Added anaconda links and formatting

README.md

@@ -2,42 +2,42 @@
 *Authors*: [Lukas Mosser](mailto:lukas.mosser15@imperial.ac.uk),
 [Olivier Dubrule](https://www.imperial.ac.uk/people/o.dubrule),
 [Martin J. Blunt](https://www.imperial.ac.uk/people/m.blunt)
-*Department of Earth Science and Engineering, Imperial College London*
+*Department of Earth Science and Engineering, Imperial College London*  
 
-This is the code repository accompanying the publication:
+This is the code repository accompanying the publication:  
  *Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models*
  [[ArXiv]()]
 
 ## Datasets and pre-trained models
 
 ### Ketton Limestone Dataset
-We provide two pre-trained GAN models. The first one is trained on the Ketton limestone training image presented here
+We provide two pre-trained GAN models. The first one is trained on the Ketton limestone training image presented here.  
 If you decide to use this dataset for your own work, please consider citing the following works:
 
-*Stochastic reconstruction of an oolitic limestone by generative adversarial networks*[[ArXiv](https://arxiv.org/abs/1712.02854)]
+*Stochastic reconstruction of an oolitic limestone by generative adversarial networks*[[ArXiv](https://arxiv.org/abs/1712.02854)]  
 *Dynamic reservoir-condition microtomography of reactive transport in complex carbonates*[[Article](https://www.sciencedirect.com/science/article/pii/S0016703717300789)]
 
-Due to their size we provide the necessary files via a [Google Drive](https://drive.google.com/open?id=1qxicm3wzpvijUEpyI3pTm2QPF520SZAw)
+Due to their size we provide the necessary files via a [Google Drive](https://drive.google.com/open?id=1qxicm3wzpvijUEpyI3pTm2QPF520SZAw)  
 
 #### Results
 
 ![Ketton Conditioned](figures/figure_1_ketton.png)
 
-The figure above shows two samples (b/c) obtained by a conditioning a generative adversarial network to three-orthogonal cross-sections of the Ketton training image(a).
-Due to the stochastic nature of the optimization procedure the resulting images have distinctly different features away from the conditioning data.
+The figure above shows two samples (b/c) obtained by a conditioning a generative adversarial network to three-orthogonal cross-sections of the Ketton training image(a).  
+Due to the stochastic nature of the optimization procedure the resulting images have distinctly different features away from the conditioning data.  
 ### Maules Creek Dataset
 
-We have trained a generative adversarial network on the Maules Creek alluvial aquifer training image.
+We have trained a generative adversarial network on the Maules Creek alluvial aquifer training image.  
 
-The required model checkpoints are included in this repository.
-If you choose to use the Maules Creek training image, please consider citing their originators at [trainingimages.org](www.trainingimages.org)
+The required model checkpoints are included in this repository.  
+If you choose to use the Maules Creek training image, please consider citing their originators at [trainingimages.org](www.trainingimages.org)  
 
 #### Results
 
 ![Maules Creek](figures/fig_2.png)
 
-We have conditioned 1024 realizations of the Maules Creek alluvial aquifer model and present mean and standard deviation maps of the resulting ensemble.
-The resulting images show that each realization honors the data at the well exactly and an ellipsoidal region of influence can be observed.
+We have conditioned 1024 realizations of the Maules Creek alluvial aquifer model and present mean and standard deviation maps of the resulting ensemble.  
+The resulting images show that each realization honors the data at the well exactly and an ellipsoidal region of influence can be observed.  
 
 ## Requirements
 
@@ -53,17 +53,18 @@ We recommend using the [anaconda](https://anaconda.org/) distribution to install
 
 ## Development
 
-Currently the code has limited object oriented design and is usable as demonstrated by the accompanying jupyter notebooks.
-We welcome contributions and suggestions for the improvement and development of the tool.
+Currently the code has limited object oriented design and is usable as demonstrated by the accompanying jupyter notebooks.  
+We welcome contributions and suggestions for the improvement and development of the tool.  
 
 ## Required Hardware
 
-We recommend at least 16 GB of RAM and a modern CUDA capable NVIDIA graphics processor.
-All experiments apart from network training have been performed on an NVIDIA 960M.
+We recommend at least 16 GB of RAM and a modern CUDA capable NVIDIA graphics processor.  
+All experiments apart from network training have been performed on an NVIDIA 960M.  
 Training of the generative networks was performed on 8xNVIDIA K40 GPUs.  
 
 ## Support
 
-The software is provided as is. If you have any questions please feel free to contact us via [[email](lukas.mosser15@imperial.ac.uk)] or [[twitter](https://twitter.com/porestar)].
+The software is provided as is.  
+If you have any questions please feel free to contact us via [[email](lukas.mosser15@imperial.ac.uk)] or [[twitter](https://twitter.com/porestar)].