Error estimation in multitemporal InSAR deformation time series, with application to Lanzarote, Canary Islands

Interferometric Synthetic Aperture Radar (InSAR) is a reliable technique for measuring crustal deformation. However, despite its long application in geophysical problems, its error estimation has been largely overlooked. Currently, the largest problem with InSAR is still the atmospheric propagation errors, which is why multitemporal interferometric techniques have been successfully developed using a series of interferograms. However, none of the standard multitemporal interferometric techniques, namely PS or SB (Persistent Scatterers and Small Baselines, respectively) provide an estimate of their precision. Here, we present a method to compute reliable estimates of the precision of the deformation time series. We implement it for the SB multitemporal interferometric technique (a favorable technique for natural terrains, the most usual target of geophysical applications). We describe the method that uses a properly weighted scheme that allows us to compute estimates for all interferogram pixels, enhanced by a Montecarlo resampling technique that properly propagates the interferogram errors (variance-covariances) into the unknown parameters (estimated errors for the displacements). We apply the multitemporal error estimation method to Lanzarote Island (Canary Islands), where no active magmatic activity has been reported in the last decades. We detect deformation around Timanfaya volcano (lengthening of line-of-sight ∼ subsidence), where the last eruption in 1730–1736 occurred. Deformation closely follows the surface temperature anomalies indicating that magma crystallization (cooling and contraction) of the 300-year shallow magmatic body under Timanfaya volcano is still ongoing.Peer reviewe

Intraoperative characteristics and adverse events.

<p>Intraoperative characteristics and adverse events.</p

Flowchart.

<p>Flowchart showing the screening and inclusion process for randomised controlled trials, which were included or excluded in the current study.</p

Study flow diagram.

<p>Study flow diagram.</p

Overview of journals`adherence to CONSORT checklist items.

<p>Shown are three categories of adherence to the 37 respective 36 (in case of not applicability of item 11b) CONSORT checklist items: below 50%, between 50% and 80%, above 80%. Results are shown in percent per journal and of all assessed journals (total).</p

Forrest plot of awake craniotomy failure.

<p>The summary value is an overall estimate from a random-effect model. The vertical dotted line shows an overall estimate of outcome proportion (based on the meta-analysis) disregarding grouping by technique. Of note, Souter et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156448#pone.0156448.ref060" target="_blank">60</a>] have used both anaesthesia techniques.</p

Anaesthesia characteristics part 1.

<p>Anaesthesia characteristics part 1.</p

Forrest plot of conversion into general anaesthesia.

<p>The summary value is an overall estimate from a random-effect model. The vertical dotted line shows an overall estimate of outcome proportion (based on the meta-analysis) disregarding grouping by technique. Of note, Souter et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156448#pone.0156448.ref060" target="_blank">60</a>] have used both anaesthesia techniques. GA, general anaesthesia.</p

Correlation of RCT adherence to CONSORT checklist and citation frequency.

<p>Correlation of citation frequency from 01.01.2012–31.12.2014 and fulfilled CONSORT items of each RCT.</p

Journals’ information.

<p>List of included journals, journals´ s impact factor, number of published RCTs in each journal and total number of citations assessed form 01.01.2012–31.12.2014 of the analysed RCTs per journal.</p><p>Journals’ information.</p