We present forward and adjoint spectral-element simulations of coupled acoustic (an)elastic seismic wave propagation on fully unstructured hexahedral meshes. Simulations benefit from recent advances in meshing, load balancing software optimization. Meshing may be accomplished using a mesh generation tool kit such as CUBIT, is facilitated by graph partitioning based the SCOTCH library. Coupling between fluid solid regions incorporated straightforward fashion domain decomposition. Topography,...

We present the first-generation global tomographic model constructed based on adjoint tomography, an iterative full-waveform inversion technique. Synthetic seismograms were calculated using GPU-accelerated spectral-element simulations of seismic wave propagation, accommodating effects due to 3-D anelastic crust & mantle structure, topography bathymetry, ocean load, ellipticity, rotation, and self-gravitation. Fréchet derivatives in models adjoint-state method. The performed Cray XK7 named...

SUMMARY Building on global adjoint tomography model GLAD-M15, we present transversely isotropic GLAD-M25, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting 1480 events in magnitude range 5.5 ≤ Mw 7.2, almost sixfold increase over first-generation model. We calculated fully 3-D synthetic seismograms a shortest period 17 s based GPU-accelerated spectral-element wave propagation solver accommodates effects due to anelastic crust and mantle...

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging acquire field with an appropriate signal-to-noise ratio in the low-frequency part spectrum. We have extrapolated from respective higher frequency components wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. numerically simulate marine...

SUMMARY We determine finite-frequency sensitivity kernels for seismic interferometry based upon noise cross-correlation measurements. Under the assumptions that is spatially uncorrelated but non-uniform, we ensemble-averaged cross correlations between synthetic seismograms at two geographically distinct locations. By minimizing a measure of difference observed and simulated ensemble correlations—subject to constraint wavefield satisfies wave equation—we obtain kernels. These reflect...

Building realistic and reliable models of the subsurface is primary goal seismic imaging. We have constructed an ensemble convolutional neural networks (CNNs) to build velocity directly from data. Most other approaches attempt map full data into 2D labels. exploit regularity acquisition train CNNs gathers neighboring common midpoints (CMPs) vertical 1D logs. This allows us integrate well-log inversion, simplify mapping by using labels, accommodate larger dips relative single CMP inputs....

We introduce a technique to compute exact anelastic sensitivity kernels in the time domain using parsimonious disk storage. The method is based on reordering of loop time-domain forward/adjoint wave propagation solvers combined with use memory buffer. It avoids instabilities that occur when time-reversing dissipative simulations. total number required steps unchanged compared usual acoustic or elastic approaches. cost reduced by factor 4/3 case which anelasticity partially accounted for...

We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of plethora deep learning frameworks, algorithms, libraries, and techniques. The key idea behind Deep500 is its modular design, where factorized into four distinct levels: operators, network processing, training, distributed training. Our evaluation illustrates (enables combining different codes) (uses carefully selected metrics). Moreover, fast (incurs negligible overheads), verifiable...

Low-frequency signal content in seismic data as well a realistic initial model are key ingredients for robust and efficient full-waveform inversions. However, acquiring low-frequency is challenging practice active surveys. Data-driven solutions show promise to extrapolate given high-frequency counterpart. While being established synthetic acoustic examples, the application of bandwidth extrapolation field datasets remains non-trivial. Rather than aiming reach superior accuracy extrapolation,...

We have developed a near real-time system for the simulation of global earthquakes. Prompted by trigger from Global Centroid Moment Tensor (CMT) Project, automatically calculates normal-mode synthetic seismograms Preliminary Reference Earth Model, and spectral-element 3-D mantle model S362ANI in combination with crustal Crust2.0. The 1-D synthetics more than 1800 seismographic stations operated members international Federation Digital Seismograph Networks are made available via internet...

SUMMARY We present our third and final generation joint P S global adjoint tomography (GLAD) model, GLAD-M35, quantify its uncertainty based on a low-rank approximation of the inverse Hessian. Starting from second-generation GLAD-M25, we added 680 new earthquakes to database for total 2160 events. New P-wave categories are included compensate imbalance between P- S-wave measurements, enhanced window selection algorithm include more major-arc phases, providing better constraints structure...

Closures of the Iapetus Ocean and Tornquist Sea lead to collision paleocontinents Laurentia, Baltica Eastern Avalonia during Caledonian orogeny. It has been speculated that relicts these two closures may be preserved within crust or upper mantle. Over past decades, numerous wide‐angle seismic profiles were gathered in northwestern Europe search for related subsurface features. Although active source studies revealed detailed crustal structures across suture zones, there are relatively few...

