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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
Main Authors: Fokker, Eldert, Ruigrok, Elmer, Hawkins, Rhys, Trampert, Jeannot
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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
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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 &amp; 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. 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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 &amp; 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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