Machine learning has already made many inroads in developments related to acquisition, processing, imaging, inverting, and interpreting seismic data. In spite of the many success stories, its commercial use has been limited as the challenges mount. These challenges include cost of training, availability of training samples, the applicability of the trained model to real data (generalization), and more importantly, the availability of practitioners who actually know what the neural networks (NNs) are doing. Taking a step back, I will review what worked in deep learning and what we are still waiting on to work. We will look into the various ML algorithms, from supervised to unsupervised, transformers to contrastive learning, and identify the potential role of these various algorithms on seismic data, with examples. The examples include seismic data denoising, data extrapolation, first arrival picking, microseismic location, velocity inversion all on real data.
|Original language||English (US)|
|Number of pages||4|
|State||Published - Aug 15 2022|
|Event||2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022 - Houston, United States|
Duration: Aug 28 2022 → Sep 1 2022
|Conference||2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022|
|Period||08/28/22 → 09/1/22|
Bibliographical notePublisher Copyright:
© 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology