Post on 13-Mar-2020
Towards high-resolution seismic imaging and monitoring
Nori Nakata (MIT)
Microseismic wavefield imaging
Ambient noise imaging
Machine learning
Ambient noise imaging
Towards high-resolution seismic imaging and monitoring
using ambient noise, machine learning, and microseismic events
Nori Nakata (MIT)Yuji Kim, Jianhang Yin, Bin Lyu (Univ. Oklahoma)Yuwei Wang (Southwest Petroleum Univ)Rie Nakata Kamei, Zack Spica (Univ. Tokyo)Greg Beroza (Stanford)David Shelly (USGS)
Human-Induced Seismicity
Rubinstein and Babaie, 2015, SRL
• EOR• Geothermal• Mining
Fracking Production Wastewater disposal
Human-Induced SeismicityTasks• Assessing seismic hazard• Understanding seismic sources• Understanding physics of seismicity• Effectively enhancing production• Real-time application• …
Human-Induced Seismicity
source
path
site
Effects of Source + Path + Site
Tasks• Assessing seismic hazard• Understanding seismic sources• Understanding physics of seismicity• Effectively enhancing production• Real-time application
Human-Induced Seismicity
source
path
site
Tasks• Assessing seismic hazard• Understanding seismic sources• Understanding physics of seismicity• Effectively enhancing production• Real-time application
My approachUnique datasets
• Ambient noise• Microseismic events• Repeated active
surveys• Tube waves• …
Advanced techniques• Time-lapse FWI• Machine learning• Stochastic modeling• Wavefield migration• …
Human-Induced Seismicity
source
path
site
My approachUnique datasets
• Ambient noise• Microseismic events• Repeated active
surveys• Tube waves• …
Advanced techniques• Time-lapse FWI• Machine learning• Stochastic modeling• Wavefield migration• …
Tasks• Assessing seismic hazard• Understanding seismic sources• Understanding physics of seismicity• Effectively enhancing production• Real-time application
Human-Induced Seismicity
source
path
site
My approachUnique datasets
• Ambient noise• Microseismic events• Repeated active
surveys• Tube waves• …
Advanced techniques• Time-lapse FWI• Machine learning• Stochastic modeling• Wavefield migration• …
Tasks• Assessing seismic hazard• Understanding seismic sources• Understanding physics of seismicity• Effectively enhancing production• Real-time application
Arrival time is useful if clearly identified
Waldhauser and Ellsworth, BSSA, 2000
Arrival time picks are used to locate source.
Small, weak, and extended events arehard to detect/locate
Wave arrivals?
Migration-based processing
Kao and Shan, GJI, 2004
We can’t pick arrival times accurately
Recording microseismic data
Time-reversal imaging
Numerical example
Time-reversal imaging
Time-reversal imaging
Time-reversal imaging
Time-reversal imaging
Time-reversal imaging
Time-reversal imaging
focused!
