I’ve seen this conversion question asked while using SWARM, but I am trying to convert my counts to pressure using a python script. I know I have to use my bit resolution, which is 24-bits, and counts relates to the number of bits the sensor can handle. What is throwing me off is the fact that the sensor is a differential pressure gauge, where one end acts like a mechanical filter and the other end is open for any air flow. I’m not entirely sure what I am missing and how to relate these values to one another. Any help would be much appreciated.
You may find this useful: https://manual.raspberryshake.org/metadata.html#example-obspy-code-for-removing-response
This takes the calibration from our FDSN servers and applies it to the raw data to remove the instrument response, after which you will have a more accurate sense of motion (or pressure variation for a Boom channel) at the sampled frequencies.
The script relies on ObsPy: https://obspy.org
A note about the Boom channels: we have an approximate calibration in our metadata for Boom instruments right now, but it has not been fine-tuned. This is something we are working on, but we don’t have an expected delivery date yet.
Thanks for the help! Do you have any recommended software that I could use to verify whether the pressure values that I am deriving are correct or not? I would assume I could use SWARM or JAmasies, I think.
I think your best bet would be to compare your numbers with the absolute pressure values at a nearby airport automated weather station (AWS) that’s close to your elevation. Sometimes these values are hard to find because they get converted to sea level pressure but are not explicitly labeled as such, and some services are better than others at differentiating between the two. I used to spend a lot of time with AWS data so if you have a source you’re not sure about let me know.
I don’t think this advice is very helpful…
I’ve done a comparison between atmospheric pressure measured by a PWS (personal weather station) right alongside my Boom and this is what I found:
Both signals have been low-pass filtered using the same orthogonal wavelet filter.
As you can see, there is poor correlation between the two, perhaps because the Boom signal already has mechanical high-pass filtering in it, thus removing atmospheric tides, etc.
For me, this is just fine because I want the Boom to measure infra sound, not atmospheric pressure per se.
Thanks for the help! Unfortunately this is not going to be a viable method for me, since I need accurate readings from the device. I do have a few more questions relating to this topic.
Do you know the resolution between each count? I want to say it’s 126…but that refers to the clip level and I’m not entirely sure what that means.
Is it possible for you to show me how you are currently approximating the metadata for the Boom instrument?
What is the part number for the pressure sensor being used? And, is all the information you provide for the pressure sensor the same as it would appear in the specification document related to the part number?
Sorry for bombarding you with these questions I’m just trying to find out as much info as I can so I can figure out this problem. I really do appreciate all the help!
It would be fun to do a direct test. Find a large rigid vessel of known volume and couple it to the RB along with a small syringe. Rapidly inject and withdraw a few cc of air and watch the pressure change recorded vs calculated.
You can see the delta-P effect of just raising and lowering the RB by say 1 meter (~ 12 pascals/meter = 48000 counts). Unfortunately the dynamics of the sensor itself and entrained air plus filtering make it hard to be precise. But it certainly is in the neighborhood of 4000 counts/pascal at low frequencies.
or 2500 micro-pascals/count.
But, at full bandwidth, resolution is really only about 1/4 pascal because of the relatively large amount of self-noise (1000 counts) Rboom self-noise
For me, the raw data should be scaled by dividing by 100 right at the very start. Then the self noise would be 10 counts. But that’s another story…
Good morning Slygo:
Thank you for your enthusiasm for the RBOOM.
Determining the Sensitivity of the RBOOM and building the nominal response is one of my projects. I will move it closer to the front of the line for you. I will reach out to you when this is ready.
Kind regards from Panama,
I have finished determining the nominal sensitivity for the RS&BOOM’s HDF channel: It is 56,000 (not the originally reported 4,000 which was just a placeholder).
I have updated this at:
Thank you for your continued support.
Sorry for the delay. You know … the pandemic :).