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Kiama Blowhole Eruptions

Keywords: Poisson process, exponential distribution


Description

The ocean swell produces spectacular eruptions of water through a hole in the cliff at Kiama, about 120km south of Sydney, known as the Blowhole. The times at which 65 successive eruptions occurred from 1340 hours on 12 July 1998 were observed using a digital watch.

Jim Irish writes

Anyone who has visited the Blowhole more than once knows that the rate and volume of eruptions varies. This variation occurs at several timescales. We might expect that part is explained by the tides, so that eruptions are more frequent and spectacular when the tide is very high, and eruptions obviously depend on the presence of a large ocean swell generated by prolonged strong winds over the ocean well offshore from Kiama. Hence, any stochastic model fitted to data observed over a short period of time is only applicable to that period, and perhaps a few hours either side of the observations. But we might infer from the model fitted to those data that a similar model applies more generally.


Variable Description

Interval Waiting time between eruptions (seconds)

Download

Data file (tab-delimited text)

Source

The data was collected and contributed by Jim Irish, Faculty of Engineering, University of Technology, Sydney.

Analysis

kiama1.gif (4268 bytes) There is no clear trend over time.
kiama1.gif (4268 bytes) There is no evidence of correlation between successive waiting times.

Do eruptions arise according to a Poisson process? If so the waiting times should be exponentially distributed, and the variance should be equal to the square of the mean. The sample variance is actually smaller than exponential variance would suggest:

> mean(Interval) [1] 39.82812
> var(Interval) [1] 1139.097
> mean(Interval)^2
 [1] 1586.28

If the waiting times are exponential, then p should be uniformly distributed and q should be standard normal:

> m <- mean(Interval)
> p <- 1-exp(-Interval/m)
> q <- qnorm(p)
> qqnorm(q)
> abline(0,1)
kiama2.gif (4401 bytes) The upper tail of the waiting time distribution is similar to exponential, but the short waiting times are not short enough.

It could be that two eruptions arriving in very close succession are indistinguishable from a single longer eruption. This might explain the short lower tail.

 


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