Body wave extraction and tomography at Long Beach, California, with ambient-noise interferometry

We retrieve P diving waves by applying seismic interferometry to ambient‐noise records observed at Long Beach, California, and invert travel times of these waves to estimate 3‐D P wave velocity structure. The ambient noise is recorded by a 2‐D dense and large network, which has about 2500 receivers...

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Veröffentlicht in:Journal of geophysical research. Solid earth 2015-02, Vol.120 (2), p.1159-1173
Hauptverfasser: Nakata, Nori, Chang, Jason P., Lawrence, Jesse F., Boué, Pierre
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Chang, Jason P.
Lawrence, Jesse F.
Boué, Pierre
description We retrieve P diving waves by applying seismic interferometry to ambient‐noise records observed at Long Beach, California, and invert travel times of these waves to estimate 3‐D P wave velocity structure. The ambient noise is recorded by a 2‐D dense and large network, which has about 2500 receivers with 100 m spacing. Compared to surface wave extraction, body wave extraction is a much greater challenge because ambient noise is typically dominated by surface wave energy. For each individual receiver pair, the cross‐correlation function obtained from ambient‐noise data does not show clear body waves. Although we can reconstruct body waves when we stack correlation functions over all receiver pairs, we need to extract body waves at each receiver pair separately for imaging spatial heterogeneity of subsurface structure. Therefore, we employ two filters after correlation to seek body waves between individual receiver pairs. The first filter is a selection filter based on the similarity between each correlation function and the stacked function. After selecting traces containing stronger body waves, we retain about two million correlation functions (35% of all correlation functions) and successfully preserve most of body wave energy in the retained traces. The second filter is a noise suppression filter to enhance coherent energy (body waves here) and suppress incoherent noise in each trace. After applying these filters, we can reconstruct clear body waves from each virtual source. As an application of using extracted body waves, we estimate 3‐D P wave velocities from these waves with travel time tomography. This study is the first body wave tomography result obtained from only ambient noise recorded at the ground surface. The velocity structure estimated from body waves has higher resolution than estimated from surface waves. Key Points We extract body waves from ambient noise recorded by a dense network Two signal‐processing filters are applied to retrieve clear body waves We use the extracted body waves to estimate 3‐D P wave velocities
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The ambient noise is recorded by a 2‐D dense and large network, which has about 2500 receivers with 100 m spacing. Compared to surface wave extraction, body wave extraction is a much greater challenge because ambient noise is typically dominated by surface wave energy. For each individual receiver pair, the cross‐correlation function obtained from ambient‐noise data does not show clear body waves. Although we can reconstruct body waves when we stack correlation functions over all receiver pairs, we need to extract body waves at each receiver pair separately for imaging spatial heterogeneity of subsurface structure. Therefore, we employ two filters after correlation to seek body waves between individual receiver pairs. The first filter is a selection filter based on the similarity between each correlation function and the stacked function. After selecting traces containing stronger body waves, we retain about two million correlation functions (35% of all correlation functions) and successfully preserve most of body wave energy in the retained traces. The second filter is a noise suppression filter to enhance coherent energy (body waves here) and suppress incoherent noise in each trace. After applying these filters, we can reconstruct clear body waves from each virtual source. As an application of using extracted body waves, we estimate 3‐D P wave velocities from these waves with travel time tomography. This study is the first body wave tomography result obtained from only ambient noise recorded at the ground surface. The velocity structure estimated from body waves has higher resolution than estimated from surface waves. Key Points We extract body waves from ambient noise recorded by a dense network Two signal‐processing filters are applied to retrieve clear body waves We use the extracted body waves to