SEG Training Course: Physics and Data Driven Seismic Data Analysis: A Narrative of Two Approaches
Much of the seismic data analysis has been carried out using methods based on the physics of wave propagation and some signal processing principles. These methods are generally termed ‘physics-based’ or ‘model- based’ approaches. The entire suite of seismic processing and inversion algorithms starting from stacking velocity analysis to full waveform inversion is developed on these principles. Recently, a flurry of data-driven models that are agnostic to the physics has been developed for application to some previously solved and unsolved problems. Physics based approached rely on fundamental physical principles resulting in some predictive equations. However, it may suffer from the limitations of being incomplete. Data driven approaches, on the other hand, are methods for understanding the mechanisms generally by identifying patterns in large volumes of data. Thus, it may appear that we are at cross-roads to decide on the applicability and usefulness of the two seemingly different approaches. Even a broader question is “do we need to choose between the two?”
Questions answered during this course:
- How do we relate input data to the desired output?
- What are the appropriate questions to ask?
- What is the role of physics in seismic data analysis? A historical perspective.
- Are we missing something in our physics-based models? What are the limitations, if any?
- Can the signal processing-based approaches address some of the limitations?
- What is data-driven or a machine learning (ML) approach?
- How do ML methods work? Is ML just the old perfume in a new bottle?
- What is the role of physics in ML formulation?
- Do ML methods offer any advantages over current physics-based approaches?
Course Objectives:
Like any other industry, seismic industry is abuzz with the resurgence of artificial intelligence and machine learning. Is this just a fashionable thing to do? Can we expect to revolutionise the seismic data processing and interpretation steps? The goal of this course is to take a step-by-step approach to explain the physics, signal processing and ML based approaches in seismic data analysis. The focus is not on the theoretical principles but only the applicability and usefulness of these approaches. Examples from basic data conditioning and NMO to velocity estimation, QSI and automated interpretation, will be used to demonstrate the current status and future directions.
Who Should Attend:
The course is intended for all practitioners including R&D professionals, managers, data processors, and interpreters. The primary target audience is exploration geophysicists, who are interested in not just an overview of the new data driven technologies but the intellectual merits and value addition in our trade.
- Processing Geophysicists would find this course useful to choose appropriate methods.
- R&D professionals would be benefited from learning the details.
- Interpreters would be able to appreciate the ease of large-scale data exploration.
- Managers would be able to assess the value addition of this new technology.