CGG is a global geoscience technology leader. Employing around 3,700 people worldwide, CGG provides a comprehensive range of data, products and services that enable our clients to responsibly solve the world’s most complex natural resource, environmental and infrastructure challenges.
The optimal transport problem was formulated more than 200 years ago to calculate the optimal way of transporting piles of sand. Due to the interesting properties of its solutions with respect to shifts between the compared distributions, optimal transport has recently been adapted to full-waveform inversion to mitigate the cycle-skipping issue. Various formalisms have been proposed. Here we present an overview of these approaches, emphasizing more specifically the approach based on the bi-dimensional Kantorovich-Rubinstein norm, which has led to numerous successful full-waveform inversion applications. We illustrate these successes with two onshore case studies from the Sultanate of Oman.
Are you a physicist, data scientist, engineer, mathematician or problem-solver? Good, glad we have that in common! Join us in transforming real seismic data into stunning 3D images of the Earth’s subsurface. No experience? Don’t worry, we’ll show you the way.
Do you treat your code the way you want others’ code to treat you? If so, you are at the right place. We have exciting development work to do globally, including high-performance computing, imaging and reservoir algorithms, and our proprietary seismic imaging software.
You’ll play a vital role in the continual development of our geoscience analytic techniques! Machine learning engineers possess a passion and aptitude for programming and enthusiasm for analytical and problem-solving challenges.