The Anisotropy Team (A-Team), under the direction of Dr. Ilya Tsvankin, is focused on modeling, inversion, and imaging of seismic reflection and borehole data from anisotropic media. The group also works on seismic fracture characterization and collaborates with the Reservoir Characterization Project (RCP) at Mines on time-lapse monitoring of unconventional reservoirs using multicomponent data. A growing part of the A-Team research portfolio is full-waveform inversion (FWI) for realistic subsurface models with transversely isotropic (TI) and orthorhombic symmetry.

Our Team

Ahmed Ahmed

Ahmed Ahmed

Maksat Jazbay


Ashish Kumar

Ilya Tsvankin

The Problems We Solve

How can we obtain high-resolution seismic images of a fractured formation?


High-resolution orthorhombic velocity models obtained by elastic full-waveform inversion

Estimation of the parameters of azimuthally anisotropic media (e.g., orthorhombic) poses serious challenges, one of which is the large computational cost of processing and inversion of 3D wide-azimuth data. Full-waveform inversion (FWI) of surface seismic data for elastic orthorhombic media also suffers from parameter trade-offs that cannot be overcome without constraining the model-updating procedure. We have developed an FWI methodology that incorporates geologic constraints to reduce the inversion nonlinearity and enhance the resolution of parameter estimation. These constraints are obtained from well logs, which can provide rock-physics relationships for different geologic facies. Because the locations of the available well logs are usually sparse, a machine-learning (ML) algorithm is employed to account for lateral heterogeneity in building the lithologic constraints. The top figure on the right is the P-wave vertical velocity estimated by unconstrained FWI for an orthorhombic model, and on the bottom is the FWI result obtained using facies-based constraints (Singh et al., 2020). The developed algorithm achieves a much higher spatial resolution of several inverted parameters compared to unconstrained FWI, even in the absence of recorded frequencies below 2 Hz.

How can we reconstruct time-lapse parameter variations with a distorted source wavelet?


Source-independent methodology incorporated into the time-lapse FWI workflow for VTI media

Full-waveform inversion (FWI) requires an accurate estimate of the source wavelet. However, reconstruction of the source wavelet from field data is often problematic. The non-repeatability of source signals in time-lapse surveys makes the source-wavelet estimation even more challenging. To address this problem, we incorporate a source-independent methodology into the time-lapse FWI workflow for VTI (transversely isotropic with a vertical symmetry axis) media. The figure shows that our algorithm makes it possible to reconstruct the time-lapse variations in the P-wave vertical velocity even using a substantially distorted source wavelet, and the achieved spatial resolution is close to that obtained with the actual wavelet. 

How can we efficiently simulate full wavefields for large-scale 3D anisotropic models?


Finite-difference modeling using mimetic operators with Full Staggered Grids

Modeling elastic wavefield solutions for large-scale 3D heterogeneous anisotropic media is a computationally challenging task. Techniques like full-waveform inversion and least-squares reverse-time migration require efficient simulation of full-wavefield solutions to produce subsurface images. Graphical Processing Units (GPUs) are more suitable for tasks which require intensive computation, high concurrency and a large volume of data than conventional CPUs. We have developed a finite-difference method using mimetic operators with Full Staggered Grids (FSGs), which accurately simulate wavefields for large-scale 3D models in a heterogeneous computing environment including both CPUs and GPUs. Top figure: Full Staggered Grid (FSG) cell for a 3D anisotropic medium. Bottom figure: Seismic wave propagation in an orthorhombic medium.

Some Recently Completed Projects

  • Wave-equation migration velocity analysis for acoustic TI models
  • Estimation of attenuation anisotropy by FWI of reflection data
  • Anisotropic diffraction-based processing and velocity analysis
  • Multicomponent stacking-velocity tomography for tilted orthorhombic media

The Techniques and Tools We Use


Seismic Anisotropy

Elastic and acoustic reverse-time migration (RTM)

Microseismic imaging, inversion, and monitoring

Velocity analysis and model-building

Elastic and acoustic full-waveform inversion (FWI)

Machine learning

Diffraction-based Processing

3D/4D Elastic anisotropic modeling

Time-lapse seismic including FWI

Our Collaborators


The A-Team works with several research groups around the globe including:

King Abdullah University of Science and Technology (Saudi Arabia)

Curtin University (Australia)

Free University of Berlin (Germany)

Join the A-Team

Mention the A-Team and CWP in your statement of interest when you apply for the graduate program.