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METEOR: Online Seismic Metadata Builder
Interpreting bibliometric signals
Engineering Characteristics of Ground Motions Recorded in the 2019 Ridgecrest Earthquake Sequence
Interpreting and reporting 40 Ar/ 39 Ar geochronologic data
Can We Trust High‐Frequency Content in Strong‐Motion Database Signals? Impact of Housing, Coupling, and Installation Depth of Seismic Sensors
RESIF RAP and RLBP Dataset of Earthquake Ground Motion in Mainland France
The 2019 Ridgecrest, California, Earthquake Sequence Ground Motions: Processed Records and Derived Intensity Metrics
A consistent global approach for the morphometric characterization of subaqueous landslides
Abstract Landslides are common in aquatic settings worldwide, from lakes and coastal environments to the deep sea. Fast-moving, large-volume landslides can potentially trigger destructive tsunamis. Landslides damage and disrupt global communication links and other critical marine infrastructure. Landslide deposits act as foci for localized, but important, deep-seafloor biological communities. Under burial, landslide deposits play an important role in a successful petroleum system. While the broad importance of understanding subaqueous landslide processes is evident, a number of important scientific questions have yet to receive the needed attention. Collecting quantitative data is a critical step to addressing questions surrounding subaqueous landslides. Quantitative metrics of subaqueous landslides are routinely recorded, but which ones, and how they are defined, depends on the end-user focus. Differences in focus can inhibit communication of knowledge between communities, and complicate comparative analysis. This study outlines an approach specifically for consistent measurement of subaqueous landslide morphometrics to be used in the design of a broader, global open-source, peer-curated database. Examples from different settings illustrate how the approach can be applied, as well as the difficulties encountered when analysing different landslides and data types. Standardizing data collection for subaqueous landslides should result in more accurate geohazard predictions and resource estimation.