Direct-seismogram inversion for receiver-side structure with unknown source-time functions

Abstract:

This work presents direct-seismogram inversion for receiver-side structure which treats the source signal incident from below (the effective source-time function, STF) as a vector of unknown parameters in a Bayesian framework. As a result, direct-seismogram inversion does not require deconvolution by observed seismogram components as typically applied in receiver-function inversion, and avoids the problematic issue of choosing subjective tuning parameters in the deconvolution. This results in more meaningful inversion results and uncertainty estimation (compared to classic receiver function inversion). A rigorous likelihood function is derived for unbiased inversion results and the STF is efficiently inferred by a maximum-likelihood closed form expression that does not require deconvolution by noisy waveforms. Rather, deconvolution is only by predicted impulse responses for the unknown environment. For a given realization of the parameter vector which describes the medium below the station, data predictions are computed as the convolution of the impulse response and the maximum-likelihood source estimate for that medium. Therefore, the assumption of a Gaussian pulse with specified parameters, typical for the prediction of receiver functions, is not required. Directly inverting seismogram components has important consequences for the noise on the data. Since the signal processing does not require filtering and deconvolution, data errors (including measurement and theory errors) appear to be less correlated and more straightforward to model than those for receiver functions. The direct-seismogram inversion is demonstrated for simulated waveforms and applied to teleseismic data recorded for station Hyderabad on the Indian craton.