## An Overview

Major: Mathematics, Geological Science minor

Academic Affiliation: Boston College

## Biography

A spark from a gifted, passionate high school earth science teacher was the catalyst for New Yorker Brian Chung, an enthusiastic mathematics student with a particular love of the outdoors and cross country running, to pursue an undergraduate education at Boston College connecting mathematics with geophysics. Additional liberal arts interests of the violin and Eastern European linguistics have led Brian to seek opportunities in applying his technical abilities in areas of social significance. At RESESS, Brian conducted computational research in stress evolution and earthquake triggering related to the 1983 Borah Peak earthquake in central Idaho, and has connected his findings to physical manifestations by way of an extensive field trip to the region. His techniques draw on distinguished studies in such areas as southern California and are directly applicable to countless regions in conducting risk analysis for seismic events.

## Abstract

**2013- Prehistoric and modern stress evolution and seismicity in Central Idaho in relation to the 1983 Borah Peak earthquake**

The M7.3 1983 Borah Peak earthquake occurred along the Lost River fault and was the largest historic earthquake in Idaho. The Lost River fault is one of several large normal faults in the central Intermountain Seismic Belt. The stress evolution of this family of faults, including the Lost River, Lemhi, Beaverhead, and Sawtooth, is analyzed by computing Coulomb stress changes from paleoearthquakes and interseismic loading. The event can be understood with respect to prehistoric stress interactions between the brittle and creeping segments of the central Idaho fault system. Paleoseismic dates, offsets, and slip rates are acquired from published scarp and trench analyses. Coulomb stress change models are based on coseismic earthquake offsets in the upper seismogenic crust and on cumulative slip from fault creep in the lower crust. Models of Coulomb stress change are based on known current fault geometry and inferred geometry from the Borah Peak event. The time-lapse models commence at 9.5 ka. Mean dates and slip rates are used in a preliminary model in light of large age ranges on the order of thousands of years. Coulomb stresses from creeping segments are modeled as slipping fault planes from the brittle-ductile boundary down to the crust- mantle boundary. The Borah Peak earthquake and most paleoearthquakes occurred in regions of increased Coulomb stress of up to 5 bars. These stress changes are dominantly dictated by single-segment coseismic displacements rather than interseismic loading in this preliminary model. Coseismic stress drops on a segment are about 5 bars, while interseismic loading contributes to approximately 2-bar Coulomb stress increases in the overriding brittle lithosphere of the same segment. Coulomb stress increases from adjacent segment earthquakes are approximately 4 bars. Both the isolated Borah Peak model and the total stress model are consistent with the distribution of post-Borah Peak earthquakes north of the Lost River fault. Additional models will be run to consider varying paleoearthquake dates and interseismic slip rates to see how sensitive Coulomb stress changes are to these parameters. Interseismic loading displacements will also be compared to GPS velocities to check fault slip rates.

**2014- A non-parametric approach to infer slopes from short time-series with seasonal fluctuation: application to GPS time-series**

A common element of time-series data in fields such as geophysics, hydrology, and astronomy is seasonal signal. This variation obscures the trends that exist in the time-series. Slope estimates can have low bias as long as the data span is long enough to outweigh the effects of the variation of the seasonal signal. The traditional method of calculating slopes of time-series is least-squares regression. Blewitt and Lavallée [2002] conclude that least-squares slope estimates that account for seasonal signal produce reasonable biases for data spans above 2.5 years long. They add that least squares slope estimates not accounting for seasonal signal produce acceptable biases for data spans beyond 4.5 years. The Theil-Sen method is a nonparametric approach to time-series regression that does not depend on the bold assumptions of parametric methods like least-squares. This method is also robust to outliers, whereas least-squares can be heavily influenced by the presence of outliers.

Geodesists calculate tectonic plate velocities based on the position data that come in the form of slopes in time-series. Technological malfunctions as well as natural, anthropogenic, and economic factors often limit the length of time spans over which one can obtain useful velocity estimates. Van Dam et al. [2001], note the effects of seasonal atmospheric and hydrospheric loading on the lithosphere, which alters GPS position measurements. A challenge at the forefront of geodesy is generating accurate and meaningful plate velocity estimates from short timespan time-series. The seasonal Theil-Sen method is henceforth examined as a slope estimator for GPS time-series shorter than 2.5 years.

Through Monte Carlo experiments on synthetic time-series and application to real data, the seasonal Theil-Sen estimator is found not to be a viable method for short time-series slope analyses. The seasonal and non-seasonal least-squares estimators along with Blewitt and Lavallée’s dataspan requirements continue to be the most effective slope estimation methods time-series. The Theil-Sen estimator, however, shows promise is specific types of time-series with phenomena—like steps.