dilshad-dataxpert / seismic_npt-code

The implemention of NPT, Disentangling Noise Pattern from Seismic Images: Noise Reduction and Style Transfer

Description

The implemention of NPT, Disentangling Noise Pattern from Seismic Images: Noise Reduction and Style Transfer .

NPT has two key parts, which are named I2I-NT and D2D-NT. I2I-NT provides image to image noise transfer while D2D-NT trains dataset to dataset noise transfer using the same network structure with DnCNN.

For new readers, we recommend they follow the steps below to better understand our model:

• 1. Build conda environment using requirments.txt
• 2. Read & run i2i_nt.ipynb to see how I2I-NT works
• 3. Read d2d_nt.py, then checking its configuration in /options
• 4. Run & check comparation.ipynb to see the results

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The directory structure and files of this project is detailed as:

Directory	Description
/data	Seismic data processing method for D2D-NT training
/fault_interpretation	Experiments for transferability
/model_zoo	Npt models that are trained by us, which outputs results in paper.
/models	Network structures of D2D-NT and baselines
/options	Hyper-parameter and configuration of D2D-NT model
/parameter_test	Data patches that used in our experiments
/tdtv_patches	Samples that ouputs by TDTV
/utils	Models that used for seismic image processing
comparation.ipynb	Experiment results in our paper
d2d_nt.py	Code of D2D-NT model
i2i_nt.ipynb	Code of I2I-NT model. We also provide some examples for readers to fine-tune the parameters on their datasets.
image_utils.py	Utils that used for experiments
no_clean.png	Position image
no_noise.png	Position image
requirements.txt	Readers can use this file to build a conda runtime environment
ricker.ipynb	Method to generate our FSSynth dataset.
tdtv.py	TDTV model that implemented by us
tdtv_validate	The smoothing process implemention and examples of TDTV model