Reza Esfahani
@gfz-potsdam.de
RESEARCH, TEACHING, or OTHER INTERESTS
Geophysics, Geotechnical Engineering and Engineering Geology, Signal Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Reza D. D. Esfahani, Fabrice Cotton, Matthias Ohrnberger, and Frank Scherbaum
Seismological Society of America (SSA)
ABSTRACT Despite the exponential growth of the amount of ground-motion data, ground-motion records are not always available for all distances, magnitudes, and site conditions cases. Given the importance of using time histories for earthquake engineering (e.g., nonlinear dynamic analysis), simulations of time histories are therefore required. In this study, we present a model for simulating nonstationary ground-motion recordings, which combines a conditional generative adversarial network to predict the amplitude part of the time–frequency representation (TFR) of ground-motion recordings and a phase retrieval method. This model simulates the amplitude and frequency contents of ground-motion data in the TFR as a function of earthquake moment magnitude, source to site distance, site average shear-wave velocity, and a random vector called a latent space. After generating the phaseless amplitude of the TFR, the phase of the TFR is estimated by minimizing all differences between the observed and reconstructed spectrograms. The simulated accelerograms produced by the proposed method show similar characteristics to conventional ground-motion models in terms of their mean values and standard deviations for peak ground accelerations and Fourier amplitude spectral values.
Reza Dokht Dolatabadi Esfahani, Kristin Vogel, Fabrice Cotton, Matthias Ohrnberger, Frank Scherbaum, and Marius Kriegerowski
Seismological Society of America (SSA)
ABSTRACT In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.
Reza Dokht Dolatabadi Esfahani, Ali Gholami, and Matthias Ohrnberger
Society of Exploration Geophysicists
Dispersion-curve inversion of Rayleigh waves to infer subsurface shear-wave velocity is a long-standing problem in seismology. Due to nonlinearity and ill-posedness, sophisticated regularization techniques are required to solve the problem for a stable velocity model. We have formulated the problem as a minimization problem with nonlinear operator constraint and then solve it by using an inexact augmented Lagrangian method, taking advantage of the Haney-Tsai Dix-type relation (a global linear approximation of the nonlinear forward operator). This replaces the original regularized nonlinear problem with iterative minimization of a more tractable regularized linear problem followed by a nonlinear update of the phase velocity (data) in which the update can be performed accurately with any forward modeling engine, for example, the finite-element method. The algorithm allows discretizing the medium with thin layers (for the finite-element method) and thus omitting the layer thicknesses from the unknowns and also allows incorporating arbitrary regularizations to shape the desired velocity model. In this research, we use total variation regularization to retrieve the shear-wave velocity model. We use two synthetic and two real data examples to illustrate the performance of the inversion algorithm with total variation regularization. We find that the method is fast and stable, and it converges to the solution of the original nonlinear problem.
Jer-Yu Jeng, Roohollah Askari, Snehamoy Chatterjee, and Reza Dolatabadi
Society of Exploration Geophysicists
Reza Dokht Dolatabadi Esfahani, Roohollah Askari, and Ali Gholami
Society of Exploration Geophysicists
Group velocity is an important characteristic of surface wave that is defined as the velocity of an envelope of frequencies. Although many studies have shown the promises of analyzing the group velocity to obtain subsurface S-wave velocity, the estimation of the group velocity is not straightforward due to the uncertainties of selecting an optimum envelope of frequencies. Conventional transformations or filtering algorithms used to define an optimum envelope usually give reasonable results just for a narrow frequency or velocity range. We introduced a new approach for the estimation of the group velocity using the sparse S transform (SST) and sparse linear Radon transform (SLRT). In SST, the width of the Gaussian window is optimally calculated by energy concentration to eliminate energy smearing in the time-frequency (TF) domain, and then the sparsity is applied to enhance the TF resolution. Compared with conventional methods for the estimation of the group velocity based on the generalized S transforms, SST does not require any adjustment to the Gaussian window and yields accurate estimates of the group velocity. We apply SST to each seismic trace of a seismic shot record to obtain a 3D cube of frequency, time, and offset. For any frequency, we obtain a common frequency gather of time and offset to which we apply SLRT to obtain the group velocity of the surface wave. Our approach is robust at calculating high-resolution distinguishable dispersion curves of the group velocity in particular when data are extremely sparse.
Jer-Yu Jeng, Roohollah Askari, Snehamoy Chatterjee, and Reza Dolatabadi
Society of Exploration Geophysicists
Reza Dolatabadi, Ali Gholami, and Roohollah Askari
Society of Exploration Geophysicists