Increasing deployment of dense arrays has facilitated detailed structure imaging for tectonic investigation, hazard assessment and resource exploration. Strong velocity heterogeneity and topographic changes have to be considered during passive source imaging. However, it is quite challenging for ray-based methods, such as Kirchhoff migration or the widely used teleseismic receiver function, to handle these problems. In this study, we propose a 3-D passive source reverse time migration strategy based on the spectral element method. It is realized by decomposing the time reversal full elastic wavefield into amplitude-preserved vector P and S wavefields by solving the corresponding weak-form solutions, followed by a dot-product imaging condition to get images for the subsurface structures. It enables us to use regional 3-D migration velocity models and take topographic variations into account, helping us to locate reflectors at more accurate positions than traditional 1-D model-based methods, like teleseismic receiver functions. Two synthetic tests are used to demonstrate the advantages of the proposed method to handle topographic variations and complex velocity heterogeneities. Furthermore, applications to the Laramie array data using both teleseismic P and S waves enable us to identify several south-dipping structures beneath the Laramie basin in southeast Wyoming, which are interpreted as the Cheyenne Belt suture zone and agree with, and improve upon previous geological interpretations.

For seismographic stations with short acquisition duration, the signal-to-noise ratios (SNRs) of ambient noise cross-correlation functions (CCFs) are typically low, preventing us from accurately measuring surface wave dispersion curves or waveform characteristics. In addition, with low-quality CCFs, it is difficult to monitor temporal variations of subsurface physical states or extract relatively weak signals such as body waves. In this study, we propose to use local attributes to improve the SNRs of ambient noise CCFs, which allows us to enhance the quality of CCFs for stations with limited acquisition duration. Two local attributes: local cross-correlation and local similarity, are used in this study. The local cross-correlation allows us to extend the dimensionality of daily CCFs with computational costs similar to global cross-correlation. Taking advantage of this extended dimensionality, the local similarity is then used to measure non-stationary similarity between the extended daily CCFs with a reference stacking trace, which enables us to design better stacking weights to enhance coherent features and attenuate incoherent background noises. Ambient noise recorded by several broadband stations from the USArray in North Texas and Oklahoma, the Superior Province Rifting EarthScope Experiment in Minnesota and Wisconsin and a high-frequency nodal array deployed in the San Bernardino basin are used to demonstrate the performance of the proposed approach for improving the SNR of CCFs.