|Title||Statistical Approaches to Detecting Transient Signals in GPS: Results from the 2009-2011 Transient Detection Exercise|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Granat R, Parker J, Kedar S, Dong DA, Tang BY, Bock Y|
|Journal||Seismological Research Letters|
|Type of Article||Article|
|Keywords||algorithm; cascadia subduction; continuous speech recognition; Crustal deformation; hector mine; hidden markov-models; northridge earthquake; silent; slip; slow; zone|
We present the results of our participation in four phases of the Southern California Earthquake Center (SCEC) transient detection exercise (Lohman and Murray, 2013). In each phase, a blind test was conducted in which sets of synthetic Global Positioning Systems (GPS) data were released and a deadline set for submission of detection results. For each data set, the presence or absence of transient events was to be determined, and the location and time of each specified. After all submissions were received, the ground‐truth information about any transient signals in the data was released. The synthetic data sets were generated by FAKENET, a software package that produces realistic GPS time series that include secular motion and seasonal signals as well as realistic noise and distributions of missing data (Agnew, 2013). Station locations in the synthetic data set were a subset of GPS installations in the western United States.
In this work, we pursue a purely data‐driven approach to transient detection, rather than one based on an assumption of an underlying physical model. We view this approach as having two important advantages. First, it facilitates the detection of events, which might happen through a previously unknown, but geophysically interesting, physical process or on a previously unknown fault or structure. Second, it enables the detection of events, which have nothing to do with the solid earth but, although not the subject of the SCEC exercise, have scientific or practical merit. These include signals such as those due to atmospheric phenomena as well as signals, which result from hardware faults or failures and software processing glitches.