SAMPLING OF FAULT POPULATIONS USING SUBSURFACE DATA - A REVIEW

In favourable circumstances, seismic reflection data can give an unriv alled view of faulted rocks in the sub-surface, imaging features down to the seismic resolution (typically 20-30 m). The lack of finer detai l can, in part, be addressed by analysing well cores through the same rock volume. Samples of fault populations from such data often exhibit power-law size distributions where 'fault size' can be trace-length o r fault-displacement. Analysis of a synthetic fractal model (the 'frag mentation model') demonstrates that changing the dimension of the samp ling domain (e.g. volume to plane, plane to line) changes the power-la w exponent of the sample's size distribution. The synthetic model also suggests how best to treat faults that extend out of the sample area, and illustrates potential problems in comparing samples from very dif ferent scales (e.g. regional and detailed mapping). Analysis of a vari ety of interpreted seismic-reflection data sets has provided a range o f power-law exponents for different sample types: (i) fault-trace leng ths (two-dimensional samples): -1.1 to -2.0; (ii) fault-trace maximum displacements (two-dimensional sample): -1.0 to -1.5; (iii) 'arbitrary ' displacements (one-dimensional sample): -0.5 to -1.0. Fault-trace le ngths are very sensitive to truncation (resolution) effects, and rip r egions should be re-assessed using displacement gradients. Maximum dis placements, and displacements obtained by line-sampling, are much more robust attributes. Well data are useful in constraining the extrapola tion of populations to smaller scales. Fault populations scale differe ntly than earthquake populations, because the latter represent only th e instantaneous deformation, whereas fault populations represent the d eformation accrued over geological time. A valuable dataset to clarify these relationships would be a true three-dimensional sample of fault s in an actively-deforming area.