| dc.description.abstract | Geophysical observations, ranging from transient earthquake oscillations to tectonic deformations at geological scales, lie in a broad temporal spectrum. The work presented in this dissertation explores the asynchronous tools and models for the complex and iterative data analysis procedures applied in the seismology and geodesy.
First, we explore the procedures applied to the seismic data and illustrate using three studies to characterize and invert for the moving source and the Earth structure. In the first study, the mysterious explosion sounds heard in the coastal town of Tamsui in Taiwan in 2013 was identified in the seismic and infrasound array data and characterized as a meteor shockwave signal and the trajectory of the meteor is inverted using Genetic Algorithm optimization scheme. In the second study, Rayleigh wave phase velocity anomaly maps of Gujarat, a westernmost province in India, is explored in a broad spectrum of 20-90s. The computed inter-station dispersion curves are inverted for high-resolution isotropic and azimuthally anisotropic phase velocity maps at each period independently, coinciding well with the known geological features in the region. In the third study, a fully automated package is developed (in Python) to conduct the Receiver Functions (RF) and Shear-wave Splitting (SWS) computation for the user-provided input parameters (the only manual part). The dataset is automatically searched and downloaded from all the available data centers around the world and is processed, and computed for RF and SWS results independently along with high-resolution figures. The package is applied to the USArray data for the RF analysis and the networks around Germany for SWS measurements.
Finally, a study is conducted to understand the origin of the high amplitude, long period, and spatially coherent common-mode error (CME) in continuous GPS (CGPS) data. Ten years of daily crustal deformations recorded at 47 CGPS stations in Taiwan are analyzed to understand the origin of CME whose seasonality evidences meteorological origin. CME is extracted using the Empirical Orthogonal Functions (EOFs) analysis and found to be significantly correlated with the atmospheric mass loading displacements in both temporal and spectral domains. | en_US |