Advanced analysis of complex seismic waveforms to characterize the subsurface Earth structure

Tianxia Jia

Advanced analysis of complex seismic waveforms to characterize the subsurface Earth structure
Jia, Tianxia
Thesis Advisor(s):
Lerner-Lam, Arthur L.
Earth and Environmental Sciences
Persistent URL:
Ph.D., Columbia University.
This thesis includes three major parts, (1) Body wave analysis of mantle structure under the Calabria slab, (2) Spatial Average Coherency (SPAC) analysis of microtremor to characterize the subsurface structure in urban areas, and (3) Surface wave dispersion inversion for shear wave velocity structure. Although these three projects apply different techniques and investigate different parts of the Earth, their aims are the same, which is to better understand and characterize the subsurface Earth structure by analyzing complex seismic waveforms that are recorded on the Earth surface. My first project is body wave analysis of mantle structure under the Calabria slab. Its aim is to better understand the subduction structure of the Calabria slab by analyzing seismograms generated by natural earthquakes. The rollback and subduction of the Calabrian Arc beneath the southern Tyrrhenian Sea is a case study of slab morphology and slab-mantle interactions at short spatial scale. I analyzed the seismograms traversing the Calabrian slab and upper mantle wedge under the southern Tyrrhenian Sea through body wave dispersion, scattering and attenuation, which are recorded during the PASSCAL CAT/SCAN experiment. Compressional body waves exhibit dispersion correlating with slab paths, which is high-frequency components arrivals being delayed relative to low-frequency components. Body wave scattering and attenuation are also spatially correlated with slab paths. I used this correlation to estimate the positions of slab boundaries, and further suggested that the observed spatial variation in near-slab attenuation could be ascribed to mantle flow patterns around the slab. My second project is Spatial Average Coherency (SPAC) analysis of microtremors for subsurface structure characterization. Shear-wave velocity (Vs) information in soil and rock has been recognized as a critical parameter for site-specific ground motion prediction study, which is highly necessary for urban areas located in seismic active zones. SPAC analysis of microtremors provides an efficient way to estimate Vs structure. Compared with other Vs estimating methods, SPAC is noninvasive and does not require any active sources, and therefore, it is especially useful in big cities. I applied SPAC method in two urban areas. The first is the historic city, Charleston, South Carolina, where high levels of seismic hazard lead to great public concern. Accurate Vs information, therefore, is critical for seismic site classification and site response studies. The second SPAC study is in Manhattan, New York City, where depths of high velocity contrast and soil-to-bedrock are different along the island. The two experiments show that Vs structure could be estimated with good accuracy using SPAC method compared with borehole and other techniques. SPAC is proved to be an effective technique for Vs estimation in urban areas. One important issue in seismology is the inversion of subsurface structures from surface recordings of seismograms. My third project focuses on solving this complex geophysical inverse problems, specifically, surface wave phase velocity dispersion curve inversion for shear wave velocity. In addition to standard linear inversion, I developed advanced inversion techniques including joint inversion using borehole data as constrains, nonlinear inversion using Monte Carlo, and Simulated Annealing algorithms. One innovative way of solving the inverse problem is to make inference from the ensemble of all acceptable models. The statistical features of the ensemble provide a better way to characterize the Earth model.
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Suggested Citation:
Tianxia Jia, 2011, Advanced analysis of complex seismic waveforms to characterize the subsurface Earth structure, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:10935.

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