Crustal structure study based on principal component analysis of receiver functions

Zhang, Jianyong; Chen, Ling; Wang, Xu

doi:10.1007/s11430-018-9341-9

Cited by 8 publications

(13 citation statements)

References 24 publications

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“…Additional components are needed as distance and back azimuth ranges increase. Using synthetics and real data, Zhang et al (2019) demonstrated that just the first few principal components could effectively reconstruct all the data variance within events from varying back azimuths. Here, we used events with varying distances and similar back azimuths to establish an equivalent idea.…”

Section: Discussionmentioning

confidence: 99%

Joint Inversion of Receiver Functions and Apparent Incidence Angles to Determine the Crustal Structure of Mars

Joshi

Knapmeyer‐Endrun

Mosegaard

et al. 2023

Geophysical Research Letters

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Recent estimates of the crustal thickness of Mars show a bimodal result of either ∼20 or ∼40 km beneath the InSight lander. We propose an approach based on random matrix theory applied to receiver functions (RFs) to further constrain the subsurface structure. Assuming a spiked covariance model for our data, we first use the phase transition properties of the singular value spectrum of random matrices to detect coherent arrivals in the waveforms. Examples from terrestrial data show how the method works in different scenarios. We identify three previously undetected converted arrivals in the InSight data, including the first multiple from a deeper third interface. We then use this information to jointly invert RFs with the absolute S‐wave velocity information in the polarization of body waves. Results show a crustal thickness of 43 ± 5 km beneath the lander with two mid‐crustal interfaces at depths of 8 ± 1 and 21 ± 3 km.

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Section: Discussionmentioning

confidence: 99%

Joint Inversion of Receiver Functions and Apparent Incidence Angles to Determine the Crustal Structure of Mars

Joshi

Knapmeyer‐Endrun

Mosegaard

et al. 2023

Geophysical Research Letters

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“…For anisotropic medium, the Ps arrival time T varies relative to back azimuth with a period of π to 2π (Cassidy, 1992; Levin & Park, 1997; J. Li et al., 2019; Savage, 1998; Zhang et al., 2019). The period and phase of the high‐order terms are associated with the plunging angle and the trend of the symmetric axis of the overlying anisotropic material (J. Li et al., 2019; Zhang et al., 2019). Thus, the parameterization of anisotropy is difficult without the knowledge of the anisotropic type, such as the symmetric axes of the overriding materials (J. Li et al., 2019).…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the harmonic back azimuthal variation, the dip direction can be indicated by the reconstructed RFs with principal features through PCA on RFs (Figures S1d-S1f in Supporting Information S1; Zhang et al, 2019). Following Zhang et al (2019), we stack the R-RFs within 10° of back azimuth after moveout correction. Then, we extract the three largest eigenvalues from the RF covariance matrix to retrieve the decomposed R-RF waveforms, which correspond to the principal components (PCs).…”

Section: Principal Component Analysismentioning

confidence: 99%

“…This method can estimate the orientations of symmetric axes within anisotropic crustal layers and can also be applied to estimate the dip direction of dipping layers (Audet, 2015;Schulte-Pelkum & Mahan, 2014). Recently, Zhang et al (2019) introduced a data-driven method, in which principal component analysis (PCA) is adopted to decompose RFs by principal eigenvalues, and apply it to estimate the dip direction of the Moho discontinuity.…”

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confidence: 99%

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A New Method to Estimate Slab Dip Direction Using Receiver Functions and Its Application in Revealing Slab Geometry and a Diffuse Plate Boundary Beneath Sumatra

et al. 2023

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While dip direction is a fundamental parameter of slab geometry, it is rarely estimated quantitatively. Here, we develop a new method, Dip Direction Searching (DDS), of receiver functions (RFs) that reduces the uncertainty of slab dip direction estimation from tens to several degrees. DDS can also resolve the thickness and depth of a dipping structure. We then apply DDS to the RFs in the Sumatran subduction zone. Travel time differences of the converted phases from the upper and lower (oceanic Moho) boundaries of the dipping low‐velocity layer (LVL) along the plate interface show a thickness of 10–14 km. The results also show increased dip direction of the slab Moho from 47 ± 5.3° in southern Sumatra to 70 ± 10.7° in northern Sumatra, indicating a complicated slab geometry and internal deformation along strike. Similar dip directions are obtained for the upper and lower LVL boundaries beneath Nias and Enggano forearc islands in the north and south, whereas we find a larger discrepancy of ∼14–23° beneath Siberut and Pagai in between. The thicker LVL with a non‐negligible difference in the dip directions of its upper and lower bounds in the center of Sumatra is interpreted as a partially serpentinized mantle layer above the oceanic crust, forming a distinct channel atop the subducting slab. Our results provide basic observational constraints on the structure and geometry of the oceanic slab and associated subduction processes. Both synthetics and data analyses also indicate DDS can be applied in other subduction zones and for other dipping interfaces.

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“…Recently, researchers have begun to realize the potential of combining the two methods. Zhang et al (2019) reconstructed RFs with PCA to gain a more accessible analysis to the back azimuthal behaviors and the phases. Yang et al (2022) designed a convolutional neural network to predict the shear wave velocity.…”

Section: Introductionmentioning

confidence: 99%

A multi-task deep learning scheme using receiver functions to study crustal tectonics and its application to the middle-southern segment of Tanlu Fault Zone and adjacencies

Chen

Wang

et al. 2022

Preprint

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We propose a novel scheme that applies a multitasking convolutional neural network to learn the back azimuthal behavior from receiver function seismograms, which can effectively predict the depth and occurrence of the Moho beneath a single seismic station. Our scheme consists of three main steps: 1. Based on the style transfer technique, we generate 9000 synthetic receiver function seismograms blended by realistic noise as training data sets. 2. A multitasking convolutional neural network is trained to predict the depth and occurrence of the Moho. 3. All real receiver function seismograms are reconstructed by the accelerated joint iterative method before prediction. We apply the scheme to study the middle-southern of the Tanlu fault zone and adjacent regions and successfully achieve the depth and occurrence of the Moho beneath 10 permanent seismic stations. The predicted depths are in agreement with the results computed by conventional methods, and the predicted strikes and dip angles present an undulating Moho with near NE-striking. Moreover, the predicted strikes are nearly consistent with the strikes of the normal faults in the upper crust, which implies that intense continental extension in the Cretaceous play a prominent role in the tectonic deformation of the brittle upper crust and the ductile lower crust simultaneously. Besides, it helps to illustrate that the stress field orientation of the major geological event can be recorded and preserved in the lower crust.

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Crustal structure study based on principal component analysis of receiver functions

Cited by 8 publications

References 24 publications

Joint Inversion of Receiver Functions and Apparent Incidence Angles to Determine the Crustal Structure of Mars

Joint Inversion of Receiver Functions and Apparent Incidence Angles to Determine the Crustal Structure of Mars

A New Method to Estimate Slab Dip Direction Using Receiver Functions and Its Application in Revealing Slab Geometry and a Diffuse Plate Boundary Beneath Sumatra

A multi-task deep learning scheme using receiver functions to study crustal tectonics and its application to the middle-southern segment of Tanlu Fault Zone and adjacencies

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