This page contains all the videos that were presented during the meeting, as well as links to the corresponding reports. At this time these are available for members of sponsoring companies only (requires password).
CREWES continues to focus on fibreoptics (DAS) as a sensing technology to support monitoring. DAS, illuminated by standard but also new types of permanent and continuous sources, comprises a potentially very powerful approach to acquisition. In this session we summarize continuing efforts to use and invert DAS data, and innovative ways to deploy DAS fibre, and interpret and process the data.
|Talk 1. Field testing of multicomponent DAS sensing (Kevin Hall)||video||report|
|Talk 2. Squid: an innovative new ground coupled source (Don Lawton)||video||report|
|Talk 3. Least squares DAS-to-geophone transform (J. Monsegny)||video||report|
|Talk 4. The role of fibre gauge length in FWI of DAS data (M. Eaid)||video||report|
|Talk 5. 2D inversion of DAS surface wave data (L. Qu)||video||report|
|Talk 6. A comparative study of different DAS vendors' data (J Monsegny)||video||report|
This session ties the acquisition research and the method development discussed in the next sessions together to support monitoring. This is rapidly growing into one of the primary applications of our geophysics, and refinement of methods to be effectively and efficiently applied is a critical task.
|Talk 7. Time-lapse VSP results from the CaMI.FRS (B. Kolkman-Quinn)||video||report|
|Talk 8. FWI TL monitoring of CO2 injection at CaMI (N. Amundaray)||video||report|
|Talk 9. MCMC-based time-lapse FWI (X. Fu)||video||report|
|Talk 10. Rock physics properties with global optimization methods (Q. Hu)||video||report|
Amplitude inversion and imaging/processing have both been the subjects of intensive work in 2020. The increasingly detailed use of reflection strengths to determine reservoir properties, and the link between HPC and blending have been particularly emphasized.
|Talk 11. Full wavefield migration in the frequency-wavenumber domain (S. Huang)||video||report|
|Talk 12. Inversion for indicators of interconnected / aligned cracks (H. Chen)||video||report|
|Talk 13. Application of stereotomography to the Hussar dataset (B. Law)||video||report|
|Talk 14. HPC methods for HR Radon transform and deblending (K. Zhuang)||video||report|
|Talk 15. Deblending in common receiver and common angle gathers (Z. Su)||video||report|
In this session we summarize our efforts in developing FWI for the reservoir, for monitoring of production, injection, storage; this requires high resolution, multiparameter subsurface models to be inferred, and for confusion between those parameters and source locations and signatures to be managed. It also requires us adapt and grow our ability to incorporate prior information and constraints.
|Talk 16. A multigrid approach for time domain FWI (D. Trad)||video||report|
|Talk 17. Elastic FWI with rock physics constraints (Q. Hu)||video||report|
|Talk 18. A tunneling approach to regularized full waveform inversion (S. Keating)||video||report|
|Talk 19. FWI model space coordinate system design based on data misfit (K. Innanen)||video||report|
|Talk 20. Acoustic full waveform inversion using blended data (H. Liu)||video||report|
|Talk 21. Source-model simultaneous FWI (S. Keating)||video||report|
|Talk 22. Incorporating a prior information in full waveform inversion (D. Li)||video||report|
The CREWES Data Science initiative, led by Daniel Trad and Marcelo Guarido, has made important progress in several areas, including wave simulation, modeling, and inversion. This includes advances in supervised and unsupervised method development, and the grey area between them. The team has also been involved in several machine learning challenges and competitions in 2020. We end the session with a special presentation by Brian Russell on unsupervised classification.
|Talk 23. CREWES solutions for machine learning competitions in 2020 (M.Guarido)||video||report|
|Talk 24. Deep learning for 3D fault detection in virtual reality visualization (A. Fathalian)||video||report|
|Talk 25. VTI/TTI full waveform inversion within theory guided neural networks (T. Zhang)||video||report|
|Talk 26. Physics-guided DL for seismic inversion: hybrid training and uncertainty (J. Sun)||video||report|
|Talk 27. Experiments on constructing seismic data using GANs (Z. Niu)||video||report|
|Talk 28. Physics-guided NN for velocity calibration using microseismic data (H. Zhang)||video||report|
|Talk 29. Deep learning for DAS-microseismic source estimation (M. Eaid)||video||report|
|Talk 30. Unsupervised seismic facies classification using K-means and GMM (B. Russell)||video||report|
This course is a growth and extension of courses the group has put on in recent years, both after the Sponsors' Meeting and for various sponsor companies. Some new features: it uses tools which are entirely online, and so does not require any downloading of apps on the participants' side. With many in the geophysical community becoming expert in the area, the course now assumes a basic comfort level with machine learning, and goes a little deeper.
|Short Course: Developing Data Science Solutions to Geophysical Challenges||course videos||syllabus|