Downsampling and class ratios
Downsampling means you sample from the majority class (the 98.5%) to reduce the imbalance between majority and minority class. If you keep the ratio constant you simply reduce your number of trainings examples. This doesn't make sense. However, you don't have to sample down to a ratio of 50:50. If you have a ratio of 98:2, you can sample to 80:2 instead of 2:2. 

The main goal of downsampling (and upsampling) is to increase the discriminative power between the two classes. Ideally, you would have a classifier that outputs a decision surface that is not simply binary (e.g. logistic regression (where you don't have to select a cut-off point of 0.5)) but gives you a continuous decision value. You can then order the data and set a decision threshold that gives you the best outcome. 

Since downsampling (or upsampling) changes your training distribution from your true distribution, you only want to downsample (or upsample) so much that your classifier can start discriminating between the two classes. You then fine-tune the results by selecting an appropriate decision threshold. (Also, in my experience, upsampling is often a better choice over downsampling.)