Least-squares reverse time migration (LSRTM) is an iterative inversion algorithm for estimating the broadband-wavenumber reflectivity model. Although it produces superior results compared with conventional (RTM), LSRTM computationally expensive. We have developed a one-step method by considering demigrated and observed data to design deblurring preconditioner in domain using Wiener filter. For filtering, we further use stabilized division via Taylor expansion. The preconditioned are then...

Abstract We adopt a spectral‐element method (SEM) to perform numerical simulations of the complex wavefield generated by 6 April 2009 M w  6.3 L’Aquila earthquake in central Italy. The mainshock is represented finite‐fault solution obtained inverting strong‐motion and Global Positioning System data, testing both 1D 3D wavespeed models for Surface topography, attenuation, Moho discontinuity are also accommodated. Including these complexities essential accurately simulate seismic‐wave...

Summary Full-waveform inversion (FWI) benefits in many ways from having low-frequency data. However, those are rarely available due to acquisition limitations. Here, we explore the feasibility of frequency-bandwidth extrapolation using an Artificial Neural Network (ANN) approach. The ANN is trained be a non-linear operator that maps high-frequency data for single source and multiple receivers Assuming point (delta function) both time space, train network on synthetic generated random...

In our first paper (Part 1) about the square-root variable metric (SRVM) method we presented basic theory and validation of inverse algorithm applicable to large-scale seismic data inversions. this second 2) SRVM method, objective is estimate resolution uncertainty inverted resulting geophysical model. Bayesian inference allows estimating posterior model distribution from its prior likelihood function. These distributions, when being linear Gaussian, can be mathematically characterized by...

Full Waveform Inversion (FWI) plays a vital role in reconstructing geophysical structures. The Uncertainty Quantification regarding the inversion results is equally important but has been missing out most of current inversions. Mathematically, uncertainty quantification involved with inverse Hessian (or posterior covariance matrix), which prohibitive computation and storage for practical FWI problems. L-BFGS populates as efficient Gauss-Newton method; however, this study, we empower it new...

Computational seismology is an area of wide sociological and economic impact, ranging from earthquake risk assessment to subsurface imaging oil gas exploration. At the core these simulations modeling wave propagation in a complex medium. Here we report on extension high-order finite-element seismic simulation package SPECFEM3D support largest scale hybrid homogeneous supercomputers. Starting existing highly tuned MPI code, migrated CUDA version. In order be immediate impact science mission...

We present an artificial neural network based approach for robust event detection from low S/N waveforms. use a feed-forward with single hidden layer that is tuned on training dataset and later applied the entire example detection. The input features used include average of absolute amplitudes, variance, energy-ratio polarization rectilinearity. These are calculated in moving-window same length waveform. output set as user-specified relative probability curve, which provides way...

We present a computational framework for the assimilation of local to global seismic data into consistent model describing Earth structure on all seismically accessible scales. This Collaborative Seismic Model (CSEM) is designed meet following requirements: (i) Flexible geometric parametrization, capable capturing topography and bathymetry, as well aspects potentially resolvable structure, including small-scale heterogeneities deformations internal discontinuities. (ii) Independence any...

We implement the wave equation on a spherical membrane, with finite-difference algorithm that accounts for finite-frequency effects in smooth-Earth approximation, and use resulting 'membrane waves' as an analogue surface propagation Earth. In this formulation, we derive fully numerical 2-D sensitivity kernels phase anomaly measurements, employ them preliminary tomographic application. To speed up computation of kernels, so it is practical to formulate inverse problem also respect laterally...

Abstract We use a large data set of 3D thermal evolution models to predict the distribution present‐day seismic velocities in Martian interior. Our show difference between maximum and minimum S wave velocity up 10% either below crust, where variations are largest, or at depth olivine wadsleyite phase transition, located around 1,000–1,200 km depth. Models with thick lithospheres on average have weak low‐velocity zones that extend deeper than 400 uppermost 400–600 closely follow crustal...

In complex acoustic or elastic media, finite element meshes often require regions of refinement to honour external internal topography, small-scale features. These localized smaller elements create a bottleneck for explicit time-stepping schemes due the Courant-Friedrichs-Lewy stability condition. Recently developed local time stepping (LTS) algorithms reduce impact these small by locally adapting time-step size element. The recursive, multi-level nature our LTS scheme introduces an...

When present in the subsurface, salt bodies impact complexity of wave-equation-based seismic imaging techniques, such as least-squares reverse time migration and full-waveform inversion (FWI). Typically, Born approximation used every iteration least-squares-based inversions is incapable handling sharp, high-contrast boundaries bodies. We have developed a variance-based method for reconstruction velocity models to resolve issues caused by Our main idea lies retrieving useful information from...