3 research outputs found
Automatic classification of geologic units in seismic images using partially interpreted examples
Geologic interpretation of large seismic stacked or migrated seismic images
can be a time-consuming task for seismic interpreters. Neural network based
semantic segmentation provides fast and automatic interpretations, provided a
sufficient number of example interpretations are available. Networks that map
from image-to-image emerged recently as powerful tools for automatic
segmentation, but standard implementations require fully interpreted examples.
Generating training labels for large images manually is time consuming. We
introduce a partial loss-function and labeling strategies such that networks
can learn from partially interpreted seismic images. This strategy requires
only a small number of annotated pixels per seismic image. Tests on seismic
images and interpretation information from the Sea of Ireland show that we
obtain high-quality predicted interpretations from a small number of large
seismic images. The combination of a partial-loss function, a multi-resolution
network that explicitly takes small and large-scale geological features into
account, and new labeling strategies make neural networks a more practical tool
for automatic seismic interpretation.Comment: 7 pages, 3 figure
Neural-networks for geophysicists and their application to seismic data interpretation
Neural-networks have seen a surge of interest for the interpretation of
seismic images during the last few years. Network-based learning methods can
provide fast and accurate automatic interpretation, provided there are
sufficiently many training labels. We provide an introduction to the field
aimed at geophysicists that are familiar with the framework of forward modeling
and inversion. We explain the similarities and differences between deep
networks to other geophysical inverse problems and show their utility in
solving problems such as lithology interpolation between wells, horizon
tracking and segmentation of seismic images. The benefits of our approach are
demonstrated on field data from the Sea of Ireland and the North Sea.Comment: 8 pages, 5 figure
Multi-resolution neural networks for tracking seismic horizons from few training images
Detecting a specific horizon in seismic images is a valuable tool for
geological interpretation. Because hand-picking the locations of the horizon is
a time-consuming process, automated computational methods were developed
starting three decades ago. Older techniques for such picking include
interpolation of control points however, in recent years neural networks have
been used for this task. Until now, most networks trained on small patches from
larger images. This limits the networks ability to learn from large-scale
geologic structures. Moreover, currently available networks and training
strategies require label patches that have full and continuous annotations,
which are also time-consuming to generate.
We propose a projected loss-function for training convolutional networks with
a multi-resolution structure, including variants of the U-net. Our networks
learn from a small number of large seismic images without creating patches. The
projected loss-function enables training on labels with just a few annotated
pixels and has no issue with the other unknown label pixels. Training uses all
data without reserving some for validation. Only the labels are split into
training/testing. Contrary to other work on horizon tracking, we train the
network to perform non-linear regression, and not classification. As such, we
propose labels as the convolution of a Gaussian kernel and the known horizon
locations that indicate uncertainty in the labels. The network output is the
probability of the horizon location. We demonstrate the proposed computational
ingredients on two different datasets, for horizon extrapolation and
interpolation. We show that the predictions of our methodology are accurate
even in areas far from known horizon locations because our learning strategy
exploits all data in large seismic images.Comment: 24 pages, 13 figure