Asiye Aziz Zanjani

Using spatiotemporal patterns in seismicity resolved from high-resolution catalogs, I identify where deformation is localized across a region. InSAR satellite radar is particularly valuable here, revealing the total surface deformation resulting from both seismic and aseismic processes. Within induced seismicity frameworks, these patterns can be correlated with the evolution of pore pressure and poroelastic effects driven by fluid injection and extraction. I develop high-resolution earthquake catalogs using machine learning pickers and advanced relocation tools. I developed a module, Pick Aggregator that allows generation of ensemble ML catalogs on the SeisBench platform.

For seismic imaging, I analyze Green's functions constructed from ambient noise cross-correlation and receiver functions using dense nodal arrays. Stacking coherent noise allows me to measure surface wave dispersion, and receiver function analysis resolves crustal discontinuities. Then I use joint inversion to leverage the strengths of each dataset in constructing shear wave velocity structure. High-resolution velocity models are important not only for constraining seismic sources with precision, but also for resolving subsurface anomalies that can be interpreted alongside available geologic and geophysical data.

A third avenue in my research is stochastic ground motion modeling, a statistical framework for analyzing filtered ground velocities and Fourier spectral amplitudes of weak motion records in the time and frequency domains, respectively, to develop source-attenuation models that feed into hazard assessment.