Artwork

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://ppacc.player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Leveraging high temporal and spatial resolution geodetic data through the earthquake cycle

1:00:00
 
Share
 

Manage episode 467653967 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://ppacc.player.fm/legal.

Cassie Hanagan, USGS

Advancing our understanding of earthquake processes inevitably pushes the bounds of data resolution in the spatial and temporal domains. This talk will step through a series of examples leveraging two relatively niche geodetic datasets for understanding portions of the earthquake cycle: (1) temporally dense and sensitive borehole strainmeter (BSM) data, and (2) spatially dense sub-pixel image correlation displacement data. More specifically, I will detail gap-filling benefits of these two datasets for different earthquakes.

BSMs respond to a frequency of deformation that bridges the capabilities of more common GNSS stations and seismometers. As such, they are typically installed to capture deformation signals such as slow slip or transient creep. In practice they are also useful for measuring dynamic and static coseismic strains. This portion of the talk will focus on enhanced network capabilities for detecting both coseismic and postseismic deformation with a relatively new BSM array in the extensional Apennines of Italy, with events spanning tens to thousands of kms away. Then, we will transition toward how these instruments can constrain spatiotemporally variable afterslip following the 2019 Mw7.1 Ridgecrest, California earthquake.

High spatial resolution displacements from sub-pixel image correlation serve as gap-filling datasets in another way – providing higher spatial resolution (~0.5 m) maps of the displacement fields than any other method to date, and patching areas where other methods fail to capture the full deformation magnitude or extent, such as where InSAR decorrelates. This portion of the talk will focus on new results that define expected displacement detection thresholds from high-resolution satellite optical imagery and, alternatively, from repeat lidar data. Examples will include synthetic and real case studies of discrete and diffuse deformation from earthquakes and fault creep.

  continue reading

18 episodes

Artwork
iconShare
 
Manage episode 467653967 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://ppacc.player.fm/legal.

Cassie Hanagan, USGS

Advancing our understanding of earthquake processes inevitably pushes the bounds of data resolution in the spatial and temporal domains. This talk will step through a series of examples leveraging two relatively niche geodetic datasets for understanding portions of the earthquake cycle: (1) temporally dense and sensitive borehole strainmeter (BSM) data, and (2) spatially dense sub-pixel image correlation displacement data. More specifically, I will detail gap-filling benefits of these two datasets for different earthquakes.

BSMs respond to a frequency of deformation that bridges the capabilities of more common GNSS stations and seismometers. As such, they are typically installed to capture deformation signals such as slow slip or transient creep. In practice they are also useful for measuring dynamic and static coseismic strains. This portion of the talk will focus on enhanced network capabilities for detecting both coseismic and postseismic deformation with a relatively new BSM array in the extensional Apennines of Italy, with events spanning tens to thousands of kms away. Then, we will transition toward how these instruments can constrain spatiotemporally variable afterslip following the 2019 Mw7.1 Ridgecrest, California earthquake.

High spatial resolution displacements from sub-pixel image correlation serve as gap-filling datasets in another way – providing higher spatial resolution (~0.5 m) maps of the displacement fields than any other method to date, and patching areas where other methods fail to capture the full deformation magnitude or extent, such as where InSAR decorrelates. This portion of the talk will focus on new results that define expected displacement detection thresholds from high-resolution satellite optical imagery and, alternatively, from repeat lidar data. Examples will include synthetic and real case studies of discrete and diffuse deformation from earthquakes and fault creep.

  continue reading

18 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play