estimate 3‐D P wave velocities</description><identifier>ISSN: 2169-9313</identifier><identifier>EISSN: 2169-9356</identifier><identifier>DOI: 10.1002/2015JB011870</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Ambient noise ; Beaches ; body wave ; Body waves ; Correlation ; Correlation analysis ; Cross correlation ; Data processing ; Diving ; Elastic waves ; Electromagnetic wave filters ; Energy ; Estimates ; Extraction ; Filters ; Geophysics ; Heterogeneity ; Imaging ; Imaging techniques ; Information processing ; Interferometry ; Long Beach ; Noise ; Noise prediction ; Noise reduction ; P wave ; P waves ; Patchiness ; Receivers ; Records ; seismic interferometry ; Seismic velocities ; Seismic wave velocities ; Signal processing ; Similarity ; Spatial heterogeneity ; Surface water waves ; Surface waves ; Three dimensional ; Three dimensional bodies ; Tomography ; Travel ; Travel time ; Velocity ; Wave energy ; Wave power ; Wave velocity</subject><ispartof>Journal of geophysical research. 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Solid earth</title><addtitle>J. Geophys. Res. Solid Earth</addtitle><description>We retrieve P diving waves by applying seismic interferometry to ambient‐noise records observed at Long Beach, California, and invert travel times of these waves to estimate 3‐D P wave velocity structure. The ambient noise is recorded by a 2‐D dense and large network, which has about 2500 receivers with 100 m spacing. Compared to surface wave extraction, body wave extraction is a much greater challenge because ambient noise is typically dominated by surface wave energy. For each individual receiver pair, the cross‐correlation function obtained from ambient‐noise data does not show clear body waves. Although we can reconstruct body waves when we stack correlation functions over all receiver pairs, we need to extract body waves at each receiver pair separately for imaging spatial heterogeneity of subsurface structure. Therefore, we employ two filters after correlation to seek body waves between individual receiver pairs. The first filter is a selection filter based on the similarity between each correlation function and the stacked function. After selecting traces containing stronger body waves, we retain about two million correlation functions (35% of all correlation functions) and successfully preserve most of body wave energy in the retained traces. The second filter is a noise suppression filter to enhance coherent energy (body waves here) and suppress incoherent noise in each trace. After applying these filters, we can reconstruct clear body waves from each virtual source. As an application of using extracted body waves, we estimate 3‐D P wave velocities from these waves with travel time tomography. This study is the first body wave tomography result obtained from only ambient noise recorded at the ground surface. The velocity structure estimated from body waves has higher resolution than estimated from surface waves. Key Points We extract body waves from ambient noise recorded by a dense network Two signal‐processing filters are applied to retrieve clear body waves We use the extracted body waves to estimate 3‐D P wave velocities</description><subject>Ambient noise</subject><subject>Beaches</subject><subject>body wave</subject><subject>Body waves</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Cross correlation</subject><subject>Data processing</subject><subject>Diving</subject><subject>Elastic waves</subject><subject>Electromagnetic wave filters</subject><subject>Energy</subject><subject>Estimates</subject><subject>Extraction</subject><subject>Filters</subject><subject>Geophysics</subject><subject>Heterogeneity</subject><subject>Imaging</subject><subject>Imaging techniques</subject><subject>Information processing</subject><subject>Interferometry</subject><subject>Long Beach</subject><subject>Noise</subject><subject>Noise prediction</subject><subject>Noise reduction</subject><subject>P wave</subject><subject>P waves</subject><subject>Patchiness</subject><subject>Receivers</subject><subject>Records</subject><subject>seismic interferometry</subject><subject>Seismic velocities</subject><subject>Seismic wave velocities</subject><subject>Signal processing</subject><subject>Similarity</subject><subject>Spatial heterogeneity</subject><subject>Surface water waves</subject><subject>Surface waves</subject><subject>Three dimensional</subject><subject>Three dimensional bodies</subject><subject>Tomography</subject><subject>Travel</subject><subject>Travel time</subject><subject>Velocity</subject><subject>Wave energy</subject><subject>Wave power</subject><subject>Wave velocity</subject><issn>2169-9313</issn><issn>2169-9356</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kMFO3DAQhiPUSiDg1gew1EsPm-Jx4tg5siu6Ld22EqLq0TXJmDUk9tb2dsnb12gRQhw6lxmNvn_mnymKd0A_AqXsjFHgl3MKIAU9KI4YNG3ZVrx581xDdVicxnhHc8jcgvqo-D33_UR2-i8SfEhBd8l6R7TrSfKjvw16s56ITmTl3S2Zo-7WM7LQgzU-OKtnZGfTmujxxqJLpfM2IrEuYTAY_IgpTCfFW6OHiKdP-bj4-enievG5XP1Yflmcr0pdC6hL1ksQPdy0PWWS0l4bw2rG6rpGzg3nvWi4lgxkRxE4tNxQTo1p8uXZVGeq4-LDfu4m-D9bjEmNNnY4DNqh30YFjRCt4K1sMvr-FXrnt8FldwpaAKgyVWdqtqe64GMMaNQm2FGHSQFVjx9XLz-e8WqP7-yA039Zdbm8mnOg8Lik3KtsTPjwrNLhXjWiElz9-r5U8ppdyVX1VX2r_gG4fY9c</recordid><startdate>201502</startdate><enddate>201502</enddate><creator>Nakata, Nori</creator><creator>Chang, Jason P.