Receiver Functions from Autoregressive Deconvolution

Summary Receiver functions can be estimated by minimizing the square errors of Wiener filter in time-domain or spectrum division in frequency domain. To avoid the direct calculation of auto-correlation and cross-correlation coefficients in Toeplitz equation or of auto-spectrum and cross-spectrum in spectrum division equation as well as empirically choosing a damping parameter, autoregressive deconvolution is presented to isolate receiver function from three-component teleseismic P waveforms. The vertical component of teleseismic P waveform is modeled by an autoregressive model, which can be forward and backward, predicted respectively. The optimum length of the autoregressive model is determined by the Akaike criterion. By minimizing the square errors of forward and backward predicting filters, autoregressive filter coefficients can be recursively solved, and receiver function is also estimated in the similar procedure. Both synthetic and real data tests show that autoregressive deconvolution is an effective method to isolate receiver function from teleseismic P waveforms in time-domain.