The characteristics analysis of strain variation associated with Wenchuan earthquake using principal component analysis

Borehole strainmeters that are installed deeply into bedrock are capable of recording both continuous stress and strain measurements, and have consequently become an important tool for monitoring crustal deformation. A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Wenchuan earthquake on May 12, 2008 (UTC) (=8.0). We apply principal component analysis (PCA) to analyze borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors are calculated. The fitted results of the cumulative number of anomalous eigenvalues demonstrate that an acceleration occurred approximately 4 months before the earthquake (from January 2008). The results of the combined eigenvalue and eigenvector analyses show that the spatial distribution of eigenvectors and accelerated occurrence of eigenvalue anomalies represents the stress evolution characteristics of the fault from a steady state to a sub-instability state in rock experiments. We tentatively infer that this process may also be linked to the preparation phase of a large earthquake

Extracting borehole strain precursors associated with the Lushan earthquake through principal component analysis

A YRY-4 borehole strainmeter installed at the Guza Station recorded anomalous changes in borehole strain data preceding the Lushan earthquake on April 20, 2013 (UTC) ( =7.0). To identify earthquake-induced abnormal strain changes, we apply principal component analysis (PCA) for the first time to analyse the borehole strain data from the Guza Station. The first principal component eigenvalues and eigenvectors demonstrate that the anomalous days are mainly concentrated within two time periods:1) October 25-December 30, 2012, and 2) April 15-19,2013. A combined eigenvalue and eigenvector analysis reveals that the abnormal days exhibit a clustered distribution that is aggregated in the same location for both periods, intuitively indicating that there is a forceful correlation between the two anomalies. We infer that a similar process contributed to the formation of both anomalies and that these two anomalies are both earthquake precursors associated with the Lushan earthquake. These findings also indicate that the PCA approach exhibits potential for the extraction of earthquake precursor anomalies

Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve

In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, Nano, and the length of alarm window, Talm. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, tpr, and false alarm rate, fpr, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting

The Seventh Visual Object Tracking VOT2019 Challenge Results

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering &amp; Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia</p