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An upper-crust lid over the Long Valley magma chamber revealed by fiber tomography

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Manage episode 425609980 series 1399341
Content provided by USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Ettore Biondi, California Institute of Technology

Traveltime-based tomographic methods have been extensively explored and employed by researchers since the 80s. Such algorithms have been successfully applied to various geophysical applications, ranging from seismic exploration to global to regional seismological scales. However, given the advancements in computational architectures over the last 20 years, full-waveform methodologies are now dominating most of the subsurface-parameter inversion applications. These workflows seek to match all the waveforms present within active seismic data or synthetic Green’s functions obtained by cross-correlating ambient noise.

Despite this decrease in the popularity of traveltime-based tomographic approaches, these methods have great potential to be successful when applied to distributed acoustic sensing (DAS) data for seismic applications. DAS instruments can operate on existing telecommunication fibers and transform them into large-scale high-resolution seismic arrays. We demonstrate such potential by applying an Eikonal traveltime double-difference tomography algorithm to DAS data recorded in the Long Valley caldera, located in the Eastern Sierra region of California. This active volcanic area has been extensively studied in the last 50 years and its recent unrest remains still poorly understood.

We employ two DAS arrays composed of almost 9000 channels along a 90-km north-south transect across the caldera to characterize the subsurface structures present underneath the area. We use almost 2000 cataloged events and apply a machine-learning algorithm to accurately pick their P and S arrival times necessary for the tomography. The range and spatial resolution of the DAS arrays allow us to retrieve structures that could not be resolved by previous studies that employed only conventional station recordings.

Our results agree well with previous studies and highlight the presence of a low-velocity basin along the Mono-Inyo craters. Both P- and S-wave models also show a low-velocity structure centered below Mono Lake, which agrees with historical gravity surveys. Moreover, the low Vp/Vs ratio inverted below the Long Valley caldera suggests a lack of newly intruded materials at depth above 10 km and a clear separation between the shallow low-velocity basins and the ≥10-km deep magmatic reservoir.

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20 episodes

Artwork
iconShare
 
Manage episode 425609980 series 1399341
Content provided by USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by USGS, Menlo Park (Scott Haefner) and U.S. Geological Survey or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Ettore Biondi, California Institute of Technology

Traveltime-based tomographic methods have been extensively explored and employed by researchers since the 80s. Such algorithms have been successfully applied to various geophysical applications, ranging from seismic exploration to global to regional seismological scales. However, given the advancements in computational architectures over the last 20 years, full-waveform methodologies are now dominating most of the subsurface-parameter inversion applications. These workflows seek to match all the waveforms present within active seismic data or synthetic Green’s functions obtained by cross-correlating ambient noise.

Despite this decrease in the popularity of traveltime-based tomographic approaches, these methods have great potential to be successful when applied to distributed acoustic sensing (DAS) data for seismic applications. DAS instruments can operate on existing telecommunication fibers and transform them into large-scale high-resolution seismic arrays. We demonstrate such potential by applying an Eikonal traveltime double-difference tomography algorithm to DAS data recorded in the Long Valley caldera, located in the Eastern Sierra region of California. This active volcanic area has been extensively studied in the last 50 years and its recent unrest remains still poorly understood.

We employ two DAS arrays composed of almost 9000 channels along a 90-km north-south transect across the caldera to characterize the subsurface structures present underneath the area. We use almost 2000 cataloged events and apply a machine-learning algorithm to accurately pick their P and S arrival times necessary for the tomography. The range and spatial resolution of the DAS arrays allow us to retrieve structures that could not be resolved by previous studies that employed only conventional station recordings.

Our results agree well with previous studies and highlight the presence of a low-velocity basin along the Mono-Inyo craters. Both P- and S-wave models also show a low-velocity structure centered below Mono Lake, which agrees with historical gravity surveys. Moreover, the low Vp/Vs ratio inverted below the Long Valley caldera suggests a lack of newly intruded materials at depth above 10 km and a clear separation between the shallow low-velocity basins and the ≥10-km deep magmatic reservoir.

  continue reading

20 episodes

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