Abstract

[1] 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.

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