Beatrice - This is a vexing problem that I've tried to deal with
in sizes of features in satellite imagery (Hlavka, C. A. and J. L. Dungan, 2002. Areal
estimates of fragmented land cover - effects of pixel size and
model-based corrections. International Journal of Remote Sensing23(4):
711-724.) The affine (count versus continuous) nature of
the digital imagery is at least part of the problem. I've used
probability plots to assess type of distribution.
In gas field work, there is evidence that the apparent
lognormality of field-sizes is due to lower rates of discovery of
smaller fields than larger fields - especially for older
surveys. It has been noted that newer field data was
closer to Pareto than older data and thus inferred that the actual
distribution is Pareto. -- Chris
Hello list
I am a PhD student looking at developing a statistical model to predict
the size-distribution of an area's oil and gas fields.
It is clear that previous investigators prefer either a Pareto power law
or a lognormal distribution to approximate field-size distributions.
The data I am using does not look like it comes from a Pareto distribution
- which I explain as being a result of undersampling - which previous
investigators have reported - that undersampling occurs because the small
fields are not sampled or recoded. However by using basin-modelling
software to simulate oil and gas fields (for the same basin that my
discovered empirical data comes from) I notice that this sample is also
undersampled - that is fields under a certain size are not being simulated
- which is probably due to the resolution of my input data but what is
interesting is that the undersampling actually occurs throughout all the
size ranges - including the medium to larger sizes - which I would not
have expected. Like the discovery dataset (n = 25) the simulated dataset
(n = 140) looks like it is more from a lognormal distribution than a
Pareto distribution.
My conclusion is that without being able to say that a Pareto is better
than a lognormal and vise-versa it appears only logical to use both
distributions.
Geologically there does not seems to be a reason why a modal size (greater
than what is detectable by exploration methods) of fields should exist -
which would be the case if the data was from a lognormal distribution -
except if the distribution is highly right skewed (at the small field
size) and the mode is actually just below the detection of size.
Geologically there does seem reason for fields to become so small that
they become entities (that trap oil and gas) - and this relationship may
be better approximated by a Pareto.
The Pareto and lognormal form is similar but maybe one is better to
approximate field sizes than the other.
My question is do you think a Pareto distribution better approximates an
oil and gas size distribution than a lognormal (or vise-versa) and if so
why.
I am currently working on goodness of fit test to throw some more light on
this - but if anyone has any thing to say I'd appreciate some comments.
Thank you,
Kind regards
Beatrice
Geological and Nuclear Sciences
New Zealand
www.gns.cri.nz
* By using the ai-geostats mailing list you agree to follow its rules
( see http://www.ai-geostats.org/help_ai-geostats.htm )
* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED]
Signoff ai-geostats
--
***************************************
Chris Hlavka
NASA/Ames Research Center 242-4
Moffett Field, CA 94035-1000
(650)604-3328 FAX 604-4680
[EMAIL PROTECTED]
***************************************
Chris Hlavka
NASA/Ames Research Center 242-4
Moffett Field, CA 94035-1000
(650)604-3328 FAX 604-4680
[EMAIL PROTECTED]
***************************************
* By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
