In order to use the airborne electromagnetic (AEM) method to image subsurface structures, the resistivity models produced from AEM data via inversion must be transformed into a measure of lithology such as sediment texture. This transform is often built using sediment texture information sourced from wells. State-of-the-art rock physics transforms have several significant limitations due to the following problems: spatial separation of the AEM resistivity models and the well sediment texture data, vertical resolution differences between the AEM method and common well drilling methods, and the variable sensitivity of the AEM method. Here, we have developed a Markov Chain Monte Carlo (MCMC) based resistivity-to-sediment-texture transform (MC-TexT) for the construction of the rock physics transform without any of the aforementioned limitations. MC-TexT was developed and tested on data from an AEM survey in Butte and Glenn counties in the northern portion of the Central Valley of California. A simple synthetic test case was run that showed that the resistivity distributions for each sediment texture category retrieved with MC-TexT had a small amount of bias but overall matched very well the input distributions. When applied to the field data from Butte and Glenn counties, MCTexT resulted in probabilistic realizations of the subsurface that qualitatively agreed with prior geologic understanding and the well sediment texture data from the area.
Presentation Date: Monday, October 12, 2020
Session Start Time: 1:50 PM
Presentation Time: 3:55 PM
Location: Poster Station 4
Presentation Type: Poster
