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Modeling the Crust and Upper Mantle Applying an Optimization Method to Multiple Datasets: Surface Wave Dispersion, P-Receiver Function, and S-Waveform

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Abstract

We develop an original algorithm for velocity estimation that incorporates the constraints of three seismic functionals: surface wave dispersion, P-receiver function, and S-waveform. Here, we use a parallelized reflectivity algorithm to generate synthetic seismograms and match the observed functionals by a global optimization scheme called very fast simulated annealing (VFSA). This method also allows us to assess the uniqueness and parameter independence of the resultant models. Synthetic tests using surface wave (SW) dispersion and receiver functions (RF), and then SW, RF, and waveforms windowed around the S arrival, establish that inclusion of the third functional results in the best recovery of the model. We employ the algorithm to model the Kachchh basin in Gujarat, India, because the area is of active interest for monitoring purposes, and prior results of RF and SW modeling are available for comparison. The waveform functionals used here are generated from broadband seismograms of teleseismic, deep (396–609 km), and moderate- to large-magnitude (6–6.8) earthquake events recorded at semipermanent seismograph stations in the Kachchh basin. Joint inversion of SW, RF, and S-waveform improves the velocity structure by revealing layers that were not identified in previous modeling that used SW and RF alone. Our model clearly shows low-velocity zones (LVZs), crustal and lithospheric thinning, and the lithosphere–asthenosphere boundary. We estimate the crustal thickness to be 41 km at all but one station, and the lithosphere to be in the range of 70–80 km. P- and S-velocities from the uppermost crust to the Moho vary from 4.7 to 7.0 km/s, and 2.7–4.1 km/s, respectively.

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Data Availability

Broadband waveforms utilized in this work were acquired as a component of the aftershock monitoring of the 2001 Mw 7.7 Bhuj earthquake by the National Geophysical Research Institute, Hyderabad, India, using instruments obtained under the Department of Science and Technology, New Delhi, India, sponsored project. Data are available by sending a request to the Director, National Geophysical Research Institute (NGRI), Council of Scientific and Industrial Research, Hyderabad, India, or by sending an email to director@ngri.res.in.

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Acknowledgements

The authors are thankful to the Director, CSIR-NGRI, Hyderabad, India, for his permission to publish this work. The data used in this paper were presented previously in Ghosh, Ranjana, Mrinal K. Sen, Prantik Mandal, Jay Pulliam, and Mohit Agrawal, "Seismic Velocity Assessment In The Kachchh Region, India, From Multiple Waveform Functionals," in AGU Fall Meeting Abstracts, vol. 2014, pp. S51B-4460, 2014. We are thankful to two anonymous reviewers for their comments and suggestions to revise the manuscript.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. CSIR-National Geophysical Research Institute, Hyderabad, India

    Ranjana Ghosh & Prantik Mandal

  2. Department of Geological Sciences and Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, USA

    Mrinal K. Sen

  3. Department of Geology, Baylor University, Waco, USA

    Jay Pulliam

  4. School of Engineering, University of Alaska, Anchorage, USA

    Utpal Dutta

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  1. Ranjana Ghosh
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RG, MKS, PM, JP and UD. The first draft of the manuscript was written by RG, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ranjana Ghosh.

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Appendices

Appendix A

See Fig. 17

Fig. 17
figure 17

A pseudo code for the VFSA algorithm (Ghosh & Ojha, 2020)

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Appendix B: Uncertainty Estimations of the Synthetic Tests

The mean, variance, and PPD of S-wave velocity obtained from joint inversion using both (a) SW and RF and (b) SW, RF and S-waveform are shown in Fig. 18 for mean and variance and in Fig. 19 for posterior probability density functions (Fig. 20).

Fig. 18
figure 18

Mean and variance of inverted S-wave velocity obtained by inversion using SW and RF (a), and SW, RF, and S-waveform (b)

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Fig. 19
figure 19

PPDs of inverted S-wave velocity obtained by inversion using SW and RF (a), and SW, RF, and S-waveform (b)

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Fig. 20
figure 20

Relative error of the inverted S-wave velocity obtained by inversion using SW and RF (blue), and SW, RF, and S-waveform (red) with respect to the true model. No. of layers is the total number of layers of the model, which here is 23

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Appendix C: Uncertainty Estimations of the Rest of the Stations

The PPDs of S-wave velocity, correlation matrices, and the particle motion of the corresponding arrivals at stations BCH, VJP, TPR, and BHU are shown in Figs. 21, 22, and 23, respectively.

Fig. 21
figure 21

PPD of S-wave velocity (a, d, g), parameter correlation matrix (b, e, h), and particle motion (c, f, i) of the observed (blue in the online edition) and the synthetic (red in the online edition) waveform for all three events modeled for station BCH. The parameter correlation matrix is symmetrical, so only the bottom half is shown. Parameter number refers, sequentially, to Vp/Vs, Vs, layer thickness, and density for each of 23 layers. Autocorrelations (on the diagonal) are unity

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Fig. 22
figure 22

Corresponding PPD distribution of S-wave velocity (a, d, g), correlation matrix of parameters (b, e, h) obtained from the inversion, and particle motion (c, f, i) of the observed (blue in the online edition) and the synthetic (red in the online edition) waveform for all events at station VJP

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Fig. 23
figure 23

Corresponding PPD distribution of S-wave velocity (a, d, g), correlation matrix of parameters (b, e, h) obtained from the inversion, and particle motion (c, f, i) of the observed (blue in online edition) and the synthetic (red in the online edition) waveform for all events at station TPR and BHU

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Ghosh, R., Sen, M.K., Mandal, P. et al. Modeling the Crust and Upper Mantle Applying an Optimization Method to Multiple Datasets: Surface Wave Dispersion, P-Receiver Function, and S-Waveform. Pure Appl. Geophys. 180, 879–908 (2023). https://doi.org/10.1007/s00024-023-03236-8

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