scholarly journals Lithospheric Structure Across the Alaskan Cordillera From the Joint Inversion of Surface Waves and Receiver Functions

2018 ◽  
Vol 123 (10) ◽  
pp. 8780-8797 ◽  
Author(s):  
Kevin M. Ward ◽  
Fan‐Chi Lin
Keyword(s):  
Surface Waves ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Lithospheric Structure

scholarly journals Lithospheric structure beneath the northern Central Andean Plateau from the joint inversion of ambient noise and earthquake‐generated surface waves

2016 ◽  
Vol 121 (11) ◽  
pp. 8217-8238 ◽  
Author(s):  
Kevin M. Ward ◽  
George Zandt ◽  
Susan L. Beck ◽  
Lara S. Wagner ◽  
Hernando Tavera
Keyword(s):  
Surface Waves ◽  
Ambient Noise ◽  
Joint Inversion ◽  
Lithospheric Structure

scholarly journals Inferring Crustal Temperatures Beneath Italy From Joint Inversion of Receiver Functions and Surface Waves

2019 ◽  
Vol 124 (8) ◽  
pp. 8771-8785 ◽  
Author(s):  
G. Diaferia ◽  
F. Cammarano ◽  
N. Piana Agostinetti ◽  
C. Gao ◽  
V. Lekic ◽  
...  
Keyword(s):  
Surface Waves ◽  
Joint Inversion ◽  
Receiver Functions

scholarly journals Reliable workflow for inversion of seismic receiver function and surface wave dispersion data: a “13 BB Star” case study

2019 ◽  
Vol 24 (1) ◽  
pp. 101-120
Author(s):  
Kajetan Chrapkiewicz ◽  
Monika Wilde-Piórko ◽  
Marcin Polkowski ◽  
Marek Grad
Keyword(s):  
Surface Wave ◽  
Inverse Problems ◽  
Receiver Function ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
P Wave ◽  
Surface Wave Dispersion ◽  
Phase Velocity Dispersion ◽  
Wide Range

AbstractNon-linear inverse problems arising in seismology are usually addressed either by linearization or by Monte Carlo methods. Neither approach is flawless. The former needs an accurate starting model; the latter is computationally intensive. Both require careful tuning of inversion parameters. An additional challenge is posed by joint inversion of data of different sensitivities and noise levels such as receiver functions and surface wave dispersion curves. We propose a generic workflow that combines advantages of both methods by endowing the linearized approach with an ensemble of homogeneous starting models. It successfully addresses several fundamental issues inherent in a wide range of inverse problems, such as trapping by local minima, exploitation of a priori knowledge, choice of a model depth, proper weighting of data sets characterized by different uncertainties, and credibility of final models. Some of them are tackled with the aid of novel 1D checkerboard tests—an intuitive and feasible addition to the resolution matrix. We applied our workflow to study the south-western margin of the East European Craton. Rayleigh wave phase velocity dispersion and P-wave receiver function data were gathered in the passive seismic experiment “13 BB Star” (2013–2016) in the area of the crust recognized by previous borehole and refraction surveys. Final models of S-wave velocity down to 300 km depth beneath the array are characterized by proximity in the parameter space and very good data fit. The maximum value in the mantle is higher by 0.1–0.2 km/s than reported for other cratons.

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