Time-reversal imaging
strong artifacts
Time-reversal imaging
R(x, t)
Time
Time-reversal imaging
Time-reversal image (conventional method)
New approach (Geometric-mean RTM)
Nakata & Beroza, Geophysics, 2016
Time-reversal image (conventional method)
Geometric-mean RTM (new approach)
Nakata & Beroza, Geophysics, 2016
At time-lag = 0…,
Time-reversal imaging= Arithmetic-mean reverse-time imaging
Geometric-mean reverse-time migration
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Comparison
Time reversal GmRTM
GmRTM is efficient for :- locating with higher spatial resolution- using long-duration signals (fracking, tremors…)- enhancing weak signals (i.e., noisy signals)
Time reversal GmRTM
Field data example (Fracking)An oil field in Japan
• Only 8 receivers available• Challenging geometry
Wang, Nakata, et al., 2019
Field data example (Fracking)
Time reversal GmRTM
Wang, Nakata, et al., 2019
Velocity update with microseismic data
source
path
site
Lyu & Nakata, 2019
Velocity
Microseismic location
GmRTM Passive FWI
R(x) =X
t
Y
i
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J =1
2||dobss,i � dpres,i ||
22
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minimize…
Velocity update with microseismic data
Lyu & Nakata, 2019
Initial velocity model
Initial source locationsGmRTM FWI
Velocity update with microseismic data
Lyu & Nakata, 2019
Initial velocity model
Initial source locationsGmRTM FWI
Inverted velocity model
Inverted source locations
True velocity model
Velocity update with microseismic data
Lyu & Nakata, 2019
Initial velocity model
Initial source locationsGmRTM FWI
Inverted velocity model
Inverted source locations
True velocity model
FWI for source parameter estimation
5
10
15
Dep
th [k
m]
2030405060Distance from deformation front [km]
(b)
−1.5
−1.0
−0.5
0.0
0.5
1.0
1.5km/s
5
10
15
Dep
th [k
m]
2030405060Distance from deformation front [km]
Cost function
Kamei, Lumley, Nakata, 2018
Field data example (crosswell active survey)Vel model Source time func.Radiation
Structure imaging with small earthquakes
Nakata & Shelly, 2018, GRLMagma roof
1 station800 earthquakes
Ambient noise
source
path
site SEISMIC AMBIENT NOISE
E D I T E D B Y
Nori Nakata, Lucia Gualtieri and Andreas Fichtner
A book about Seismic Ambient Noise
Cambridge University Press
370 pp
Published on May 2, 2019
Velocity estimationNear-surface
S velocity 3D P velocity
Nakata et al., 2015
0.0
1.0
Dept
h (k
m)
0.0
3.0
2.0
1.0
4.0
Easting (km)
0.0
8.07.0
6.05.0
4.03.0
2.01.0
Northing (km)
0
10
5
-10
-5 P ve
