It depends also on upper layer velocity Vupper if we take into account sensors elevations
R=Vp upperVs upper P and S velocities ratio under the array
If the array includes a 3-component sensor we can multiply JB(αVp) by the polarization estimation PPASB(α) The final estimation is
Rating(αVp)=PPASB(α)JB(αVp)
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
For the algorithm it is important to know Vp and VpVs under an array
We have selected several events picked manually P and S phases and computed their compatibility estimations for different variants of Vp upper and (VpVs) upper
( )
( ) max ( ( ) )P
P upper p s upper P P upper P S upperV
C V V V JB V V V V
Storfjorden events Maximum atVp=55 kmsec and VpVs=18
SPI array Spitsbergen
Events from North-WestMaximum is not realistic VpVs~1
SPI array Spitsbergen
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Joint analysis of couples can screen out non-realistic candidates to be phases
P candidate
Centreof envelope
S candidate
Penalties dependent on envelope shape and position of phases inside envelope can be implemented to screen out non-realistic couples
In this case the following penalties are used to decrease the rating
bull Due to presence of a phase before the P candidatebull Due to large time difference between the envelope centre and the S candidatebull Due to huge amplitude ratio of the P and S candidates
Such penalties andor rating functions can be combined in a more strict manner using probabilistic approach based on Bayesian belief networks
Dist to the centreP ampl
S ampl
Phase before P
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
An example of Bayesian belief network analyzing couples of phases
Linear polarization of 1st phase
YesNo
V apparent 1st phasegtVpMinYesNo
Is the 1st phase P-wave YesNo
Is compatible phase before the 1st
YesNo
Is this P-wave of event beginning
YesNo
1st and 2nd phases are compatible by polarization
YesNo
1st and 2nd phases are compatible by joint beamforming
YesNo
Phases are connected in envelope
YesNo
The couple is PS of the same event
YesNo
Final decision
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Usage of the joint beamforming algorithm for Storfjorden seismicity monitoring(UDL program)
bull STA-LTA detector for generalized envelopesbull P and S association in time interval 9-15 sec (to avoid false associations for different events in the same area)
GBF
UDL
Manual analysis has shown that at least 95 of the events are trueLinear slope of the frequency-magnitude curve for Mgt-02 leads us to conclusion that we detect the most part of the events with Mgt-02
SPI data had been processed since 2008
Storfjorden is a seismically active zone at distances 100-150 km from SPI array
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Near real-time Storfjorden monitoring httpwwwkrscrustorfjorden(since 12012011)
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Joint beamforming by a couple of arrays (idea)
Location of Zapolyarny explosion by two arrays AP0 (Apatity) and ARC (Norway)When P onsets are picked the line can be drawn on which the event occurred (P-P)Distances to APA and ARC are about the same (205-210 km) so we can expect apparent velocities at the arrays to be the same
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Example of joint beamforming by couple of phases S(AP0) and S(ARC) for Zapolyarny explosion
X axis length (L) along S-S lineY axis apparent velocity (kmsec)
(the same for both arrays)Red points uncertainty area (Bgt099 Bmax)
Maximizing function 00( ) ( ( )) ( ( ))ARC APTot ARC APV l V l V lB B B
Where l is length along S-S line V is apparent velocity α is backazimuth to an array dependent on lB is amplitude of an array beam (sum of time-shifted channels)
In more complicated cases V apparent can be calculated using travel-time model
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Perspectives of joint data processing
The European Arctic is area with a very sparse seismic network But huge amount of seismic events with complicated shapes of wave forms occur here Every known algorithm of detection and location makes a lot of false alarms It is difficult to obtain a realistic picture of local seismicity
We suppose to make a new data processing system based on the following principles
bull A collection of algorithms where each one estimates probability of some fact (for example that a part of recording contains an onset that an onset is P wave that a part of envelope corresponds to a seismic event etc) The algorithms could be very different (ordinary STALTA envelope-based neural networks etc) but operate with standard data types
bull A system that calls the algorithms according to absence or presence of some data types (3C data array data envelopes etc)
bull A Bayesian belief network that makes a final decision about processed data sets based on results of work of the algorithms (does a data set contain a seismic event or not are onsets found what are the waves etc)
Thank You for attention
Thank You for attention
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