SINBAD Consortium

SINBAD is an industry supported research consortium with 10 member companies that have members only unrestricted access to our research findings.

Summary: Seismic Imaging by Next-Generation Basis Functions Decomposition (SINBAD) is an industry-supported (10 oil & gas majors and contractor companies) research consortium that is pushing the envelope on seismic data acquisition, processing, and wave-equation based imaging and inversion by adapting recent developments from compressive sensing and machine learning. By incorporating ideas from these research areas into seismic workflows, we offer innovative sampling & inversion strategies where costs are no longer dominated by overly pessimistic sampling criteria and where the inversion results are less sensitive to initial models and parameter settings. By removing our insistence to collect and process all data, we are also in a better position for quality control and uncertainty analysis.

Overview

As conventional oil and gas fields are maturing, our profession is challenged to come up with the next-generation of more and more sophisticated exploration tools. In exploration seismology this trend has let to the emergence of wave-equation based inversion technologies such as reverse-time migration and full-waveform inversion that rely heavily on full-azimuth, and now also far offset, acquisitions. This reliance exposes vulnerabilities in acquisition and processing given the exponential growth in size of acquired seismic data volumes.

While significant progress has been made in wave-equation based inversion, major challenges remain in the development of robust and computationally feasible workflows that give reliable results in geophysically challenging areas that may include ultra-low shear velocity zones or high-velocity salt. Moreover, sub-salt production carries risks that needs mitigation, which raises the bar from creating sub-salt images to inverting for sub-salt overpressure.

Amongst the many challenges that wave-equation based inversion faces—see the mind map, outlining the SINBAD/DNOISE research program at UBC—we focus on reducing the excessive

  • sampling-related costs associated with wave-equation based inversions that call for full-azimuth and long-offset acquisitions.

  • computational costs of wave-equation based inversion such as Estimation of Primaries by Sparse Inversion (EPSI), Reverse-Time Migration (RTM), Wave-equation based Migration Velocity Analysis (WEMVA), and Full-Waveform Inversion (FWI).

We accomplish these cost reductions by using modern techniques from machine learning and compressive sensing. Contrary to prevailing practices, we propose

  • acquisition schemes where data is acquired with simultaneous sources—via multiple time-jittered simultaneous sources in marine settings or via phase-encoding on land. This leads to denser samplings and hence better performance of wave-equation based inversion technology.

  • to not insist on using all data—i.e., looping over all sources, to calculate the model updates. Instead, we rely on formulations that only call for more data (and possibly more accuracy) as needed by the inversion.

This new strategy can lead to major savings in acquisition and processing costs allowing for improved data coverage and inversion results. Since this approach reduces the computational costs significantly, we open the way to test different scenarios or to include more sophisticated regularization. Without this cost reduction, these would both be computationally infeasible.

Aside from making seismic work flows more feasible, we have and continue to work on novel formulations of nonlinear wave-equation based inversions that are less prone to local minima. This has resulted in completely new formulations for WEMVA, based on matrix probing, and of imaging and FWI, based on the penalty method. Contrary to adjoint-state methods, which undergird RTM and FWI, the penalty method does not involve the solution of reverse-time (adjoint) wave equation, dividing the computational costs by half, and also requires less accurate initial models for FWI to converge.

Since its inception, SINBAD has enjoyed widespread interest and uptake by industry of its technology ranging from the use of curvelets during adaptive subtraction in Surface-related Multiple Removal (SRME) to denoising, randomized sampling techniques in land and marine acquisition, and robust data-reduction techniques in FWI.

Our research has seen a considerable uptake1 by industry including

  • curvelet-based denoising and missing-trace interpolation

  • curvelet-based adaptive subtraction part of primary-multiple separation

  • seismic data acquisition using compressive sensing including randomized marine & land acquisition, and simultaneous marine acquisition

  • estimation of primaries by sparse inversion

  • imaging with surface-related multiples

  • efficient (via batching) and robust full-waveform inversion

  • full-waveform inversion with compressive sensing

  • widespread adaption of SPGl1 by industry as the state-of-art large-scale one-norm solver


