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4D Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry
This study introduces a technique for four‐dimensional pore pressure monitoring using passive image interferometry. Surface‐wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels...
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Published in: | Geophysical research letters 2023-03, Vol.50 (5), p.n/a |
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description | This study introduces a technique for four‐dimensional pore pressure monitoring using passive image interferometry. Surface‐wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels can be used to invert time‐lapse seismic velocity changes, retrieved with passive image interferometry, for hydrological pore pressure variations as a function of time, depth, and region. This new approach is applied in the Groningen region of the Netherlands. We show good recovery of pore pressure variations in the upper 200 m of the subsurface from passive seismic velocity observations. This depth range is primarily limited by the reliable frequency range of the seismic data.
Plain Language Summary
In this study, we develop a method for pore pressure monitoring using seismic ambient noise. We use passive image interferometry to estimate surface‐wave velocity changes as a function of frequency, and compute for surface‐wave velocities the sensitivity to pore pressure changes as a function of depth. These so‐called pore pressure sensitivity kernels are then used to invert surface‐wave velocity changes for pore pressure variations as a function of depth. By comparing different regions of Groningen, The Netherlands, we build a four‐dimensional pore pressure model for the shallowest 200 m of the subsurface. While the hydrological pore pressure variation can continue beyond 200 m depth, our method is limited by the shallow sensitivity and the frequency ranges for which seismic velocity measurements are possible.
Key Points
Surface‐wave velocity changes are directly linked to pore pressure variations through sensitivity kernels
Pore pressure sensitivity kernels enable an inversion of surface‐wave velocity changes for 4D pore pressure variations
The shallow sensitivity to pore pressure changes in Groningen limits the method to the upper 200 m of the subsurface |
doi_str_mv | 10.1029/2022GL101254 |
format | article |
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Plain Language Summary
In this study, we develop a method for pore pressure monitoring using seismic ambient noise. We use passive image interferometry to estimate surface‐wave velocity changes as a function of frequency, and compute for surface‐wave velocities the sensitivity to pore pressure changes as a function of depth. These so‐called pore pressure sensitivity kernels are then used to invert surface‐wave velocity changes for pore pressure variations as a function of depth. By comparing different regions of Groningen, The Netherlands, we build a four‐dimensional pore pressure model for the shallowest 200 m of the subsurface. While the hydrological pore pressure variation can continue beyond 200 m depth, our method is limited by the shallow sensitivity and the frequency ranges for which seismic velocity measurements are possible.
Key Points
Surface‐wave velocity changes are directly linked to pore pressure variations through sensitivity kernels
Pore pressure sensitivity kernels enable an inversion of surface‐wave velocity changes for 4D pore pressure variations
The shallow sensitivity to pore pressure changes in Groningen limits the method to the upper 200 m of the subsurface</description><identifier>ISSN: 0094-8276</identifier><identifier>EISSN: 1944-8007</identifier><identifier>DOI: 10.1029/2022GL101254</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>ambient noise ; Aquifers ; coda‐based monitoring ; Data recovery ; Depth ; Earthquakes ; Frequency ranges ; Groundwater ; Hydrology ; Interferometry ; Monitoring ; passive image interferometry ; Permeability ; Physics ; Pore pressure ; pore pressure monitoring ; Pore water pressure ; Pressure ; Pressure changes ; Pressure variations ; Seismic data ; Seismic velocities ; Seismological data ; Variation ; Velocity ; Wave velocity</subject><ispartof>Geophysical research letters, 2023-03, Vol.50 (5), p.n/a</ispartof><rights>2023. The Authors.</rights><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3611-e258efb1c588bf0376c3dbc95dc16f65caa4d40b8da54924413f757e03beb0ce3</citedby><cites>FETCH-LOGICAL-c3611-e258efb1c588bf0376c3dbc95dc16f65caa4d40b8da54924413f757e03beb0ce3</cites><orcidid>0000-0001-7153-5115 ; 0000-0001-9207-1129 ; 0000-0001-7295-6691 ; 0000-0002-5868-9491</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2022GL101254$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2022GL101254$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,11513,11561,27923,27924,46051,46467,46475,46891</link.rule.ids></links><search><creatorcontrib>Fokker, Eldert</creatorcontrib><creatorcontrib>Ruigrok, Elmer</creatorcontrib><creatorcontrib>Hawkins, Rhys</creatorcontrib><creatorcontrib>Trampert, Jeannot</creatorcontrib><title>4D Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry</title><title>Geophysical research letters</title><description>This study introduces a technique for four‐dimensional pore pressure monitoring using passive image interferometry. Surface‐wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels can be used to invert time‐lapse seismic velocity changes, retrieved with passive image interferometry, for hydrological pore pressure variations as a function of time, depth, and region. This new approach is applied in the Groningen region of the Netherlands. We show good recovery of pore pressure variations in the upper 200 m of the subsurface from passive seismic velocity observations. This depth range is primarily limited by the reliable frequency range of the seismic data.
