The 2016 Central Italy earthquake sequence caused numerous landslides over a large area in the Central Apennines. As a result, the Geotechnical Extreme Events Reconnaissance Association (GEER) organized post-earthquake reconnaissance missions to collect perishable data. Given the challenging conditions following the earthquakes, the GEER team implemented a phased reconnaissance approach. This paper illustrates this approach and how it was used to document the largest and most impactful seismically induced landslides. This phased approach relied upon satellite-based interferometric damage proxy maps, preliminary published reports of observed landslides, digital imaging from small unmanned aerial vehicles (UAVs), traditional manual observations, and terrestrial laser scanning. Data collected from the reconnoitered sites were used to develop orthophotos and meshed three-dimensional digital surface models. These products can provide valuable information such as accurate measurements of landslide ground movements in complex topographic geometries or boulder runout distances from rock falls. The paper describes three significant landslide case histories developed and documented with the phased approach: Nera Valley, Village of Pescara del Tronto, and near the villages of Crognaleto and Cervaro.
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Ground deformation data from GEER investigations of Ridgecrest Earthquake Sequence
Following the Ridgecrest Earthquake Sequence, consisting of a M6.4 foreshock and M7.1 mainshock along with many other foreshocks and aftershocks, the Geotechnical Extreme Events Reconnaissance (GEER) Association deployed a team to gather perishable data. The team focused their efforts on documenting ground deformations including surface fault rupture south of the Naval Air Weapons Station China Lake, and liquefaction features in Trona and Argus. The team published a report within two weeks of the M7.1 mainshock. This paper presents data products gathered by the team, which are now published and publicly accessible. The data products presented herein include ground-based observations using GPS trackers, digital cameras, and hand measuring devices, as well as UAV-based imaging products using Structure from Motion to create point clouds and digital surface models. The paper describes the data products, as well as tools available for interacting with the products.
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- Award ID(s):
- 1826458
- NSF-PAR ID:
- 10165387
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Seismological research letters
- ISSN:
- 0895-0695
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Surface rupture from the 2019 Ridgecrest earthquake sequence, initially associated with the Mw 6.4 foreshock, occurred on 4 July on a ∼17 km long, northeast–southwest-oriented, left-lateral zone of faulting. Following the Mw 7.1 mainshock on 5 July (local time), extensive northwest–southeast-oriented, right-lateral faulting was then also mapped along a ∼50 km long zone of faults, including subparallel splays in several areas. The largest slip was observed in the epicentral area and crossing the dry lakebed of China Lake to the southeast. Surface fault rupture mapping by a large team, reported elsewhere, was used to guide the airborne data acquisition reported here. Rapid rupture mapping allowed for accurate and efficient flight line planning for the high-resolution light detection and ranging (lidar) and aerial photography. Flight line planning trade-offs were considered to allocate the medium (25 pulses per square meter [ppsm]) and high-resolution (80 ppsm) lidar data collection polygons. The National Center for Airborne Laser Mapping acquired the airborne imagery with a Titan multispectral lidar system and Digital Modular Aerial Camera (DiMAC) aerial digital camera, and U.S. Geological Survey acquired Global Positioning System ground control data. This effort required extensive coordination with the Navy as much of the airborne data acquisition occurred within their restricted airspace at the China Lake ranges.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1785/0220190291