locit
yflu
ctua
tion
(%)
High-frequency ground motion prediction
Time (s)0 2 4 6 8 10 12
Nor
mal
ized
pow
er
0
0.2
0.4
0.6
0.8
1 earthquake enveloperadiative transfer (P)radiative transfer (S)
Nakata & Beroza, 2015
8-16 Hz
source
path
site
Groningen Gas Field60
km
40 km
Spica, Nakata et al, 2018ab, SRL
• 415 stations• 3 components• Receiver spacing: 400 m• One-month continuous record
Groningen Gas Field60
km
40 km
Spica, Nakata et al, 2018ab, SRL
Near-surface velocity model estimated from• H/V• Rayleigh and Love waves
Groningen Gas FieldHourly seismic velocity change
60 k
m
40 km
Nakata, et al., in prep
Time-lapse FWI (CO2 monitoring)
−150 −100 −50 0 50DistDnce (m)
900
950
1000
1050
1100
1150
1200
1250
Dep
th (m
)
(a) 011 - 010
−150 −100 −50 0 50DistDnce (m)
(b) 012 - 010
−150 −100 −50 0 50DistDnce (m)
(c) 013 - 010
−150 −100 −50 0 50DistDnce (m)
(d) 014 - 010
−150 −100 −50 0 50DistDnce (m)
(e) 015 - 010
−150 −100 −50 0 50DistDnce (m)
(f) 016 - 010
−150 −100 −50 0 50DistDnce (m)
(e) 017 - 010
−0.5 0.0 0.5dVp (Nm/V)
Baseline
Nakata & Nakata, in prep
Receiver array
Source array
MN1 – Baseline(3000 t injected)
MN2 – Baseline(6000 t injected)
MN3 – Baseline(9000 t injected)
−150−100−500
50DistDnce (m
)
900 950 1000 1050 1100 1150 1200 1250Depth (m)
(a) 011 - 010 −150−100
−50050
DistDnce (m)
(b) 012 - 010 −150−100
−50050
DistDnce (m)
(c) 013 - 010 −150−100
−50050
DistDnce (m)
(d) 014 - 010 −150−100
−50050
DistDnce (m)
(e) 015 - 010 −150−100
−50050
DistDnce (m)(f) 016 - 010 −150−100
−50050
DistDnce (m)
(e) 017 - 010
−0.50.0
0.5dVp (Nm/V)
dVp(km
/s)
-0.5
0.5
Velocity change prediction by Machine learning
Dec 11 Dec 21 Dec 31 Jan 10 Jan 20-1
-0.5
0
0.5
1Ve
loci
ty c
hang
e (%
) observed
Nakata & Kim, in prep
Near-surface velocity changes over 40 days
Dec 11 Dec 21 Dec 31 Jan 10 Jan 20-1
-0.5
0
0.5
1Ve
loci
ty c
hang
e (%
) observedpredicted
Velocity change prediction by Machine learning
Nakata & Kim, in prep
Near-surface velocity changes over 40 days
Prediction using machine-learning regression analysisInput data: first 20 days & environmental parameters
Reflectivity inversion
Reflectivity inversion based on conventional deconvolution (least-squares inversion)
s(t) = h(t) ⇤ r(t)<latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit>
reflectivity
observed datasource wavelet