Plain Language Summary
In this study, we develop a method for pore pressure monitoring using seismic ambient noise. We use passive image interferometry to estimate surface‐wave velocity changes as a function of frequency, and compute for surface‐wave velocities the sensitivity to pore pressure changes as a function of depth. These so‐called pore pressure sensitivity kernels are then used to invert surface‐wave velocity changes for pore pressure variations as a function of depth. By comparing different regions of Groningen, The Netherlands, we build a four‐dimensional pore pressure model for the shallowest 200 m of the subsurface. While the hydrological pore pressure variation can continue beyond 200 m depth, our method is limited by the shallow sensitivity and the frequency ranges for which seismic velocity measurements are possible.
Key Points
Surface‐wave velocity changes are directly linked to pore pressure variations through sensitivity kernels
Pore pressure sensitivity kernels enable an inversion of surface‐wave velocity changes for 4D pore pressure variations
The shallow sensitivity to pore pressure changes in Groningen limits the method to the upper 200 m of the subsurface</description><subject>ambient noise</subject><subject>Aquifers</subject><subject>coda‐based monitoring</subject><subject>Data recovery</subject><subject>Depth</subject><subject>Earthquakes</subject><subject>Frequency ranges</subject><subject>Groundwater</subject><subject>Hydrology</subject><subject>Interferometry</subject><subject>Monitoring</subject><subject>passive image interferometry</subject><subject>Permeability</subject><subject>Physics</subject><subject>Pore pressure</subject><subject>pore pressure monitoring</subject><subject>Pore water pressure</subject><subject>Pressure</subject><subject>Pressure changes</subject><subject>Pressure variations</subject><subject>Seismic data</subject><subject>Seismic velocities</subject><subject>Seismological data</subject><subject>Variation</subject><subject>Velocity</subject><subject>Wave 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Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry</title><author>Fokker, Eldert ; Ruigrok, Elmer ; Hawkins, Rhys ; Trampert, Jeannot</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3611-e258efb1c588bf0376c3dbc95dc16f65caa4d40b8da54924413f757e03beb0ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>ambient noise</topic><topic>Aquifers</topic><topic>coda‐based monitoring</topic><topic>Data recovery</topic><topic>Depth</topic><topic>Earthquakes</topic><topic>Frequency ranges</topic><topic>Groundwater</topic><topic>Hydrology</topic><topic>Interferometry</topic><topic>Monitoring</topic><topic>passive image interferometry</topic><topic>Permeability</topic><topic>Physics</topic><topic>Pore pressure</topic><topic>pore pressure monitoring</topic><topic>Pore water pressure</topic><topic>Pressure</topic><topic>Pressure changes</topic><topic>Pressure variations</topic><topic>Seismic data</topic><topic>Seismic velocities</topic><topic>Seismological data</topic><topic>Variation</topic><topic>Velocity</topic><topic>Wave velocity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fokker, Eldert</creatorcontrib><creatorcontrib>Ruigrok, Elmer</creatorcontrib><creatorcontrib>Hawkins, Rhys</creatorcontrib><creatorcontrib>Trampert, Jeannot</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Free Content</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech 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Interferometry</atitle><jtitle>Geophysical research letters</jtitle><date>2023-03-16</date><risdate>2023</risdate><volume>50</volume><issue>5</issue><epage>n/a</epage><issn>0094-8276</issn><eissn>1944-8007</eissn><abstract>This study introduces a technique for four‐dimensional pore pressure monitoring using passive image interferometry. Surface‐wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels can be used to invert time‐lapse seismic velocity changes, retrieved with passive image interferometry, for hydrological pore pressure variations as a function of time, depth, and region. This new approach is applied in the Groningen region of the Netherlands. We show good recovery of pore pressure variations in the upper 200 m of the subsurface from passive seismic velocity observations. This depth range is primarily limited by the reliable frequency range of the seismic data.
Plain Language Summary
In this study, we develop a method for pore pressure monitoring using seismic ambient noise. We use passive image interferometry to estimate surface‐wave velocity changes as a function of frequency, and compute for surface‐wave velocities the sensitivity to pore pressure changes as a function of depth. These so‐called pore pressure sensitivity kernels are then used to invert surface‐wave velocity changes for pore pressure variations as a function of depth. By comparing different regions of Groningen, The Netherlands, we build a four‐dimensional pore pressure model for the shallowest 200 m of the subsurface. While the hydrological pore pressure variation can continue beyond 200 m depth, our method is limited by the shallow sensitivity and the frequency ranges for which seismic velocity measurements are possible.
Key Points
Surface‐wave velocity changes are directly linked to pore pressure variations through sensitivity kernels
Pore pressure sensitivity kernels enable an inversion of surface‐wave velocity changes for 4D pore pressure variations
The shallow sensitivity to pore pressure changes in Groningen limits the method to the upper 200 m of the subsurface</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2022GL101254</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-7153-5115</orcidid><orcidid>https://orcid.org/0000-0001-9207-1129</orcidid><orcidid>https://orcid.org/0000-0001-7295-6691</orcidid><orcidid>https://orcid.org/0000-0002-5868-9491</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | ambient noise Aquifers coda‐based monitoring Data recovery Depth Earthquakes Frequency ranges Groundwater Hydrology Interferometry Monitoring passive image interferometry Permeability Physics Pore pressure pore pressure monitoring Pore water pressure Pressure Pressure changes Pressure variations Seismic data Seismic velocities Seismological data Variation Velocity Wave velocity |
title | 4D Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry |
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