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| - | ===== Signal Analysis and Imaging Group ===== | ||
| + | === Signal Analysis and Imaging Group — Industry Partnership ==== | ||
| - | The SAIG consortium at the University of Alberta is recognized for developing algorithms for multi-dimensional seismic data reconstruction, de-noising and applying sparsity-promoting methods to seismic data processing. An essential objective of SAIG is to offer advanced training to [[people|graduate students]] in seismic signal processing and imaging. Research developed at SAIG has been recognized by national and international [[start:awards|awards]]. | + | |
| + | The Signal Analysis and Imaging Group (SAIG) at the University of Alberta develops advanced computational methods for seismic acquisition, imaging, inversion, and monitoring. | ||
| + | |||
| + | Our research is aimed at problems that remain difficult in practice: large-scale 3D land seismic data, multiparameter full-waveform inversion, sparse and irregular acquisition, 4D monitoring, DAS, and imaging in complex geological settings. | ||
| + | |||
| + | SAIG combines long-standing expertise in inverse problems and seismic signal processing with current developments in high-performance computing, optimization, and machine learning. Our objective is not simply to publish new algorithms, but to develop methods that can be tested on realistic data and transferred into industrial workflows. | ||
| + | |||
| + | ==== Current research directions ==== | ||
| + | |||
| + | |||
| + | Our current program includes: | ||
| + | |||
| + | * 3D and multiparameter full-waveform inversion, with particular emphasis on challenging land data | ||
| + | |||
| + | * seismic acquisition design, 5D reconstruction, and compressive sensing, including sparse and non-conventional geometries. Fourier sparsity-driven methods and tensor-based reconstruction techniques. | ||
| + | |||
| + | * 4D seismic and CCS monitoring, including repeatability, reconstruction, and high-dimensional processing | ||
| + | |||
| + | * DAS and borehole seismic imaging | ||
| + | |||
| + | * least-squares migration, vector-reflectivity imaging, and Hessian-based methods | ||
| + | |||
| + | * deblending, multidimensional reconstruction, and rank-reduction methods | ||
| + | |||
| + | * physics-guided machine learning, including learned representations, implicit neural representations, and hybrid inversion methods. | ||
| + | |||
| + | * A particular strength of SAIG is the integration of acquisition, signal processing, and inversion. Rather than treating these as separate problems, we study how survey design, reconstruction, imaging, and inversion interact within the complete seismic workflow. | ||
| + | |||
| + | ==== Why participate in SAIG? ==== | ||
| + | |||
| + | SAIG sponsorship provides companies with direct access to an active research group working on problems of immediate relevance to seismic exploration, CCS, and subsurface monitoring. | ||
| + | |||
| + | Sponsors have the opportunity to: | ||
| + | |||
| + | * interact directly with SAIG researchers and graduate students; | ||
| + | * influence future research directions through technical discussions; | ||
| + | * obtain early access to research results, software, and computational developments; | ||
| + | * propose challenging datasets and problems for investigation; | ||
| + | * participate in the annual SAIG research meeting and focused technical discussions; | ||
| + | * arrange visits and technical exchanges with the group; | ||
| + | * identify and recruit highly trained MSc, PhD, and postdoctoral researchers. | ||
| + | |||
| + | SAIG is intended to be more than a publication consortium. We want participating companies to regard the group as an extension of their own research network: a place where difficult technical ideas can be explored, tested, and discussed with researchers who have substantial experience in seismic inverse problems. | ||
| + | |||
| + | ==== Why SAIG now?===== | ||
| + | |||
| + | The seismic industry is undergoing a significant transition. Acquisition systems are becoming larger and denser, while economic constraints are driving interest in sparse acquisition. Land datasets remain difficult to process and invert. CCS is developing new requirements for repeatable, cost-effective monitoring. DAS is producing new types of large-scale wavefield data. Machine learning is rapidly entering seismic processing, although robust integration with wave physics and inverse theory remains an open problem. | ||
| + | |||
| + | These developments strongly favor groups that can combine numerical optimization, wave propagation, signal processing, acquisition theory, and machine learning. | ||
| + | |||
| + | This combination has been at the core of SAIG for many years. | ||
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| + | |||
| + | We invite companies interested in these problems to participate in SAIG and help define the research questions that should be addressed over the coming years. | ||
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| + | |||
| + | * [[start:awards|awards]]. | ||
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| ===== Announcements ===== | ===== Announcements ===== | ||
| - | * SAIG 24 Annual Meeting, January 29, 2024, Calgary UofA Centre. | + | * SAIG 27 Annual Meeting, December 2, 2026, Calgary. |
| * For reports, go to Publications -> Annual Reports | * For reports, go to Publications -> Annual Reports | ||
| * Contact MDS if you need sponsor access. | * Contact MDS if you need sponsor access. | ||
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