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README.md

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-docs/README.md

README.md

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+---
+title: DeepDenoiser
+emoji: 🌊
+colorFrom: purple
+colorTo: blue
+sdk: docker
+pinned: false
+---
+
+# DeepDenoiser: Seismic Signal Denoising and Decomposition Using Deep Neural Networks
+
+[![](https://github.com/AI4EPS/DeepDenoiser/workflows/documentation/badge.svg)](https://ai4eps.github.io/DeepDenoiser)
+## 1.  Install [miniconda](https://docs.conda.io/en/latest/miniconda.html) and requirements
+- Download DeepDenoiser repository
+```bash
+git clone https://github.com/wayneweiqiang/DeeoDenoiser.git
+cd DeepDenoiser
+```
+- Install to default environment
+```bash
+conda env update -f=env.yml -n base
+```
+- Install to "deepdenoiser" virtual envirionment
+```bash
+conda env create -f env.yml
+conda activate deepdenoiser
+```
+
+## 2. Pre-trained model
+Located in directory: **model/190614-104802**
+
+## 3. Related papers
+- Zhu, Weiqiang, S. Mostafa Mousavi, and Gregory C. Beroza. "Seismic Signal Denoising and Decomposition Using Deep Neural Networks." arXiv preprint arXiv:1811.02695 (2018).
+
+## 4. Interactive example
+See details in the [notebook](https://github.com/wayneweiqiang/DeepDenoiser/blob/master/docs/example_interactive.ipynb): [example_interactive.ipynb](example_interactive.ipynb)
+
+
+## 5. Batch prediction
+See details in the [notebook](https://github.com/wayneweiqiang/DeepDenoiser/blob/master/docs/example_batch_prediction.ipynb): [example_batch_prediction.ipynb](example_batch_prediction.ipynb)
+## 6. Train
+### Data format
+
+Required: two csv files for signal and noise, corresponding directories of the npz files.
+
+The csv file contains four columns: "fname", "itp", "channels"
+
+The npz file contains four variable: "data", "itp",  "channels"
+
+The shape of "data" variables has a shape of 9001 x 3
+
+The variables "itp" is the data points of first P arrival times.
+
+Note: In the demo data, for simplicity we use the waveform before itp as noise samples, so the train_noise_list is same as train_signal_list here.
+
+~~~bash
+python deepdenoiser/train.py --mode=train --train_signal_dir=./Dataset/train --train_signal_list=./Dataset/train.csv --train_noise_dir=./Dataset/train --train_noise_list=./Dataset/train.csv --batch_size=20
+~~~
+
+Please let us know of any bugs found in the code. Suggestions and collaborations are welcomed

docs/README.md

@@ -1,60 +0,0 @@
----
-title: DeepDenoiser
-emoji: 🌊
-colorFrom: purple
-colorTo: blue
-sdk: docker
-pinned: false
----
-
-# DeepDenoiser: Seismic Signal Denoising and Decomposition Using Deep Neural Networks
-
-[![](https://github.com/AI4EPS/DeepDenoiser/workflows/documentation/badge.svg)](https://ai4eps.github.io/DeepDenoiser)
-## 1.  Install [miniconda](https://docs.conda.io/en/latest/miniconda.html) and requirements
-- Download DeepDenoiser repository
-```bash
-git clone https://github.com/wayneweiqiang/DeeoDenoiser.git
-cd DeepDenoiser
-```
-- Install to default environment
-```bash
-conda env update -f=env.yml -n base
-```
-- Install to "deepdenoiser" virtual envirionment
-```bash
-conda env create -f env.yml
-conda activate deepdenoiser
-```
-
-## 2. Pre-trained model
-Located in directory: **model/190614-104802**
-
-## 3. Related papers
-- Zhu, Weiqiang, S. Mostafa Mousavi, and Gregory C. Beroza. "Seismic Signal Denoising and Decomposition Using Deep Neural Networks." arXiv preprint arXiv:1811.02695 (2018).
-
-## 4. Interactive example
-See details in the [notebook](https://github.com/wayneweiqiang/DeepDenoiser/blob/master/docs/example_interactive.ipynb): [example_interactive.ipynb](example_interactive.ipynb)
-
-
-## 5. Batch prediction
-See details in the [notebook](https://github.com/wayneweiqiang/DeepDenoiser/blob/master/docs/example_batch_prediction.ipynb): [example_batch_prediction.ipynb](example_batch_prediction.ipynb)
-## 6. Train
-### Data format
-
-Required: two csv files for signal and noise, corresponding directories of the npz files.
-
-The csv file contains four columns: "fname", "itp", "channels"
-
-The npz file contains four variable: "data", "itp",  "channels"
-
-The shape of "data" variables has a shape of 9001 x 3
-
-The variables "itp" is the data points of first P arrival times.
-
-Note: In the demo data, for simplicity we use the waveform before itp as noise samples, so the train_noise_list is same as train_signal_list here.
-
-~~~bash
-python deepdenoiser/train.py --mode=train --train_signal_dir=./Dataset/train --train_signal_list=./Dataset/train.csv --train_noise_dir=./Dataset/train --train_noise_list=./Dataset/train.csv --batch_size=20
-~~~
-
-Please let us know of any bugs found in the code. Suggestions and collaborations are welcomed