An investigation on slowness‐weighted CCP stacking and its application to receiver function imaging

Guan, Zhe; Niu, Fenglin

doi:10.1002/2017gl073755

Cited by 25 publications

(13 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the SWCCP stacking approach (Guan & Niu, 2017), we successfully removed most of the multiples that appeared at the MTZ depths in the CCP stacked profiles. The real seismic discontinuities have been thus clearly identified (Figures 4c and 4d).…”

Section: Discussionmentioning

confidence: 99%

“…The D660 is relatively easier to pick but it shows clearly different depth variations along two profiles. To reduce the influence of the multiples in our CCP profiles, we performed the slowness weighted CCP (SWCCP) stacking method (Guan & Niu, 2017). A slowness‐based factor is designed to weight RF amplitudes along the CCP profile mainly according to the difference between the relative slowness of the slant‐stacked amplitude and the theoretical slowness of the direct Ps conversion (e.g., Figure 3c) (see Text in supporting information for more details).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mantle Transition Zone Structure Beneath Myanmar and Its Geodynamic Implications

Bai

Yuan

et al. 2020

Geochem Geophys Geosyst

View full text Add to dashboard Cite

Following the closure of the Neo-Tethys ocean, the Indian plate began to collide with the Eurasian plate in the Eocene (e.g., Molnar & Stock, 2009). One of the profound consequences is the formation of the Himalayan mountain belt and the Tibetan plateau. The convergent boundary extends along the Himalayan arc and is terminated to the east at the eastern Himalayan syntaxis, where it bends to the south, continues along the Burma arc, and connects to the Andaman-Sumatra arc, a site of oceanic subduction. While many seismic experiments have demonstrated that the Indian lithosphere is underthrusting northwards under Tibet (e.g., Kind et al., 2002; Nábělek et al., 2009), the detailed subduction geometry

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mantle Transition Zone Structure Beneath Myanmar and Its Geodynamic Implications

Bai

Yuan

et al. 2020

Geochem Geophys Geosyst

View full text Add to dashboard Cite

show abstract

“…These existing methods have been applied in many different cases, but their applications also have many restrictions. Most of these methods are too sensitive to noise (Guan & Niu, ; Mousavi & Langston, , , ; Mousavi et al, ).…”

Section: Introductionmentioning

confidence: 99%

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Chen

Zhang

Bai

et al. 2019

Earth and Space Science

Self Cite

View full text Add to dashboard Cite

Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.

show abstract

“…We note also that various approaches have been proposed to filter out the different contributions from the different modes directly on the raw data/RFs (e.g. Guan & Niu 2017;Ainiwaer & Gurrola 2017).…”

Section: Common Reflection Point Stacking For Vertical Autocorrelationsmentioning

confidence: 99%

Receiver functions from seismic interferometry: a practical guide

Tauzin

Phạm

Tkalčić

2019

Geophysical Journal International

View full text Add to dashboard Cite

This paper reviews the concepts underlying the well-documented receiver functions (RFs) method, and places it in the conceptual framework of seismic interferometry. We first propose a simple and efficient approach for isolating the receiver-side seismic response (i.e. the record of reflections and conversions in the stratification beneath receivers): this method makes use of the P-wave coda recorded on the radial and vertical components of three-component stations, applies spectral whitening, which is followed by auto-and cross-correlation. The interferometric principle underpinning RFs analysis is shown theoretically and illustrated in practice using earthquake records and synthetic waveforms computed from simple structures. We point out to a major limitation, which is the contamination of the receiver-side response by propagation effects in the source-side structure. We then apply our approach to teleseismic earthquake data recorded in California. We show that the reconstructed vertical and horizontal seismic responses can be back-projected to illuminate the crustal and mantle structure. We build comparable ∼300-km-long seismic reflectivity profiles from pure P-wave reverberations and from the converted wavefield across the forearc and arc of the southern Cascadia subduction zone. Then, we show a case of processing data from narrow bandpass, short-period and single-component sensors, usually unsuitable for RFs analysis. Finally, through the same interferometric principle, we attempt to demonstrate a link between event-and noise-based seismic interferometry. We demonstrate that it is possible to extract approximate responses from the records of low-magnitude-down to 4.5-teleseismic earthquakes. We make a comparison of these estimates with the result from the autocorrelation of the continuous ambient noise seismic wavefield. While the amplitudes of the extracted receiver-side responses are mutually different, their phases are in a relative agreement. This development opens a way to the use of small magnitude teleseismic earthquakes to characterize the receiver-side structure.

show abstract

An investigation on slowness‐weighted CCP stacking and its application to receiver function imaging

Cited by 25 publications

References 39 publications

Mantle Transition Zone Structure Beneath Myanmar and Its Geodynamic Implications

Mantle Transition Zone Structure Beneath Myanmar and Its Geodynamic Implications

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Receiver functions from seismic interferometry: a practical guide

Contact Info

Product

Resources

About