Here are some results for the 3-corner hat subtraction for the six accelerometers, from ~1 hour of data that didn't look to have any sharp features/glitches from human activity in the lab. 

I used the python uncertainties package to try and get an estimate of the uncertainty in the subtracted noise floor, by taking into account every possible possible combination of 3 sensors and the fluctuations in the spectrograms of the subtracted signals. I've attached the python code if anyone is interested in the implementation. 

I pulled out the accelerometer data sheets to read their slightly varying V/g calibration (which differs on the order of a few percent from unit to unit). This improved the subtraction factor from ~20 to over 100 at some frequencies. I've edited the filter modules such that the OUT_DQ channels are already calibrated into m/s^2.