Comments (1)
The current code uses segyio.tools.cube() to extract a volume of data from segy files that can easily be chuncked up. However, most segy files do not have geometry in them that allows segyio to infer the dimensions of the volume. In the case where geometry is not present. You need to manually infer the dimensions by reading through the trace headers and constructing a volume manually.
Thanks! Could you please provide a reference to where you see this in our code?
from seismic-deeplearning.
Related Issues (20)
- add pre-trained models to test section of the HRNet notebooks - facilitates faster onboarding with more pre-trained models
- provide pre-trained SEResNet model with high accuracy - facilitates faster onboarding with pre-trained SEResNet model
- Azure ML training pipeline improvement comments - facilitates better user onboarding for the pipeline
- add test for multi-GPU train.py run - facilitates bug-free train.py for multi-GPU training
- Dynamic global data information, reduces user-required specifications
- Add data QC module
- add the learning rate to the tensorboard
- investigate the reproducibility problem with patch_deconvnet for small training data set
- bug bash - facilitates gathering feedback from stakeholders
- operator1 and operator2 close-out
- cache Penbscot and Dutch F3 data on build VM and download weekly
- add a clean-up job to remove output directories from build VM
- enable multi-GPU training with Docker image - facilitates multi-GPU training on any Linux OS HOT 1
- README documentation fixes - facilitates clear understanding of the content of the repo
- fix seeds in train.py and test.py to make results perfectly reproducible on a single GPU - enhances the robustness of results
- determine the best configuration parameters for multi-GPU training - facilitates better multi-GPU results
- check license headers again - facilitates compliance to legal terms
- debug slow runtime in multi GPU training - figure out why multi-GPU training does not produce much of a speedup
- debug test.py scoring of trained models - facilitates correct results when scoring trained models
- Trying to get in touch regarding a security issue HOT 3
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from seismic-deeplearning.