<latexit sha1_base64="7fCBr4wiH7EWOTWZxVzjcscUfuk=">AAAB7HicbZBNSwMxEIZn61etX1WPXoJFrJeyK4I9Frx4rOC2hXYp2TTbhmazSzIrlNLf4MWDIl79Qd78N6btHrT1hcDDOzNk5g1TKQy67rdT2Njc2t4p7pb29g8Oj8rHJy2TZJpxnyUy0Z2QGi6F4j4KlLyTak7jUPJ2OL6b19tPXBuRqEecpDyI6VCJSDCK1vL1ZRWv+uWKW3MXIuvg5VCBXM1++as3SFgWc4VMUmO6nptiMKUaBZN8VuplhqeUjemQdy0qGnMTTBfLzsiFdQYkSrR9CsnC/T0xpbExkzi0nTHFkVmtzc3/at0Mo3owFSrNkCu2/CjKJMGEzC8nA6E5QzmxQJkWdlfCRlRThjafkg3BWz15HVrXNc/yw02lUc/jKMIZnEMVPLiFBtxDE3xgIOAZXuHNUc6L8+58LFsLTj5zCn/kfP4A1oyOAA==</latexit>
L =1
2||s(t)� h(t) ⇤ r0(t)||22
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Kim & Nakata, 2018, TLE
Reflectivity inversion with Machine Learning
Reflectivity inversion based on conventional deconvolution (least-squares inversion)
s(t) = h(t) ⇤ r(t)<latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit><latexit sha1_base64="r0gNx3LatYCY6eJ4m5nQTDVrOKI=">AAAB+3icbVDLSgNBEOz1GeNrjUcvg0GIHsKuCHoRgl48RjAPSJYwO5kkQ2YfzPSKYcmvePGgiFd/xJt/42yyB00sGLqo6qZ7yo+l0Og439bK6tr6xmZhq7i9s7u3bx+UmjpKFOMNFslItX2quRQhb6BAydux4jTwJW/549vMbz1ypUUUPuAk5l5Ah6EYCEbRSD27pCt4Sq7JKCtnRJnSs8tO1ZmBLBM3J2XIUe/ZX91+xJKAh8gk1brjOjF6KVUomOTTYjfRPKZsTIe8Y2hIA669dHb7lJwYpU8GkTIvRDJTf0+kNNB6EvimM6A40oteJv7ndRIcXHmpCOMEecjmiwaJJBiRLAjSF4ozlBNDKFPC3ErYiCrK0MRVNCG4i19eJs3zqmv4/UW5dpPHUYAjOIYKuHAJNbiDOjSAwRM8wyu8WVPrxXq3PuatK1Y+cwh/YH3+ABsvkeI=</latexit>
observed datasource wavelet
<latexit sha1_base64="7fCBr4wiH7EWOTWZxVzjcscUfuk=">AAAB7HicbZBNSwMxEIZn61etX1WPXoJFrJeyK4I9Frx4rOC2hXYp2TTbhmazSzIrlNLf4MWDIl79Qd78N6btHrT1hcDDOzNk5g1TKQy67rdT2Njc2t4p7pb29g8Oj8rHJy2TZJpxnyUy0Z2QGi6F4j4KlLyTak7jUPJ2OL6b19tPXBuRqEecpDyI6VCJSDCK1vL1ZRWv+uWKW3MXIuvg5VCBXM1++as3SFgWc4VMUmO6nptiMKUaBZN8VuplhqeUjemQdy0qGnMTTBfLzsiFdQYkSrR9CsnC/T0xpbExkzi0nTHFkVmtzc3/at0Mo3owFSrNkCu2/CjKJMGEzC8nA6E5QzmxQJkWdlfCRlRThjafkg3BWz15HVrXNc/yw02lUc/jKMIZnEMVPLiFBtxDE3xgIOAZXuHNUc6L8+58LFsLTj5zCn/kfP4A1oyOAA==</latexit>
L =1
2||s(t)� h(t) ⇤ r0(t)||22
<latexit sha1_base64="6EL/lAblpR2bu8Il9voErtGIn0E=">AAACFHicbVC7TsMwFHXKq5RXgJHFokIUEFUSIdEFqRILA0ORaIvUhshxndaq85DtIFVpPoKFX2FhACFWBjb+BqfNAC1Hsnx8zr26vseNGBXSML61wsLi0vJKcbW0tr6xuaVv77REGHNMmjhkIb9zkSCMBqQpqWTkLuIE+S4jbXd4mfntB8IFDYNbOYqI7aN+QD2KkVSSo59cwwvY9TjCiZkmVgrHYygq8giewoG6jvlh9hiPHevecvSyUTUmgPPEzEkZ5Gg4+le3F+LYJ4HEDAnRMY1I2gnikmJG0lI3FiRCeIj6pKNogHwi7GSyVAoPlNKDXsjVCSScqL87EuQLMfJdVekjORCzXib+53Vi6dXshAZRLEmAp4O8mEEZwiwh2KOcYMlGiiDMqforxAOkEpIqx5IKwZxdeZ60rKqp+M1ZuV7L4yiCPbAPKsAE56AOrkADNAEGj+AZvII37Ul70d61j2lpQct7dsEfaJ8/VeCbLg==</latexit><latexit