Principles of seismic stratigraphy and seismic geomorphology I: Extracting geologic insights from seismic data
With the advent of widely available 3D seismic data, numerous workflows focused on
extracting subsurface stratigraphic information have been developed. We present here tools …
extracting subsurface stratigraphic information have been developed. We present here tools …
Building realistic structure models to train convolutional neural networks for seismic structural interpretation
Seismic structural interpretation involves highlighting and extracting faults and horizons that
are apparent as geometric features in a seismic image. Although seismic image processing …
are apparent as geometric features in a seismic image. Although seismic image processing …
Seismic imaging of incomplete data and simultaneous-source data using least-squares reverse time migration with shaping regularization
Simultaneous-source acquisition improves the efficiency of the seismic data acquisition
process. However, direct imaging of simultaneous-source data may introduce crosstalk …
process. However, direct imaging of simultaneous-source data may introduce crosstalk …
Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network
The seismic horizon is a critical input for the structure and stratigraphy modeling of
reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for …
reservoirs. It is extremely hard to automatically obtain an accurate horizon interpretation for …
Deep learning for relative geologic time and seismic horizons
Constructing a relative geologic time (RGT) image from a seismic image is crucial for
seismic structural and stratigraphic interpretation. In conventional methods, automatic RGT …
seismic structural and stratigraphic interpretation. In conventional methods, automatic RGT …
Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a …
Fault detection in a seismic image is a key step of structural interpretation. Structure-oriented
smoothing with edge-preserving removes noise while enhancing seismic structures and …
smoothing with edge-preserving removes noise while enhancing seismic structures and …
Horizon volumes with interpreted constraints
X Wu, D Hale - Geophysics, 2015 - library.seg.org
Horizons are geologically significant surfaces that can be extracted from seismic images.
Color coding of horizons based on amplitude or other attributes can help reveal ancient …
Color coding of horizons based on amplitude or other attributes can help reveal ancient …
Deep relative geologic time: A deep learning method for simultaneously interpreting 3‐D seismic horizons and faults
Extracting horizons and detecting faults in a seismic image are basic steps for structural
interpretation and important for many seismic processing schemes. A common ground of the …
interpretation and important for many seismic processing schemes. A common ground of the …
Separation of simultaneous sources using a structural-oriented median filter in the flattened dimension
Simultaneous-source shooting can help tremendously shorten the acquisition period and
improve the quality of seismic data for better subsalt seismic imaging, but at the expense of …
improve the quality of seismic data for better subsalt seismic imaging, but at the expense of …
Waveform embedding: Automatic horizon picking with unsupervised deep learning
Picking horizons from seismic images is a fundamental step that could critically impact
seismic interpretation quality. We have developed an unsupervised approach, waveform …
seismic interpretation quality. We have developed an unsupervised approach, waveform …