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Space geodetic data improve seismic hazard assessment in California: Workshop on incorporating geodetic surface deformation data Into UCERF3; Pomona, California, 1–2 April 2010

A workshop was held to begin scientific consideration of how to incorporate space geodetic constraints on strain rates and fault slip rates into the next generation Uniform California Earthquake Rupture Forecast, version 3 (UCERF3), due to be completed in mid-2012. Principal outcomes of the meeting were (1) an assessment of secure science ready for UCERF3 applications within the next year, and (2) an agenda of new research objectives for the Southern California Earthquake Center (SCEC), the U.S. Geological Survey (USGS), and others in support of UCERF3 and related probabilistic seismic hazard assessments (PSHA). A number of goals potentially achievable within a year were identified, including (1) slip rate and fault locking depth estimates, with uncertainties or ranges, for all major and some minor faults of the extended San Andreas system; (2) strain rate estimates or bounds on rates for selected regions lying off the major faults of the San Andreas system; and (3) corrections or bounds on perturbing effects of postseismic deformation and elastic modulus heterogeneities on the observed Global Positioning System (GPS) velocity field (needed as input to models for estimating fault slip and strain rates in goals 1 and 2 above).

California

Mathematical models for non-parametric inferences from line transect data

A general mathematical theory of line transects is developed which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(0) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y I r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(0 I r).

Biometrics