sha1_base64="6EL/lAblpR2bu8Il9voErtGIn0E=">AAACFHicbVC7TsMwFHXKq5RXgJHFokIUEFUSIdEFqRILA0ORaIvUhshxndaq85DtIFVpPoKFX2FhACFWBjb+BqfNAC1Hsnx8zr26vseNGBXSML61wsLi0vJKcbW0tr6xuaVv77REGHNMmjhkIb9zkSCMBqQpqWTkLuIE+S4jbXd4mfntB8IFDYNbOYqI7aN+QD2KkVSSo59cwwvY9TjCiZkmVgrHYygq8giewoG6jvlh9hiPHevecvSyUTUmgPPEzEkZ5Gg4+le3F+LYJ4HEDAnRMY1I2gnikmJG0lI3FiRCeIj6pKNogHwi7GSyVAoPlNKDXsjVCSScqL87EuQLMfJdVekjORCzXib+53Vi6dXshAZRLEmAp4O8mEEZwiwh2KOcYMlGiiDMqforxAOkEpIqx5IKwZxdeZ60rKqp+M1ZuV7L4yiCPbAPKsAE56AOrkADNAEGj+AZvII37Ul70d61j2lpQct7dsEfaJ8/VeCbLg==</latexit><latexit sha1_base64="6EL/lAblpR2bu8Il9voErtGIn0E=">AAACFHicbVC7TsMwFHXKq5RXgJHFokIUEFUSIdEFqRILA0ORaIvUhshxndaq85DtIFVpPoKFX2FhACFWBjb+BqfNAC1Hsnx8zr26vseNGBXSML61wsLi0vJKcbW0tr6xuaVv77REGHNMmjhkIb9zkSCMBqQpqWTkLuIE+S4jbXd4mfntB8IFDYNbOYqI7aN+QD2KkVSSo59cwwvY9TjCiZkmVgrHYygq8giewoG6jvlh9hiPHevecvSyUTUmgPPEzEkZ5Gg4+le3F+LYJ4HEDAnRMY1I2gnikmJG0lI3FiRCeIj6pKNogHwi7GSyVAoPlNKDXsjVCSScqL87EuQLMfJdVekjORCzXib+53Vi6dXshAZRLEmAp4O8mEEZwiwh2KOcYMlGiiDMqforxAOkEpIqx5IKwZxdeZ60rKqp+M1ZuV7L4yiCPbAPKsAE56AOrkADNAEGj+AZvII37Ul70d61j2lpQct7dsEfaJ8/VeCbLg==</latexit><latexit sha1_base64="6EL/lAblpR2bu8Il9voErtGIn0E=">AAACFHicbVC7TsMwFHXKq5RXgJHFokIUEFUSIdEFqRILA0ORaIvUhshxndaq85DtIFVpPoKFX2FhACFWBjb+BqfNAC1Hsnx8zr26vseNGBXSML61wsLi0vJKcbW0tr6xuaVv77REGHNMmjhkIb9zkSCMBqQpqWTkLuIE+S4jbXd4mfntB8IFDYNbOYqI7aN+QD2KkVSSo59cwwvY9TjCiZkmVgrHYygq8giewoG6jvlh9hiPHevecvSyUTUmgPPEzEkZ5Gg4+le3F+LYJ4HEDAnRMY1I2gnikmJG0lI3FiRCeIj6pKNogHwi7GSyVAoPlNKDXsjVCSScqL87EuQLMfJdVekjORCzXib+53Vi6dXshAZRLEmAp4O8mEEZwiwh2KOcYMlGiiDMqforxAOkEpIqx5IKwZxdeZ60rKqp+M1ZuV7L4yiCPbAPKsAE56AOrkADNAEGj+AZvII37Ul70d61j2lpQct7dsEfaJ8/VeCbLg==</latexit>
Kim & Nakata, 2018, TLE
L =1
2||rs(t)� h†
⇥(t) ⇤ ss(t)||22
<latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit>
Mapping operatorsynthetic data
Synthetic training set
Kim & Nakata, 2018, TLE
L =1
2||rs(t)� h†
⇥(t) ⇤ ss(t)||22
<latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">AAACKHicbZDJSgNBEIZ73I1b1KOXxiCooMwMgrmIghcPHiIYFTJx6OnUJE16FrprhDCZx/Hiq3gRUSRXn8TOcnAraPj4/yqq6w9SKTTa9sCamp6ZnZtfWCwtLa+srpXXN250kikOdZ7IRN0FTIMUMdRRoIS7VAGLAgm3Qfd86N8+gNIiia+xl0IzYu1YhIIzNJJfPr2kJ9QLFeO5U+RuQft9qny9i3v0gHbuc6/F2m1Qhe9ddwCZ0ff12O73fffe9csV+9AeFf0LzgQqZFI1v/zqtRKeRRAjl0zrhmOn2MyZQsElFCUv05Ay3mVtaBiMWQS6mY8OLeiOUVo0TJR5MdKR+n0iZ5HWvSgwnRHDjv7tDcX/vEaGYbWZizjNEGI+XhRmkmJCh6nRllDAUfYMMK6E+SvlHWZSQ5NtyYTg/D75L9y4h47hq6PKWXUSxwLZIttklzjkmJyRC1IjdcLJI3kmb+TderJerA9rMG6dsiYzm+RHWZ9fS4CkQA==</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">AAACKHicbZDJSgNBEIZ73I1b1KOXxiCooMwMgrmIghcPHiIYFTJx6OnUJE16FrprhDCZx/Hiq3gRUSRXn8TOcnAraPj4/yqq6w9SKTTa9sCamp6ZnZtfWCwtLa+srpXXN250kikOdZ7IRN0FTIMUMdRRoIS7VAGLAgm3Qfd86N8+gNIiia+xl0IzYu1YhIIzNJJfPr2kJ9QLFeO5U+RuQft9qny9i3v0gHbuc6/F2m1Qhe9ddwCZ0ff12O73fffe9csV+9AeFf0LzgQqZFI1v/zqtRKeRRAjl0zrhmOn2MyZQsElFCUv05Ay3mVtaBiMWQS6mY8OLeiOUVo0TJR5MdKR+n0iZ5HWvSgwnRHDjv7tDcX/vEaGYbWZizjNEGI+XhRmkmJCh6nRllDAUfYMMK6E+SvlHWZSQ5NtyYTg/D75L9y4h47hq6PKWXUSxwLZIttklzjkmJyRC1IjdcLJI3kmb+TderJerA9rMG6dsiYzm+RHWZ9fS4CkQA==</latexit><latexit