Machine learning for subsurface geological feature identification from seismic data: Methods, datasets, challenges, and opportunities
Identification of geological features from seismic data such as faults, salt bodies, and
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
channels, is essential for studies of the shallow Earth, natural disaster forecasting and …
Integrating Energy Datasets: The MDIO format
MDIO offers a technical solution for storing and retrieving energy data in the cloud and on-
premises. As an open-source framework, it incorporates high-resolution, multi-dimensional …
premises. As an open-source framework, it incorporates high-resolution, multi-dimensional …
Encoding the subsurface in 3D with seismic
This article presents a self-supervised generative AI approach to seismic data processing
and interpretation using a M asked A uto E ncoder (MAE) with a V ision Transformer (ViT) …
and interpretation using a M asked A uto E ncoder (MAE) with a V ision Transformer (ViT) …
Scaling Seismic Foundation Models
Traditional workflows using machine learning interpretation of seismic data rely on iterative
training and inference on single datasets, producing models that fail to generalise beyond …
training and inference on single datasets, producing models that fail to generalise beyond …
A Comparative Study of the Application of 2D and 3D CNNs for Salt Segmentation
M Roberts, C Warren, B Lasscock… - 85th EAGE Annual …, 2024 - earthdoc.org
This study provides a detailed analysis of seismic interpretation techniques by moving from
2D to 3D Convolutional Neural Networks (CNNs). The study focuses on the effectiveness of …
2D to 3D Convolutional Neural Networks (CNNs). The study focuses on the effectiveness of …