Three-dimensional frequency-domain full waveform inversion based on the nearly-analytic discrete method

The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.This study applies the NAD method to solving three-dimensional(3D)acoustic wave equations in the frequency-domain.This forward modeling approach is then used as the“engine”for implementing 3D frequency-domain full waveform inversion(FWI).In the numerical modeling experiments,synthetic examples are first given to show the superiority of the NAD method in forward modeling compared with traditional finite difference methods.Synthetic 3D frequency-domain FWI experiments are then carried out to examine the effectiveness of the proposed methods.The inversion results show that the NAD method is more suitable than traditional methods,in terms of computational cost and stability,for 3D frequency-domain FWI,and represents an effective approach for inversion of subsurface model structures.

THE WASSERSTEIN-FISHER-RAO METRIC FOR WAVEFORM BASED EARTHQUAKE LOCATION

In this paper,we apply the Wasserstein-Fisher-Rao(WFR)metric from the unbalanced optimal transport theory to the earthquake location problem.Compared with the quadratic Wasserstein(W2)metric from the classical optimal transport theory,the advantage of this method is that it retains the important amplitude information as a new constraint,which avoids the problem of the degeneration of the optimization objective function near the real earthquake hypocenter and origin time.As a result,the deviation of the global minimum of the optimization objective function based on the WFR metric from the true solution can be much smaller than the results based on the W2 metric when there exists strong data noise.Thus,we develop an accurate earthquake location method under strong data noise.Many numerical experiments verify our conclusions.

A wave propagation model with the Biot and the fractional viscoelastic mechanisms

Energy loss in porous media containing fluids is typically caused by a variety of dynamic mechanisms.In the Biot theory,energy loss only includes the frictional dissipation between the solid phase and the fluid phase,resulting in underestimation of the dispersion and attenuation of the waves in the low frequency range.To develop a dynamic model that can predict the high dispersion and strong attenuation of waves at the seismic band,we introduce viscoelasticity into the Biot model and use fractional derivatives to describe the viscoelastic mechanism,and finally propose a new wave propagation model.Unlike the Biot model,the proposed model includes the intrinsic dissipation of the solid frame.We investigate the effects of the fractional order parameters on the dispersion and attenuation of the P-and S-waves using several numerical experiments.Furthermore,we use several groups of experimental data from different fluid-saturated rocks to testify the validity of the new model.The results demonstrate that the new model provides more accurate predictions of high dispersion and strong attenuation of different waves in the low frequency range.

Non-line-of-sight reconstruction with signal–object collaborative regularization

Non-line-of-sight imaging aims at recovering obscured objects from multiple scattered lights.It has recently received widespread attention due to its potential applications,such as autonomous driving,rescue operations,and remote sensing.However,in cases with high measurement noise,obtaining high-quality reconstructions remains a challenging task.In this work,we establish a unified regularization framework,which can be tailored for different scenarios,including indoor and outdoor scenes with substantial background noise under both confocal and non-confocal settings.The proposed regularization framework incorporates sparseness and non-local self-similarity of the hidden objects as well as the smoothness of the signals.We show that the estimated signals,albedo,and surface normal of the hidden objects can be reconstructed robustly even with high measurement noise under the proposed framework.Reconstruction results on synthetic and experimental data show that our approach recovers the hidden objects faithfully and outperforms state-of-the-art reconstruction algorithms in terms of both quantitative criteria and visual quality.