Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008

Abstract

Continuous Global Positioning System (GPS) networks record station position changes with millimeter‐level accuracy and have revealed transient deformations on various spatial and temporal scales. However, the transient deformation may not be easily identified from the position time series because of the large number of sites in a network, low signal‐to‐noise ratios (SNR) and correlated noise in space and time. Here we apply state estimation and principal component analysis to the daily GPS position time series measured in Alaska sites of the Plate Boundary Observatory network. Our algorithm detects a transient signal, whose maximum displacement is ∼9 mm in horizontal and ∼11 mm in vertical, that occurred at Akutan volcano during the first half of 2008. A simple Mogi source inversion suggests inflation at shallow depth (∼3.9 km) beneath the volcano. Although the detection was not easy because the signal was aseismic, non‐eruptive and weak (not apparent in raw daily time series), our detection method improves the SNR and therefore provides higher resolution for detecting the transient signal.

Episodic inflation events at Akutan Volcano, Alaska, during 2005–2017

“…The TPO measures how closely observed data are represented by a predefined spatial pattern (or a target). [Ji and Herring, 2011]. Large amplitudes for this projection indicate that observed data contain a spatial pattern close to the target.…”

“…In March 1996, an intense earthquake swarm was accompanied by uplift as much as 60 cm on the west flank of Akutan [Lu et al, 2000b[Lu et al, , 2005. Small inflation events occurred in 2008 [Ji and Herring, 2011;Li et al, 2016] and in 2011 [Walwer et al, 2016], identified with Global Positioning System (GPS) data. The 1996The , 2008, and 2011 events did not culminate in an eruption, and they may be episodes of magma accumulation in a shallow reservoir underneath Akutan.…”

“…Here we used as a target the Mogi point source model [Mogi, 1958] with a depth of 3.9 km inferred from the 2008 inflation episode detected by principal component analysis (PCA) and Kalman filtering [Ji and Herring, 2011] (Figure 1a). A target should represent volcano deformation.…”

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

“…For example, GNSS trends of vertical station position are used to quantify processes such as glacial isostatic rebound (for a comprehensive review on this topic see King et al 2010), sea-level rise (for a comprehensive review see Blewitt et al 2010), present day ice-mass change (Khan et al 2010a,b), and inflation and deflation of calderas (Ji and Herring 2011;Chang et al 2007;Tizzani et al 2007). For these signals, the trends determined from short times series (less than 3 years) are often the same order of magnitude as the uncertainty on the trend.…”

The SCEC Geodetic Transient-Detection Validation Exercise

“…Ji and Herring (2013) present details of their approach, which relies on principle-component analysis of filtered time series and has been applied to data outside of southern California (Ji and Herring, 2011). Granat et al (2013) present results of three separate detection approaches applied to the synthetic data, all based on spatial and temporal coherence of the signals instead of reliance on a physical model for the underlying deformation.…”