Cordilleran Section - 116th Annual Meeting - 2020

Paper No. 13-22
Presentation Time: 9:00 AM-6:00 PM

STRATIGRAPHIC FRAMEWORK OF THE PLIOCENE TUSCAN FORMATION AND QUATERNARY UNITS IN THE VINA SUB-BASIN, CHICO, CA


STOKES, Scott, Geological & Environmental Sciences, California State University, Chico, Chico, CA 95929-0205 and GREENE, Todd J., Geological and Environmental Sciences, California State University-Chico, 400 W. 1st Street, Chico, CA 95929-0205

The Vina Sub-basin (VSB) is considered a high-priority groundwater basin within a rich agricultural area in the northeastern Sacramento Valley, near the cities of Chico and Durham, California. Recently acquired Airborne Electromagnetic (AEM) data is currently being used to help create a Hydrogeologic Conceptual Model (HCM) as part of the Groundwater Sustainability Plan mandated by the Sustainable Groundwater Management Act (SGMA). However, the focus of this study is to fill in a 130 square km gap between two main AEM fly zones within the VSB in order to better understand the geologic and hydrogeologic characteristics of the Pliocene-aged Tuscan Formation and overlying Quaternary aquifer system. This gap contains well completion reports and geophysical log data from 151 wells of varying quality and vertical resolution. The data was digitized and inserted into the Petra software (IHS-Markit) and was used to create three time-equivalent subsurface horizons (UT2, UT3 and TT1). The subsurface horizons were then used to construct structure maps and percent coarse material maps with the intention to display spatial variations between horizons and highlight channelized areas with high concentrations of coarse sediment. We also assigned quality control values to every subsurface horizon pick to compare maps from low and high quality picks to demonstrate the value of having a geologist qualify well completion report data. Future work will use modeling software to create training images in our study area that will provide ground-truth details of channel dimensions, sinuosity, and channel density. These training images can then be used to guide statistical models that distribute lithologic facies for the larger AEM datasets.