An Effective Technique to Constrain the Non‐Uniqueness of Receiver Function Inversion

Abstract

The relation curve between the apparent velocity of S wave and the filter‐parameter is obtained by low‐pass filtering the radial and vertical receiver function, then it is transformed into the S velocity structure beneath the station with empirical formula; moreover, the S velocity structure will be regarded as initial model for receiver function inversion. The numerical simulation indicated that the inversion processes would approach quickly to the true model once the initial S velocity can supply the effective constraint to inversion, even if the depth of Moho is not accurate. In addition, for the same initial S structure, the inversion results also approach to the true model quickly, even adding 10% of random noises to the “observed” receiver function, and 5% of positive and negative perturbation to the velocity ratio (corresponding to the Poisson's ratio of 0.27 and 0.23) respectively. We processed the teleseismic receiver function recorded by Baoshan station. The result from our method is similar to that of sounding section. These confirmed that the inversion processes of receiver function are not sensitive to the initial P velocity if the initial S wave velocity can provide the effective constraint information.

Crustal S-Wave Velocity from Apparent Incidence Angles: A Case Study in Preparation for InSight

“…However, the number of layers is in principle also a variable in the inversion process , and it would be interesting to know if this information can be obtained directly from the data. Peng et al (2012) suggest to look for maxima in the derivative dv S,app (T )/dT to identify the depth of layer boundaries based on some scaling relation, but for more complex models, these boundaries do not correctly reflect the number of layers in the models, and the derived depths show large deviations as the v S,app (T ) curves only constrain the average velocity over a certain depth range (Fig. 3).…”

“…After adjusting the method to the boundary conditions at the ocean bottom, Hannemann et al (2016) successfully applied it to an OBS data set with one to five receiver functions per station, thus demonstrating its usefulness when only a small number of event recordings is available. It has also been shown that a priori S-velocity information deduced from P-wave polarization measurements can be useful when attempting to model or invert the actual receiver function waveforms (Peng et al 2012;Hannemann et al 2017), even when polarization information at long periods is missing (Kieling et al 2011). Schiffer et al (2015Schiffer et al ( , 2016 used the frequency-dependent apparent S-wave velocities as stabilizing information in a joint inversion with receiver function waveforms, whereas Chong et al (2018) directly inverted the measured receiver function amplitude ratios instead.…”

A review on the analysis of the crustal and upper mantle structure using receiver functions

“…Synthetic receiver function waveform is called as ''observed'' waveform in Fig. 4a, in which there are several Ps phases, and delay time of the Ps phase from Moho is 5.5 s. The initial model of S-wave velocity with depth is got according to Peng et al (2012), represented by black dash line in Fig. 4b.…”

“…To test the feasibility of the empirical formula in Peng et al (2012) in getting an initial model, we take a complicated crustal model by Ammon et al (1990) as ''true'' model for numerical experiment. As shown in Fig.…”

“…Svenningsen and Jacobsen (2007) suggested using low-pass filtered receiver functions for the calculation of the apparent incidence angle. Using empirical method, Peng et al (2012) transformed the relation between the period of low-pass filter and the S wave velocity calculated through Eq. (13) into the relation between S wave velocity and depth, which can be used as an initial S velocity model for waveform fitting.…”