Geology topics

Anna H. Olsen

Publications and source records attributed to Anna H. Olsen.

7 recordsLinked to original sources

USGS2015

The 2014 United States National Seismic Hazard Model

New seismic hazard maps have been developed for the conterminous United States using the latest data, models, and methods available for assessing earthquake hazard. The hazard models incorporate new information on earthquake rupture behavior observed in recent earthquakes; fault studies that use both geologic and geodetic strain rate data; earthquake catalogs through 2012 that include new assessments of locations and magnitudes; earthquake adaptive smoothing models that more fully account for the spatial clustering of earthquakes; and 22 ground motion models, some of which consider more than double the shaking data applied previously. Alternative input models account for larger earthquakes, more complicated ruptures, and more varied ground shaking estimates than assumed in earlier models. The ground motions, for levels applied in building codes, differ from the previous version by less than ±10% over 60% of the country, but can differ by ±50% in localized areas. The models are incorporated in insurance rates, risk assessments, and as input into the U.S. building code provisions for earthquake ground shaking.

Earthquake Spectra↗

USGS2015

Characterizing ground motions that collapse steel special moment-resisting frames or make them unrepairable

This work applies 64,765 simulated seismic ground motions to four models each of 6- or 20-story, steel special moment-resisting frame buildings. We consider two vector intensity measures and categorize the building response as “collapsed,” “unrepairable,” or “repairable.” We then propose regression models to predict the building responses from the intensity measures. The best models for “collapse” or “unrepairable” use peak ground displacement and velocity as intensity measures, and the best models predicting peak interstory drift ratio, given that the frame model is “repairable,” use spectral acceleration and epsilon ( ϵ ) as intensity measures. The more flexible frame is always more likely than the stiffer frame to “collapse” or be “unrepairable.” A frame with fracture-prone welds is substantially more susceptible to “collapse” or “unrepairable” damage than the equivalent frame with sound welds. The 20-story frames with fracture-prone welds are more vulnerable to P-delta instability and have a much higher probability of collapse than do any of the 6-story frames.

Earthquake Spectra↗

USGS2015

Seismic-hazard maps for the conterminous United States, 2014

The maps presented here provide an update to the 2008 data contained in U.S Geological Survey Scientific Investigations Map 3195 ( http://pubs.usgs.gov/sim/3195/ ).Probabilistic seismic-hazard maps were prepared for the conterminous United States for 2014 portraying peak horizontal acceleration and horizontal spectral response acceleration for 0.2- and 1.0-second periods with probabilities of exceedance of 10 percent in 50 years and 2 percent in 50 years. All of the maps were prepared by combining the hazard derived from spatially smoothed historical seismicity with the hazard from fault-specific sources. The acceleration values contoured are the random horizontal component. The reference site condition is firm rock, defined as having an average shear-wave velocity of 760 m/s in the top 30 meters corresponding to the boundary between NEHRP (National Earthquake Hazards Reduction program) site classes B and C. This data set represents the results of calculations of hazard curves for a grid of points with a spacing of 0.05 degrees in latitude and longitude. The grid of points was contoured to produce the final representation of the seismic hazard. These maps are intended to summarize the available quantitative information about seismic ground motion hazard for the conterminous United States from geologic and geophysical sources.

Scientific Investigations Map↗

USGS2014

Documentation for the 2014 update of the United States national seismic hazard maps

The national seismic hazard maps for the conterminous United States have been updated to account for new methods, models, and data that have been obtained since the 2008 maps were released (Petersen and others, 2008). The input models are improved from those implemented in 2008 by using new ground motion models that have incorporated about twice as many earthquake strong ground shaking data and by incorporating many additional scientific studies that indicate broader ranges of earthquake source and ground motion models. These time-independent maps are shown for 2-percent and 10-percent probability of exceedance in 50 years for peak horizontal ground acceleration as well as 5-hertz and 1-hertz spectral accelerations with 5-percent damping on a uniform firm rock site condition (760 meters per second shear wave velocity in the upper 30 m, V S30 ). In this report, the 2014 updated maps are compared with the 2008 version of the maps and indicate changes of plus or minus 20 percent over wide areas, with larger changes locally, caused by the modifications to the seismic source and ground motion inputs.

