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Bayesian Statistics and Helium Isotopes

Don L. Anderson

Seismological Laboratory, California Institute of Technology, Pasadena, CA 91125

Some Earth scientists mistrust formal statistics, preferring to rely upon geological insight and experience in treating sampled data. There are formal ways of incorporating prior expectations into statistical treatments of data and evaluating hypotheses. For example, if it is believed that the mid-ocean ridge basalt (MORB) reservoir is uniform and restricted in value for the helium isotope ratio 3He/4He (R) to 7 – 9 Ra, where Ra is the atmospheric value, then values outside this range can be assigned a prior probability of zero. Likewise, if ridge depths shallower than 2.5 km, for example, are thought to be contaminated by ocean-island basalt (OIB) magma from hotspots (assumed to represent a different population) then such shallow samples can be assigned a zero prior probability of representing MORB, and the “convecting mantle”.

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The use of prior probabilities and subjective constraints external to the dataset is known as Bayesian statistics. Bayesian reasoning is common in statistical treatments of noble gas data. It is assumed that there are two populations in isotopic data – the MORB reservoir, corresponding to the “convecting degassed upper mantle”, and the OIB reservoir, assumed to be an isolated, more primitive, less-degassed, more variable reservoir in the lower mantle [Allègre, 1987; Allegre et al., 1995].

A compilation of all 3He/4He measurements along the global spreading ridge system gives [Anderson, 2000a; Anderson, 2000b]:

He/4He = 9.06 ± 3.26 Ra

If all samples from depths shallower than 2.5 km, and all values greater than 9.5 Ra are excluded from the data the result is:

3He/4He = 7.47 ± 1.95 Ra

The data from the North Atlantic only give:

3He/4He = 9.37 ± 2.45 Ra

If we exclude all values greater than 9.5 Ra the result is:

3He/4He = 7.87 ± 0.69 Ra

The selected subsets of data give values much closer to the initial geochemical expectations than the whole dataset taken together.

Which kind of statistical approach is preferable in this situation? Bayesian methods have a long and controversial history. Although the dominant statistical culture is still “normal” or “objective” statistics, also called frequentism, Bayesian reasoning is emerging from an intuitive to a conscious and formal level in many fields of science. Subjective probability appears to be a natural concept developed by the human mind to quantify the plausibility of events under circumstances of uncertainty. Bayes' theorem is seen by some as a natural way of reasoning, e.g.

There is an apparent contradiction between the cultural background in statistics and the intuition of geologists. Geologists' intuition resembles the Bayesian approach. On the other hand, a common objection is that science must be objective – there is no room for belief. Science is not a matter of religion.

Nevertheless, there are good reasons for applying Bayesian methods to 3He/4He datasets. First of all, 3He/4He is a ratio. It cannot be negative and conclusions should not depend on whether it or its inverse is analysed. In the absence of information to the contrary it can be assumed that all values of R from 0 to infinity are equally probable in the underlying distribution (i.e., the source, prior to sampling and averaging). Sampling of such a source, according to the central limit theorem (CLT) [Meibom & Anderson, 2003], will yield a peaked distribution that, in the limit of a large sample volume, is Gaussian. Most geochemical samples can be considered to have sampled fairly large volumes. These considerations are more critical for 3He/4He than for heavier isotopes since the spread of values about the mean is larger, and median values are not far from R = 0.

Distributions are commonly asymmetric and skewed. Medians of 3He/4He data are more robust measures than the arithmetic means, with which they commonly disagree. These considerations show that log-normal distributions are more appropriate than linear Gaussian distributions. These are relatively mild applications of Bayesian reasoning. Stronger Bayesian priors would involve placing a zero prior probability on certain ranges of values of the parameter being estimated, or on external parameters. For example, one may wish to put a prior probability of zero on values of R > 9 Ra, or on values obtained from water depths of less than 2.5 km.

Histogram of 3He/4He data from close to mid-ocean ridges

Basaltic volcanism is by nature an integrator of the underlying source. All volcanoes average, to a greater or lesser extent, underlying heterogeneities. To determine the true heterogeneity of the mantle samples from a large variety of environments are required, including fast and slow spreading ridges, small off-axis seamounts, fracture zones, new and dying ridges, various ridge depths, overlapping spreading centers, melt-starved regions, unstable ridge systems such as back-arc ridges, and so on. Various materials enter subduction zones, including sediments, altered oceanic crust and peridotite, and some of these are incorporated into the upper mantle. To the extent that anomalous materials are excluded, or anomalous regions left unsampled, the degree of true intrinsic heterogeneity will be unknown. In essence, one must sample widely, collecting specimes that represent different degrees of melting and different source volumes.

The main distinguishing feature of the Bayesian approach is that it makes use of more information than the standard statistical approach. Whereas the latter is based on analysis of “hard data”, i.e., data derived from a well-defined observation process, Bayesian statistics accommodates “prior information” which is usually less well specified and may even be subjective. This makes Bayesian methods potentially more powerful, but also imposes the requirement for extra care in their use. In particular, we are no longer approaching an analysis in an “open-minded” manner, allowing the data to determine the result. Instead, we input “prior information” about what we think the answer is before we analyse the data! The danger of subjective Bayesian priors is that beliefs become immune to data.


  • Allègre, C.J., Isotope geodynamics, Earth Planet Sci Lett, 86, 175-203, 1987.
  • Allegre, C.j., M. Moreira, and T. Staudacher, 3He/4He dispersion and mantle convection, Geophys. Res. Lett., 2325-2328, 1995.
  • Anderson, D.L., The statistics and distribution of helium in the mantle, International Geology Review, 42, 289-311, 2000a.
  • Anderson, D.L., The statistics of helium isotopes along the global spreading ridge system and the Central Limit Theorem, Geophys. Res. Lett., 27, 2401-2404, 2000b.
  • Anderson, D.L., A statistical test of the two reservoir model for helium, Earth planet. Sci. Lett., 193, 77-82, 2001.
  • Botz, R., G. Winckler, R. Bayer, M. Schmitt, M. Schmidt, D. Garbe-Schonberg, P. Stoffers, and J.K. Kristjansson, Origin of trace gases in submarine hydrothermal vents of the Kolbeinsey Ridge, north Iceland, Earth planet. Sci. Lett., 171, 83-93, 1999.
  • Farley, K.A., and E. Neroda, Noble gases in the Earth's mantle, Ann. Rev. Earth Planet. Sci., 26, 189-218, 1998.
  • Meibom, A., and D.L. Anderson, The central limit theorem, Earth planet. Sci. Lett., in press, 2003.
    Sarda, P., M. Moreira, and T. Staudacher, Argon-lead isotopic correlation in mid-Atlantic Ridge basalts, Science, 283, 666-668, 1999.
last updated 29th March, 2005