Blind Signal Separation Methods for InSAR: The Potential to Automatically Detect and Monitor Signals of Volcanic Deformation

Abstract

There are some 1,500 volcanoes with the potential to erupt, but most are not instrumentally monitored. However, routine acquisition by the Sentinel‐1 satellites now fulfils the requirements needed for interferometric synthetic aperture radar (InSAR) to progress from a retrospective analysis tool to one used for near‐real‐time monitoring globally. However, global monitoring produces vast quantities of data, and consequently, an automatic detection algorithm is therefore required that is able to identify signs of new deformation, or changes in rate, in a time series of interferograms. On the basis that much of the signal contained in a time series of interferograms can be considered as a linear mixture of several latent sources, we explore the use of blind source separation methods to address this issue. We consider principal component analysis and independent component analysis (ICA) which have previously been applied to InSAR data and nonnegative matrix factorization which has not. Our systematic analysis of the three methods shows ICA to be best suited for most applications with InSAR data. However, care must be taken in the dimension reduction step of ICA not to remove important smaller magnitude signals. We apply ICA to the 2015 Wolf Volcano eruption (Galapagos Archipelago, Ecuador) and automatically isolate three signals, which are broadly similar to those manually identified in other studies. Finally, we develop a prototype detection algorithm based on ICA to identify the onset of the eruption.

How AI and satellites could help predict volcanic eruptions

“…At the University of Leeds, UK, a group led by geophysicist Andrew Hooper is developing another way to detect potential signs of unrest automatically. If the ground is already deforming at a volcano, his method will flag if that distortion starts to speed up, slow down or change in some other way 3 . That would allow researchers to detect even small ground alterations over long periods of time.…”

Automated Methods for Detecting Volcanic Deformation Using Sentinel‐1 InSAR Time Series Illustrated by the 2017–2018 Unrest at Agung, Indonesia

“…To be useful in volcanic crises, monitoring systems need to be available quickly, robust, and objective. Automated analysis tools are particularly useful for volcano observatories as they can be routinely applied at large number of systems and used to flag anomalous events for expert analysis (Anantrasirichai et al, , ; Gaddes et al, ).…”

“…Recent examples include the regional study of volcanic unrest in Latin America (Pritchard et al, ) and the study of specific events such as the 2014 unrest at Chiles‐Cerro Negro (Ebmeier et al, ) or the 2010 eruption at Merapi volcano (Pallister et al, ). Automated monitoring systems are being developed to routinely analyze large volumes of InSAR data, making use of artificial intelligence tools such as machine learning (Anantrasirichai et al, , ) and blind source separation (Ebmeier, ; Gaddes et al, , ).…”

Using Conceptual Models to Relate Multiparameter Satellite Data to Subsurface Volcanic Processes in Latin America

“…Automatic classification schemes may be able to expand this to a larger sample of volcanoes (Jerram & Cheadle, 2000;Meng & Maynard, 2001;Templ et al, 2008). Recent advances in applying machine learning to volcanic activity (e.g., Anantrasirichai et al, 2019Anantrasirichai et al, , 2018Gaddes et al, 2019Gaddes et al, , 2018 should simplify this complex processes. This of course would produce quicker results that would be based on a uniform method of analysis.…”