Question Iso-risk decompression schedules

[L13]

Off course. The point is to know how safe a certain profile is. The Algorithm presented by Doolette tells you the probability of getting DCS so it would be very useful to be able to compare to Buhlman to know how safe your GF are. It seems from his presentation that a given GF has different safety margin depending on the depth (safer with smaller deco dives, less safe with big dives).

Not just depth, also bottom time. For two dives with the same GF's but different deco times, risk goes up as deco time goes up. But it isn't a linear function.

This makes sense if you think about it. GF is a measure of supersaturation. I you spend more time at the same supersaturation, you have a greater chance of an adverse event resulting from that supersaturation.

Say you are using GF's 50/70, If you do a dive with 10 minutes of deco, you spend a total of 10 minutes with supersaturation going up to 50% dropping and going up to 70, then it drops rapidly after that on the surface. If you do a dive with 600 minutes of deco. You spend hours with supersaturation above 50, peaking at 70, and dropping slowly over hours after you surface. Same GF settings on your computer, but your exposure to supersaturation is much more, and your risk is therefore more.

Since risk of an adverse event at 60% << at 70% etc., just reducing your GF's a little as total deco time goes up can keep your total risk the same (total risk is the sum of the risk/min for each min of exposure).

For Iso-risk using GF's, lower GF's should be used for bigger dives than the GF's used for smaller dives.

 

[rjack321]

For Iso-risk using GF's, lower GF's should be used for bigger dives than the GF's used for smaller dives.

For sure, just a bit hard to wing it and know how much longer creates how much lower

 

[LFMarm]

Trying to find a few tables to be able to build comparable curves with GFs. Example here: https://apps.dtic.mil/sti/pdfs/ADA549553.pdf

 

[LFMarm]

Here is my understanding after reading the Navy report:

• XVal-He-4 can be best used for dives shallower than 140fsw / 42m to keep DCS probability (PDCS) to 2.3%
• XVal-He-4B can be best used for dives between 140fsw / 42m and 200fsw / 61m with one-hour maximum time deeper than 140fsw / 42m; however decompression schedules for dives with bottom times longer than 20 minutes at these depths have PDCS that can be substantially above 3%

 

[LFMarm]

Another interesting read for the weekend: https://apps.dtic.mil/sti/tr/pdf/AD1000575.pdf

 

[Kendall Raine]

Thank you LFMarm for linking the presentation. There is a lot in it and it's worth watching several times.

The Iso-Risk v 50/85 comparison is very interesting (so much for rumors/hand waving that Deep Stops per se are dead (debunked/out of favor/discredited, blah, blah) since anything with GF Low< 100 is a "deep stop" and the XVal shape is definietly a deep stops-but not a dual phase aka VPM/RGBM-shape). I enjoyed the observation that a proxy for XVal (GF 50/85) works well in the 250' range but not so in the 400" range (50/60 being much closer). Would be great to see a Kevin Watts supersaturation heat map comparison (or ISS graphic) of an 50/85 and 50/60 v. XVal shapes for those exposures.

I also found the detailed discussion of Doppler studies, and the very large variability in bubble grades for the same divers doing identical dives across time, worth looking at. Doppler scores are known to be directionally, but very loosely, correlated with DCS. Doolette's comment that Dopplers are virtually useless in the context of individual divers (but useful for populations of divers) because of this variability was enlightening. More so because I have sometimes seen large variability in my own scores doing identical dives and been puzzled by that. That said, I have also seen a strong correlation (I believe non-spurious) between scores and conditions (cold, dark, work load, depth) that I think are not just random variability but directly related to physical and emotional stress (e.g. riding a scooter and good viz lowers scores). Too bad the comparative bubble grade analysis was not spread across a time scale so one might infer any "adaptation" effects on sequential scores.

 

[LFMarm]

Building a curve out of 2 data points is not very robust but here is my extrapolation out of the 2 charts.

GF High = 93 - 0.055 x mins of deco (from the charts)
GF Low = 0.83 x GF High (from other Doolette article)

Deco [min]	GF Low	GF High
10	80	95
20	75	90
30	75	90
40	75	90
50	75	90
60	75	90
70	75	90
80	75	90
90	75	90
100	75	90
120	70	85
140	70	85
160	70	85
180	70	85
200	65	80
220	65	80
240	65	80
260	65	80
280	65	80
300	60	75
330	60	75
360	60	75
390	60	70
420	60	70
450	60	70
480	55	65
510	55	65
540	55	65

 

[LFMarm]

wonder how close a 50/70 would line up.

From my testing quite close. The closest is 55/65

 

[LFMarm]

Here is a chart showing the equivalent GF High as a function of deco time (TST) to produce a DCS risk of 2.3% — all built out of the work of Doolette in 2018 (https://apps.dtic.mil/sti/trecms/pdf/AD1215316.pdf):

 

[justinthedeeps]

Here is a chart showing the equivalent GF High as a function of deco time (TST) to produce a DCS risk of 2.3% — all built out of the work of Doolette in 2018 (https://apps.dtic.mil/sti/trecms/pdf/AD1215316.pdf):

View attachment 839028​

Thanks for that. How is TST defined? Is that a bottom time? Total dive time?
How many dives in the training data set actually ran that long? What was the n/n for DCS on those dives?

Presumably the vast majority of dives in the training set had no DCS, despite un-conservative parameters (due to sanity and ethics). So this model fits are tasked with focusing on a few very rare DCS incidents, imagining some edge case parameters that might eliminate them--unless they don't, due to additional causative or 'random' factors.

Are we sure that the relatively small number of DCS incidences (≤2%) in these models can be explained or predicted on the basis to minor differences in GFs and deep stops? How much additional data would you need to statistically control for a random null model or negative hypothesis of DCS occurrence, and other factors such as person-to-person variation, etc?

In the context of the original thread question that brought me here, the graph here suggests a SurfGF of ~50? Which I guess in part explains all the deco habitats that we're seeing on new 'world record' dives.