sha1_base64="uaaUPypqo0nSupLg1TlHgiYe5XQ=">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</latexit>
Resolution test
Kim & Nakata, 2018, TLE
True model
Least squares
Input seismic data
Neural network
Field data example
Kim & Nakata, 2018, TLE
Least squaresInput seismic data Neural network
Studies for human-induced seismicity
source
path
site
TR
GmRTM
Source parameters of micro earthquakes
0.0
1.0
Dept
h (k
m)
0.0
3.0
2.0
1.0
4.0
Easting (km)
0.0
8.07.0
6.05.0
4.03.0
2.01.0
Northing (km)
0
10
5
-10
-5 P ve
locit
yflu
ctua
tion
(%)
3D/4D High-resolution structural imaging
Dec 11 Dec 21 Dec 31 Jan 10 Jan 20-1
-0.5
0
0.5
1
Velo
city
cha
nge
(%) observed
predicted
Near surface velocities and their dynamics
Publications:http://www.mit.edu/~nnakata
Time
Time
Ambient noise correlation
Processing
- crosscorrelation- spectral
whitening- time and/or space
averaging- normalization- etc…
= ambient-fieldcorrelationseismic interferometry2
Small EQs occur more frequently than large EQs
Gutenberg-Richter law (b value)
log10 N = a� bMData
Prediction
N
M
a
b
Amplitude
Ambient noiseObserved data
(snapshot)
Long Beach array (2012)
Data courtesy:Signal Hill Petroleum, NodalSeismic4 km
8 km
2500 receivers
source
path
site
Amplitude
Data mining to extract coherent wavesObserved data
(snapshot)
Amplitude
Amplitude
Rayleigh waves P waves
Correlation analysis
Nakata et al., 2015
Amplitude
Velocity estimationObserved data
(snapshot)
Near-surface S velocity 3D P velocity
Correlation analysis
Nakata et al., 2015
0.0
1.0
Dept
h (k
m)
0.0
3.0
2.0
1.0
4.0
Easting (km)
0.0
8.07.0
6.05.0
4.03.0
2.01.0
Northing (km)
0
10
5
-10
-5 P ve
locit
yflu
ctua
tion
(%)
High-frequency ground motion prediction
Time (s)0 2 4 6 8 10 12
Nor
mal
ized
pow
er
0
0.2
0.4
0.6
0.8
1 earthquake enveloperadiative transfer (P)radiative transfer (S)
Nakata & Beroza, 2015
8-16 Hz
source
path
site
Groningen Gas Field60
km
40 km
Spica, Nakata et al, 2018ab, SRL
• 415 stations• 3 components• Receiver spacing: 400 m• One-month continuous record
Groningen Gas Field60
km
40 km
Spica, Nakata et al, 2018ab, SRL
Near-surface velocity model estimated from• H/V• Rayleigh and Love waves
Groningen Gas FieldHourly seismic velocity change
60 k
m
40 km
Nakata, et al., in prep