Joining the SINBAD Consortium

Over the last 3 years, SINBAD has grown from 5 to 10 companies. In addition to being supported by oil & gas majors (BG Group, Chevron, ConocoPhillips, Hess, Petrobras, Woodside), SINBAD is also sponsored by major contractor companies (DownUnder GeoSolutions, PGS, Schlumberger, Sub Salt Solutions). We invite parties with an interest in exploration seismology to become members of our Industrial Research Consortium: Seismic Imaging by Next-Generation Basis Functions Decomposition (SINBAD) is active in a wide range of research areas (see our mind map) including

Fee structure

The SINBAD Consortium is financed by our industry Consortium members, who sponsor our research. New sponsors gain access to IP and software developed since the inception of SINBAD. All sponsors have representation in the Steering Committee. Subject to the University’s compliance with the terms of this Agreement, the Participant shall pay the University funds in accordance with the schedule below (all funds in US dollars):

  • OIL & GAS COMPANIES: effective Jan 1, 2017, new sponsoring companies pay $78,500 USD per annum due on the Effective Date and on each anniversary of the Effective Date until termination;

  • SERVICE PROVIDERS: effective Jan 1, 2017, new sponsoring companies pay $83,500 USD per annum due on the Effective Date and on each anniversary of the effective date until termination.

Because companies receive access to cumulative research findings of SINBAD, a Late Entry fee applies:

  • companies new to SINBAD will pay 50 % of the previous three years of funding;

  • companies that were previously members of SINBAD, absent for two or less years, will pay 50 % of one year of funding.

For questions regarding the fee structure, please contact of the UILO. If you have questions please feel free to contact , phone 1 (604) 822-5674.

Why join the SINBAD Consortium?

The SINBAD Consortium is built around a team with various backgrounds and expertise across a wide-range of disciplines, including Electrical Engineering, Engineering Physics, Earth, Ocean and Atmospheric Sciences, Computer Science, and Mathematics. The unique composition of our team has resulted in fundamental contributions in sparse- recovery with practical applications such as seismic regularization, multiple removal, and simultaneous-source acquisition designed to reduce acquisition and processing related costs. We successfully used this latter methodology to speed up simulations with our preconditioned time-harmonic Helmholtz solver.

By becoming a sponsor, your organization will receive the following:

Software releases, toolboxes, & demos

We implement our algorithms in concrete software releases. See our mind map for connections between the different research directions and information on our publications and software.

Research team

The industrial support for SINBAD supports a broad research team of 20–25 students (including 2 MSc and 13 Phd), 2 post-docs, and a research associate led by

  • Dr. Felix J. Herrmann, professor at the UBC Department of Earth, Ocean, and Atmospheric Sciences and Director of the Seismic Laboratory for Imaging and Modelling. Felix’s research interests include seismic data acquisition and processing, imaging, and full-waveform inversion. Felix serves as Deputy editor for Geophysical Prospecting and editor of Applied Geophysics.

  • Dr. Michael Friedlander, associate professor at the Computer Science Department. Michael’s research interests include convex optimization, stochastic optimization, and machine learning. Michael serves on the editorial boards of SIAM Journal on Optimization (SIOPT), SIAM Journal on Matrix Analysis and Applications (SIMAX), SIAM Journal on Scientific Computing (SISC), Mathematical Programming Computation (MPC), Optimization Methods and Software (OMS) (2010-2013).

  • Dr. Ozgur Yilmaz, associate professor at the Mathematics Department. Ozgur’s research interests include compressive sensing, theoretical signal processing, applied harmonic analysis (wavelets).

Terms & Conditions

UBC does not permit secrecy in research. The director of SLIM determines the research direction and will seek advice from the Steering Committee, which consists of representatives of all SINBAD Consortium member companies. There will be procedures in place for industrial visitors. UBC’s intellectual property (IP) policies apply to research conducted as part of SINBAD Consortium. The membership fees are inclusive of all costs, including all licenses to use the technology commercially, including third party intellectual property rights. The membership fees also include all overhead (25 %) and access fees. In addition, members will gain access to all background and foreground IP for the duration of the Consortium and will have the option to buy a perpetual royalty-free license. The UBC Liaison Office (UILO) has drawn up a detailed research agreement, which is available upon request from .


Professor
Director UBC-Seismic Laboratory for Imaging and Modeling (SLIM)
Department of Earth, Ocean, and Atmospheric Sciences


Research objectives

Our research program, with connections outlined in the mind map, involves the following main components:

Seismic data acquisition — where we leverage recent developments in compressive sensing towards the development of new (randomized) seismic acquisition and recovery schemes that include

  • simultaneous marine acquisition with jittered sampling and randomly placed ocean bottom nodes yielding a new sampling scheme that reduces acquisition costs and improves sampling rates of full-azimuthal sampling.

