September 2022 – Technical Talk
Speaker: Dr. Seogi Kang, Postdoc Researcher, Stanford University.
Title: Time-lapse inversion of airborne electromagnetic data for monitoring saltwater intrusion
Date: Wed, September 28, 2022
Time: 5:00pm to 6:00pm PST
Locations: This talk will be held both in-person and virtually via Zoom.
In-person:
Room 111, 409 Granville St. Vancouver BC, V6C 1T2
Virtual: via Zoom
Link will be distributed via our newsletter prior to the talk.
Bio:
Dr. Kang completed his PhD in Geophysics at University of British Columbia, Canada, in 2018. His thesis work focused on computational electromagnetics and its application to mining problems. Currently, he is a Postdoctoral Researcher in the Geophysics Department at Stanford. His current research focus is on maximizing the value of electromagnetic imaging for groundwater management and science. He is a co-creator of an open-source geophysical software, SimPEG.
Abstract:
In coastal areas, half of the population lives and 75% of the cities are located. Groundwater is a major source of freshwater in these coastal. Increased water demand with the population growth causes more pumping of groundwater. Climate change causes sea level rise. Net effect of these is increased saltwater intrusion threatening the freshwater security in coastal regions. Due to the close connection between salinity and electrical resistivity (or conductivity), electromagnetic (EM) geophysics can play a critical role by imaging saltwater intrusion. A specific form of the EM geophysics used in this study is the airborne EM (AEM) method, which can rapidly map out subsurface resistivity of a large coastal region. While there are many AEM experiments for imaging subsurface hydrogeology, conducting time-lapse AEM experiments is not common due to the relatively expensive cost the survey as well as the repeatability issues. Further, there is yet no implementation of time-lapse inversion for AEM data, which simultaneously invert multiple AEM data sets with a constraint along a time dimension. Working with the two AEM data sets acquired in 2017 and 2019 at the Northern Salinas Valley of California, USA, in this study, we developed a novel time-lapse inversion approach using an Lp-norm for the spatial and temporal constraints. Three different types of inversion were conducted: 1) Separate inversions with L2-norm constraints; 2) Time-lapse inversion with L2-norm constraints; 3) Time-lapse inversion with L0-norm constraints. From inversion results, we found that the time constraint played an important role for minimizing the inversion artifacts in the conductivity difference between resistivity models at 2017 and 2019. We also found that the use of L0-norm for both spatial and time constraints provided the most confident estimate of the conductivity difference due to the sparse nature of the implemented L0-norms. The final conductivity difference from the time-lapse inversion with L0-norm was compared with salinity contours from in-situ measurements.
Webinar:
A recording of this webinar is available on our Youtube channel.