- © 2015 by the Seismological Society of America
Receiver function imaging is an important tool for imaging the local and regional lithospheric structure beneath seismic arrays. The spatial density of stations and uniformity of their spacing in a given study area can strongly influence image quality. Sparsely distributed stations may produce distorted images and artifacts due to uneven spatial sampling. Reconstructing and resampling seismic wavefields to produce virtual stations within a regular, fine‐scale observation system can provide a more accurate image with fewer artifacts.
We developed a wavefield reconstruction method to accurately map teleseismic data from a sparsely spaced seismic network into a regular and fine‐scale observation system. The method reconstructs wavefields in each time slice using a 2D cubic‐spline interpolation. As a preprocess step, wavefield reconstruction can significantly improve the receiver function by greatly reducing stair‐step artifacts for dipping structures. We applied the method to data from the Ordos regional network, which has a station interval of ∼30–80 km. The virtual stations rendered image results that showed almost perfect consistency with images produced by a fine‐scale temporary array having uniform station interval of 10 km. Numerical experiments show the wavefield reconstruction method improved lithospheric images derived from traditional receiver function imaging techniques.
RECEIVER FUNCTION METHODS
Receiver function imaging is routinely used to identify velocity discontinuity structures in the crust and upper mantle. Receiver functions are obtained by deconvolving vertical components from horizontal components of teleseismic records (Langston, 1979). Irregularities in distributions of both seismic stations and earthquakes pose challenges for receiver function imaging techniques. Based on theoretical seismograms from 2D models, Ryberg and Weber (2000) deduced that resolving structures of similar scale requires a station spacing of a few kilometers. This result demonstrates the best way to improve receiver function image quality is to increase station density. Station spacings of a few kilometers are rare, due to the high costs of their …