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Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, $K$ -nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate. [ABSTRACT FROM AUTHOR]

Variations of the ionosphere can significantly disrupt synthetic aperture radar (SAR) acquisitions and interferometric measurements of ground deformation. In this paper, we show how the ionosphere can also strongly modify C-band interferograms despite its smaller influence at higher frequencies. Thus, ionospheric phase screens should not be neglected: their compensation improves the estimation of ground deformation maps. The split-spectrum method is able to estimate the dispersive ionospheric component of the interferometric phase; we describe the implementation of this method for the burst modes TOPS and ScanSAR to estimate and remove ionospheric phase screens. We present Sentinel-1 interferograms of the 2016 Taiwan earthquake and ALOS-2 interferograms of the 2015 Nepal earthquake, which show strong ionospheric phase gradients, and their corrected versions. Finally, to validate the results and better understand the origin of these ionospheric variations, we compare the estimated differential ionosphere with global Total Electron Content maps and local Global Positioning System measurements. [ABSTRACT FROM PUBLISHER]