Application of Iteratively Truncated Singular Value Decomposition to Computation of Teleseismic Receiver Functions

Okeler, A.

doi:10.1007/s00024-021-02767-2

Cited by 3 publications

(1 citation statement)

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“…Over the past two decades, advances in the methodology of deconvolution have triggered the development of increasingly sophisticated algorithms of RF estimation in pursuit of higher stability, accuracy, and flexibility (e.g., C.‐W. Chen et al., 2010; Kolb & Lekić, 2014; Mercier et al., 2006; Ökeler, 2021; Yildirim et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

Multichannel Sparse Deconvolution of Teleseismic Receiver Functions With f − x Preconditioning

Sun

Sacchi

2023

JGR Solid Earth

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Teleseismic receiver functions (RFs) are an effective tool for structural imaging due to their simplicity and sensitivity to changes in rock elastic properties. Generally, RFs are estimated through trace‐by‐trace deconvolution. This individualized approach ignores the lateral coherency among RFs from neighboring raypaths, which could lead to rougher or incorrect seismic sections due to the presence of noise. This study presents a multichannel sparse deconvolution method that takes advantage of the cross‐trace coherency of RFs at individual stations for stable and accurate imaging outcomes. The proposed algorithm incorporates sparse inversion and frequency‐space prediction filters, which facilitate the retrieval of high‐resolution and spatially continuous conversion energy. Rigorous testing using synthetic and actual data typically suggests the higher robustness of multichannel deconvolution over single‐channel deconvolution, especially under low signal‐to‐noise ratios. The noise‐resistance property of the multichannel approach offers new opportunities to increase the volume of usable, high‐quality data for receiver function analysis.

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