</creator><creator>Lawrence, Jesse F.</creator><creator>Boué, Pierre</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope></search><sort><creationdate>201502</creationdate><title>Body wave extraction and tomography at Long Beach, California, with ambient-noise interferometry</title><author>Nakata, Nori ; 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Solid earth</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nakata, Nori</au><au>Chang, Jason P.</au><au>Lawrence, Jesse F.</au><au>Boué, Pierre</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Body wave extraction and tomography at Long Beach, California, with ambient-noise interferometry</atitle><jtitle>Journal of geophysical research. Solid earth</jtitle><addtitle>J. Geophys. Res. Solid Earth</addtitle><date>2015-02</date><risdate>2015</risdate><volume>120</volume><issue>2</issue><spage>1159</spage><epage>1173</epage><pages>1159-1173</pages><issn>2169-9313</issn><eissn>2169-9356</eissn><abstract>We retrieve P diving waves by applying seismic interferometry to ambient‐noise records observed at Long Beach, California, and invert travel times of these waves to estimate 3‐D P wave velocity structure. The ambient noise is recorded by a 2‐D dense and large network, which has about 2500 receivers with 100 m spacing. Compared to surface wave extraction, body wave extraction is a much greater challenge because ambient noise is typically dominated by surface wave energy. For each individual receiver pair, the cross‐correlation function obtained from ambient‐noise data does not show clear body waves. Although we can reconstruct body waves when we stack correlation functions over all receiver pairs, we need to extract body waves at each receiver pair separately for imaging spatial heterogeneity of subsurface structure. Therefore, we employ two filters after correlation to seek body waves between individual receiver pairs. The first filter is a selection filter based on the similarity between each correlation function and the stacked function. After selecting traces containing stronger body waves, we retain about two million correlation functions (35% of all correlation functions) and successfully preserve most of body wave energy in the retained traces. The second filter is a noise suppression filter to enhance coherent energy (body waves here) and suppress incoherent noise in each trace. After applying these filters, we can reconstruct clear body waves from each virtual source. As an application of using extracted body waves, we estimate 3‐D P wave velocities from these waves with travel time tomography. This study is the first body wave tomography result obtained from only ambient noise recorded at the ground surface. The velocity structure estimated from body waves has higher resolution than estimated from surface waves. Key Points We extract body waves from ambient noise recorded by a dense network Two signal‐processing filters are applied to retrieve clear body waves We use the extracted body waves to estimate 3‐D P wave velocities</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2015JB011870</doi><tpages>15</tpages></addata></record>
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subjects Ambient noise
Beaches
body wave
Body waves
Correlation
Correlation analysis
Cross correlation
Data processing
Diving
Elastic waves
Electromagnetic wave filters
Energy
Estimates
Extraction
Filters
Geophysics
Heterogeneity
Imaging
Imaging techniques
Information processing
Interferometry
Long Beach
Noise
Noise prediction
Noise reduction
P wave
P waves
Patchiness
Receivers
Records
seismic interferometry
Seismic velocities
Seismic wave velocities
Signal processing
Similarity
Spatial heterogeneity
Surface water waves
Surface waves
Three dimensional
Three dimensional bodies
Tomography
Travel
Travel time
Velocity
Wave energy
Wave power
Wave velocity
title Body wave extraction and tomography at Long Beach, California, with ambient-noise interferometry
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