Airborne Geophysical Imaging of Weak Zones on Iliamna Volcano, Alaska: Implications for Slope Stability

Abstract

Water‐saturated, hydrothermally altered rocks reduce the strength of volcanic edifices and increase the potential for sector collapses and far‐traveled mass flows of unconsolidated debris. Iliamna Volcano is an andesitic stratovolcano located on the western side of the Cook Inlet, ∼225 km southwest of Anchorage and is a source of repeated avalanches. The widespread snow and ice cover on Iliamna Volcano make surface alteration difficult to identify. However, intense hydrothermal alteration significantly reduces both the electrical resistivity and magnetization of volcanic rock and can therefore be identified with airborne geophysical measurements. We use airborne electromagnetic and magnetic data to map snow and ice thickness and identify underlying alteration zones at Iliamna Volcano, Alaska. Resistivities were calculated to an average depth of >300 m, and a 3‐D susceptibility model extends from the surface to the base of the volcano, about 3,000 m below the summit. Geophysical models image low resistivity (<30 ohm‐m) and low susceptibilities near the summit of Iliamna and below its older vent complex, with the low susceptibilities indicating alteration up to ∼800 m in thickness. Thin conductors (∼50–100 m thick) on the edifice slopes coincide with recorded locations of repeated debris avalanches over the past ∼60 years and are attributed to saturated zones at high elevation. Three‐dimensional slope stability models based upon the geophysically constrained alteration distribution suggest the edifice of Iliamna is unstable and could lead to collapse scars ∼400 m deep near the current and former vent complexes.

An overview of multimethod imaging approaches in environmental geophysics

“…When modeling a homogeneous subsurface property distribution the most likely flank to collapse was estimated to be the north-western flank, however, when incorporating rock alterations mapped by the AEM data, the south-eastern flank was estimated to having a higher susceptibility to failure. Similar results where also obtained by Peterson et al (2021), which show that particularly deep hydro-thermal alteration of the Iliamna Volcano considerably increases the susceptibility of slope failure, but also increases the potential volume of material that could be released during failure (Fig. 18).…”

“…Yet, in contrast to vadose zone or groundwater studies, where many process-based inversion approaches have been developed and used, in the field of natural hazards this is only now starting to emerge. Some recent studies have combined subsurface structures identified from airborne EM data with hydro-geomechanical models to assess the stability of volcano flanks (Peterson et al, 2021;Finn et al, 2018). By incorporating subsurface features into the slope stability model, Finn et al (2018) could improve the hazards assessment for slope failure of Mt.…”

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

“…For example, using AEM data, subsurface properties in Alaska over an area of about 300 km 2 have been estimated (31). Within the last few years, AEM surveys are covering increasingly larger areas at regional basin scales, as large as 140,000 km 2 (32), and are moving from predominantly flat areas [e.g., (33,34)] to mountainous environments (35)(36)(37). There is an increased effort to use AEM data to construct hydrological models from local to regional scales (38)(39)(40).…”

Instantaneous Inversion of Airborne Electromagnetic Data Based on Deep Learning

“…As the technology develops, AEM survey has been applied extensively in groundwater detection (Ball et al., 2020; Klee, 2013; Mikucki et al., 2015; Minsley et al., 2021; Thomsen et al., 2004; Viezzoli et al., 2010), mineral exploration (Koné et al., 2021; Okada, 2020; Roach et al., 2014), urban investigation (Steuer et al., 2020) and geological mapping (Dzikunoo et al., 2020; Jordan et al., 2009; Ley‐Cooper & Brodie, 2020; Rey et al., 2019; Silvestri et al., 2019; Wong et al., 2020). Recently, it has been exploited specifically in confined aquifer detection (Dugan et al., 2015), subglacial hydrology (Blatter et al., 2018), and volcano hazards (Dumont et al., 2019; Peterson et al., 2021).…”