  • 4-D seismic data acquisition with less reliance on repeatability in acquisition.

Objectives. Design and implementation of new seismic-data acquisition methodologies that reduce costs by exploiting structure in seismic data.

Outcomes. Development of a new paradigm for seismic data acquisition and sparsity/low-rank-promoting recovery that will allow us to acquire high-resolution wide azimuth seismic data volumes at significantly reduced costs. Our technology will be a key enabler for full-waveform inversion by pushing access to both the low and high end of the spectrum.

Seismic data processing — where we leverage recent developments in sparse and rank-revealing optimization methods that include

  • curvelet-based missing-trace interpolation with co-sparsity promotion, which improves recovery for certain redundant transforms.

  • extension of estimation of primaries by sparse inversion to 3-D, which is an alternative formulation to SRME and that is computationally affordable.

  • missing trace-trace interpolation with matrix or tensor factorizations, which is computationally affordable while avoiding redundancy of transform-domain methods such as curvelets.

Objectives. Wave-equation-based mitigation of the free surface by sparse inversion and recovery of incomplete data.

Outcomes. A robust framework for the estimation of surface-free Green’s functions (multiple-free data) and source signatures that serve as input to imaging, migration-velocity analysis, and full-waveform inversion.

Seismic modelling — where we leverage recent developments wave simulators and streaming field-programmable gate-array (FPGA) hardware that include

  • optimized (on FPGA) preconditioner for the time-harmonic Helmholtz solver for frequency domain solvers yielding a computationally efficient solver suitable for reverse-time migration and full-waveform inversion.

  • optimized (on FPGA) time-stepping method for wave-equation solvers yielding a computationally efficient solver suitable for reverse-time migration and full-waveform inversion.

Objectives. Design and implementation of efficient wavefield simulators in 2- and 3-D.

Outcomes. Concrete implementation of a scalable virtually parameter-free object-oriented parallel simulation framework in 2- and 3-D for time-harmonic wave equations including explicit control of simulation accuracy, matrix-free definition of the linearized Born scattering operator (the Jacobian) and its adjoint the reverse-time migration operator (adjoint of the Jacobian).

Seismic wave-equation based imaging—where we leverage recent developments in sparse recovery and imaging with multiples that includes

  • extension of fast sparsity-promoting imaging to 3D yielding a formulation for least-squares reverse-time migration in 3D that has roughly the cost of a single migration.

  • extension of imaging with surface-related multiples to 3D yielding an imaging scheme that is fast, images surface-related multiples and that estimates the source function on the fly.

  • migration-velocity analysis with the double two-way wave equation yielding an automatic velocity model building technology that uses both reflected and turning waves.

Objectives. Design and implementation of an efficient and robust wave-equation based inversion framework leveraging recent developments in machine learning, sparse recovery, robust statistics, and optimization.

Outcomes. An efficient, concrete, and versatile imaging framework accelerated by message passing and improved by curvelet-domain sparsity promotion by leveraging the free surface and properties of extended image volumes.

Seismic full-waveform inversion (FWI) —where we leverage recent developments in machine learning and PDE constrained optimization that include

  • Extension of our efficient badging techniques for 3-D FWI to multiparameter (anisotropic, elastic, etc.) case, yielding a fast implementation based on our fast wave simulators.

  • New workflows for FWI that allow us to work with elastic data, which include the use of curvelets for signal separation, yielding a robust scheme for the inversion of elastic data with an incomplete (e.g. acoustic) wave simulator.

  • Extension of our new penalty formulation to 3-D and to more general wave physics, yielding a scheme for FWI that requires less accurate initial models to guarantee convergence.

Objectives. Development of a fast and versatile framework for FWI.

Outcomes. A fast and robust framework for full-waveform inversion that removes some of the impediments of computational complexity, by using randomized dimensionality-reduction techniques, some of reliance on accurate wave physics, by using misfit functionals derived from robust statistics, and some of the reliance of accurate starting models by enlarging the search space of the optimization.

Case studies: Wave-equation based inversion on industrial data–where we leverage our tools to solve industry-scale problems in above areas of exploration seismology.


  1. For commercial reasons, industry does not always share with us how technology developed in our group is implemented.