A new cohort of master’s level students will join EESA as interns who will work on year-long research projects on August 1. This is the fourth set of students to participate in the Berkeley Lab Pathbreaker program since it was started as a joint collaboration with Cal State University East Bay (CSUEB) by Berkeley Lab Associate Laboratory Director for EESA Susan Hubbard and Ruth Tinnacher of Cal State University East Bay (CSUEB) in 2017.
Julie Shahan, Abbas Jalili, and Fnu Sharmila Bolikoppa Palakshappa were the CSUEB master’s level students selected after a competitive selection process this spring. The interns will begin implementing their one-year research projects virtually next month, due to the Laboratory’s reduced on-site staffing levels. EESA scientists who will serve as intern project mentors – Michelle Newcomer, Housen Chu, Charuleka Varadharajan, Robert Crystal Ornelas, and Jonny Rutqvist – are preparing to support students in conducting the initial aspects of their research through telework.
The three interns will begin collaborating with their Berkeley Lab project mentors and CSUEB faculty advisors on August 1. Their three projects are described below.
Investigating CO2 and CH4 Fluxes Across a Heterogeneous Restored Tidal Wetland
Julie Shahan will be supported in this research by CSUEB faculty Patty Oikawa and EESA scientists Michelle Newcomer and Housen Chu. This study takes place at the Eden Landing Ecological Reserve, a restored tidal salt marsh in the South San Francisco Bay. This research aims to determine what processes are driving higher emissions of CO2 from bare mudflats and higher emissions of CH4 from S. foliosa land cover areas.
Development of automation methods and visualization tools for QA/QC of sensor data from the Watershed Function Scientific Focus Area
Abbas Jalili will be supported in this research by CSUEB faculty Eric A. Suess and Shirley Yap and EESA scientists Charuleka Varadharajan and Robert Crystal-Ornelas.
A watershed is an area or ridge of land that separates waters flowing to different rivers, basins, or seas. This project seeks to use statistical approaches to develop scalable algorithms for some of the Watershed Function SFA sensor data streams. This work will be conducted in coordination with the SFA data management team and build off their prior QA/QC workflows and scripts.
Machine Learning of Induced Seismicity Associated with Fluid Injection in Oklahoma
Sharmila Bolikoppa Palakshappa will be supported in this research by CSUEB faculty Zahra Derakhshandeh and EESA scientist Jonny Rutqvist.
Induced seismicity is a major concern associated with industrial underground fluid injection activities, including waste water injection and injection for sequestering carbon dioxide. In this research project, scientists will use ML methods to predict seismic rate that is correlated to fluid injection considering the impacts of hydromechanical (HM) couplings and indirectly considering the influences of HM on reactivation of faults.