Quantifying Geophysical Inversion Uncertainty Using Airborne Frequency Domain Electromagnetic Data—Applied at the Province of Zeeland, the Netherlands. Issue 10 (29th October 2018)

• Record Type: Journal Article
• Authors: King, Jude; Oude Essink, Gualbert; Karaolis, Marios; Siemon, Bernhard; Bierkens, Marc. F. P.
• Is Part Of: Water resources research. Volume 54:Issue 10(2018)
• Page Start: 8420
• Page End: 8441
• Publication Date: 2018-10-29
• Subjects: hydrogeophysics -- inversion -- airborne geophysics -- airborne electromagnetics -- groundwater salinity -- saltwater intrusion; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2018WR023165
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: An accurate understanding of the fresh‐saline distribution of groundwater is necessary for effective groundwater management. Airborne electromagnetic (AEM) surveys offer a rapid and cost‐effective method with which to map this, offering valuable additional information about the subsurface. To convert AEM data into electric conductivity and ultimately groundwater salinity, an inversion is undertaken. A number of algorithms are available for this purpose; however, these are affected by significant uncertainty, owing to inherent nonunique characteristics of this process. The most commonly used inversion codes in hydrogeophysical studies were quantitatively tested using frequency domain AEM and ground data from the province of Zeeland, the Netherlands. These include UBC1DFM code and quasi‐2D laterally constrained inversions. Following an investigation of inversion parameter settings, data were inverted for four inversion methods and interpolated into 3‐D volumes. Using geological data and empirical electrical conductivity and water salinity relationships, each inversion was converted into groundwater electrical conductivity and split into fresh‐brackish‐saline regions. For groundwater volume estimates out of a total volume of 2.8 billion m 3, a fresh groundwater estimate could differ by as much as 178 million m 3, depending on the inversion used. The primary factor here was the choice of model smoothness, which was shown to affect the thickness of the brackish interval. Fresh‐brackish‐saline interfaces were consistently mapped with an accuracy of ~3 m, the brackish being the most accurately resolved. The few layer method was less successful at resolving smoothly varying salinity distributions but more successful at mapping the brackish interface at greater depth. Plain Language Summary: Understanding the current distribution of groundwater salinity in low‐elevation coastal zones is important because globally over 600 million people live and potentially rely on fresh groundwater in these areas. Fresh groundwater is also vulnerable to a process called saline intrusion, where usable groundwater can be displaced by less‐usable saline groundwater. Groundwater salinity on more regional scales is currently mapped by an instrument that is towed beneath a helicopter. This technique measures electromagnetic responses that relate mostly to salinity variations within groundwater reserves. For these measurements to make sense, they need to be transformed into units that can physically relate to groundwater salinity. This process is called inversion. However, as this procedure comes with significant uncertainty, there are a number of different algorithms available for this. Here we used airborne and ground data from the province of Zeeland, the Netherlands, and tested eight inversion algorithms that are commonly used for this purpose. Results indicate that based on the inversion type, fresh groundwater estimates in a volume of 2.8 billion m 3 could differ by up to 178 million m 3 . Finally, it was shown that according to mapping objectives, the type of inversion needs to be selected carefully to maximize the use of the highly valuable airborne data. Key Points: Airborne electromagnetic and ground data from the Netherlands are used to quantify differences between commonly used 1‐D inversion algorithms Qualitatively, inversions are found to be consistent; however, a quantitative analysis on resulting 3‐D groundwater salinity volumes highlights significant differences on mapping outcomes Out of a total volume of 2.8 billion m 3, a fresh groundwater volume estimate differed by up to 6.5%, depending on the inversion method used; the primary controlling factor on results was found to be model smoothness