Open-File Report↗

USGS2014

2014 update of the U.S. national seismic hazard maps

We held 8 regional and topical workshops across the U.S. to gather information for these maps. The maps were available to the public for comment during a 60-day period. A Steering Committee (9 experts) was assembled to review the inputs and results and provide additional insights. The maps have been presented at several professional meetings. In this talk we discuss: (1) CEUS, (2) WUS (outside California), (3) California, (4) Ground motion models We discuss the hazard maps and hazard curves used in the building code. The maps in this presentation are for 2% probability of exceedance in 50 years on a uniform firm rock site condition (V S30 =760 m/s). Maps are for 0.2 s (5 Hz) spectral acceleration with 5% damping. We consider difference maps (2014-2008) and ratio maps (2014/2008).

Book↗

USGS2012

Demand surge following earthquakes

Demand surge is understood to be a socio-economic phenomenon where repair costs for the same damage are higher after large- versus small-scale natural disasters. It has reportedly increased monetary losses by 20 to 50%. In previous work, a model for the increased costs of reconstruction labor and materials was developed for hurricanes in the Southeast United States. The model showed that labor cost increases, rather than the material component, drove the total repair cost increases, and this finding could be extended to earthquakes. A study of past large-scale disasters suggested that there may be additional explanations for demand surge. Two such explanations specific to earthquakes are the exclusion of insurance coverage for earthquake damage and possible concurrent causation of damage from an earthquake followed by fire or tsunami. Additional research into these aspects might provide a better explanation for increased monetary losses after large- vs. small-scale earthquakes.

Conference Paper↗

USGS2011

On the contribution of reconstruction labor wages and material prices to demand surge

Demand surge is understood to be a socio-economic phenomenon of large-scale natural disasters, most commonly explained by higher repair costs (after a large- versus small-scale disaster) resulting from higher material prices and labor wages. This study tests this explanation by developing quantitative models for the cost change of sets, or "baskets," of repairs to damage caused by Atlantic hurricanes making landfall on the mainland United States. We define six such baskets, representing the total repair cost, and material and labor components, each for a typical residential or commercial property. We collect cost data from the leading provider of these data to insurance claims adjusters in the United States, and we calculate the cost changes from July to January for nine Atlantic hurricane seasons at fifty-two cities on the Atlantic and Gulf Coasts. The data show that: changes in labor costs drive the changes in total repair costs; cost changes can vary significantly by geographic region and year; and cost changes for the residential basket of repairs are more volatile than the cost changes for the commercial basket. We then propose a series of multilevel regression models to predict the cost changes by considering several combinations of the following explanatory variables: the largest gradient wind speed at a city in a hurricane season; the number of tropical storms in a hurricane season whose center passes within 200 km of a city; and cost changes in the first two quarters of the year. We also allow the coefficients of the regression model to be stochastic, varying across groups defined by region of the Southeastern United States and year. Our best models predict that, for any city on the Gulf or Atlantic Coasts in any hurricane season, the residential total repair cost changes vary from 0.01 to 0.25, depending on the wind speed and number of storms, with an uncertainty of 0.1 (two standard errors of prediction) given the wind speed and number of storms. The commercial total repair cost changes vary from 0.005 to 0.15 with an uncertainty of 0.08. Our models including wind speed, the number of storms affecting a city, and cost changes in the first half of the year explain roughly half of the observed variability in cost changes. Additional explanatory variables that we have not considered may account for the remaining variability. Given these models, however, there is still considerable uncertainty in their predictions. This uncertainty arises from variations between groups defined by region and year, not from variations within a given region and year.

SESM↗