Data

These maps show the potential of widespread slope failures, in terms of landslide probabilities and displacement, triggered by a M7.0 scenario earthquake on the Hayward Fault in the 10-county area surrounding the San Francisco Bay region, California. The likelihood of landsliding was evaluated using an equation developed by Jibson and others (2000) that estimates landslide probability as a functio

These data are 3D point cloud data collected by laser scanner in the Hetch Hetchy area of Yosemite National Park, USA. The data were collected to assess landscape change and vegetation response following the 2013 Rim wildfire. Filename convention: "hh_datatype_dd_ddmmyyyy_laserreturns_siteidentification". "datatype" is either "alsm" or "tls" The former was collected by the National Center for Airb

This dataset consists of the processed ground motions from 96 earthquake rupture scenarios for M7 earthquakes on the Salt Lake City segment of the Wasatch fault. Details about the simulation method, ground motion variability and sensitivity of the ground motions to the kinematic rupture parameters are described in the accompanying manuscript. Simulated data is distributed as 5-percent damped respo

These data are a geospatial representation of liquefaction potential for the HayWired earthquake scenario, a magnitude 7.0 earthquake occurring on the Hayward Fault on April 18, 2018, with an epicenter in the city of Oakland, CA. These data are the product of an analysis that created a detailed liquefaction probability map covering the northern Santa Clara County and western Alameda County areas.

This dataset represents the spatial locations of all modeled aftershocks magnitude 2.5 and greater resulting from the HayWired M7.0 mainshock occurring on April 18, 2018 along the Hayward Fault. The date/time, horizontal and vertical location, and sequence position is provided for each aftershock. The spatial extent covers 24 counties in whole or in part, corresponding to the modeled shaking exten

We produced a one-year 2017 seismic hazard forecast for the central and eastern United States from induced and natural earthquakes that replaces the one-year 2016 forecast, and evaluated the 2016 seismic hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of damaging ground shaking in 1 year) in portions of Oklahoma/Kansas, th

The U. S. Geological Survey (USGS) makes long-term seismic hazard forecasts that are used in building codes. The hazard models usually consider only natural seismicity; non-tectonic (man-made) earthquakes are excluded because they are transitory or too small. In the past decade, however, thousands of earthquakes related to underground fluid injection have occurred in the central and eastern U.S. (

This is a catalog of earthquake scenarios, represented as ShakeMaps. A ShakeMap is a USGS product that facilitates communication of earthquake effects by portraying a map of the severity of shaking. Maps of shaking severity are provided in terms of macroseismic intensity, peak ground acceleration, peak ground velocity, and spectral accelerations (at 0.3, 1.0 and 3 sec oscillator periods).

Point cloud data in .laz format collected using a 3D laser scanner in Napa California in 2014 and 2015. Also included are aligned airborne laser scanner data from 2003 and 2014.

These data include observations of liquefaction from ten earthquakes. The data are provided as a feature collection in a GeoJSON file format. Individual features are either points or polygons. Each feature has a single attribute called "earthquake" which gives the name and year of the earthquake associated with the liquefaction feature.

A one-year seismic hazard forecast for the Central and Eastern United States (CEUS), based on induced and natural earthquakes, has been produced by the U.S. Geological Survey. The model assumes that earthquake rates calculated from several different time windows will remain relatively stationary and can be used to forecast earthquake hazard and damage intensity for the year 2016. This assessment