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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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Seismic hazard in the South Carolina coastal plain: 2002 update of the USGS national seismic hazard maps

The damaging 1886 moment magnitude ???7 Charleston, South Carolina earthquake is indicative of the moderately likely earthquake activity along this portion of the Atlantic Coast. A recurrence of such an earthquake today would have serious consequences for the nation. The national seismic hazard maps produced by the U.S. Geological Survey (USGS) provide a picture of the levels of seismic hazard across the nation based on the best and most current scientific information. The USGS national maps were updated in 2002 and will become part of the International Codes in 2006. In the past decade, improvements have occurred in the scientific understanding of the nature and character of earthquake activity and expected ground motions in the central and eastern U.S. The paper summarizes the new knowledge of expected earthquake locations, magnitudes, recurrence, and ground-motion decay with distance. New estimates of peak ground acceleration and 0.2 s and 1.0 s spectral acceleration are compared with those displayed in the 1996 national maps. The 2002 maps show increased seismic hazard in much of the coastal plain of South Carolina, but a decrease in long period (1 s and greater) hazard by up to 20% at distances of over 50 km from the Charleston earthquake zone. Although the national maps do not account for the effects of local or regional sediments, deep coastal-plain sediments can significally alter expected ground shaking, particularly at long period motions where it can be 100% higher than the national maps.

Conference Paper↗

Automated georeferencing and feature extraction of geologic maps and mineral sites

The predictive power of mineral prospectivity analysis depends on high quality, spatially accurate, analysis-ready datasets. Of paramount importance are geologic maps and mineral site data, but the state of readiness for utilizing these datasets remains sub-optimal for advanced computational techniques. As the U.S. Geological Survey (USGS) fulfils its mission to map the distribution of critical mineral commodities, non-georeferenced maps held within historical collections represent rich sources of input data. Through a series of machine learning challenges organized by the Defense Advanced Research Projects Agency (DARPA) in collaboration with the USGS, significant progress has been made in accelerating data ingestion, processing, and preparation tasks that enable mineral prospectivity mapping and mineral resource assessment workflows. Specifically, two tasks that previously required time-intensive human effort, 1) georeferencing map images, and 2) legend-based feature extraction from map images, are discussed.

Conference Paper↗