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Geologic Hazards Science Center

The Geologic Hazards Science Center (GHSC), on the Colorado School of Mines campus, is home to the National Earthquake Information Center (NEIC), many scientists in the Earthquake Hazards Program and Landslide Hazards Program, as well as the Geomagnetism Program staff.



Earthquakes can strike faster than a New York minute – What to do when the ground shakes...


New USGS map shows where damaging earthquakes are most likely to occur in US


New Year’s Day M7.5 Earthquake Shakes Japan’s West Coast


Satellite Interferometry Landslide Detection and Preliminary Tsunamigenic Plausibility Assessment in Prince William Sound, Southcentral Alaska

Regional mapping of actively deforming landslides, including measurements of landslide velocity, is integral for hazard assessments in paraglacial environments. These inventories are also critical for describing the potential impacts that the warming effects of climate change have on slope instability in mountainous and cryospheric terrain. The objective of this study is to identify slow-moving la

Lauren N. Schaefer, Jinwook Kim, Dennis M. Staley, Zhong Lu, Katherine R. Barnhart

The 2022 Chaos Canyon landslide in Colorado: Insights revealed by seismic analysis, field investigations, and remote sensing

An unusual, high-alpine, rapid debris slide originating in ice-rich debris occurred on June 28, 2022, at 16:33:16 MDT at the head of Chaos Canyon, a formerly glacier-covered valley in Rocky Mountain National Park, CO, USA. In this study, we integrate eyewitness videos and seismic records of the event with meteorological data, field observations, pre- and post-event satellite imagery, and uncrewed
Kate E. Allstadt, Jeffrey A. Coe, Elaine Collins, Francis K. Rengers, Anne Mangeney, Scott M. Esser, Jana Pursley, William L. Yeck, John Bellini, Lance R. Brady

Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-t
Annette Patton, Lisa Luna, Josh J. Roering, Aaron Jacobs, Oliver Korup, Benjamin B. Mirus