Seismic methods have the benefit of being noninvasive while providing continuous field-scale (hundreds of meters) information on subsurface characteristics of permafrost-affected soils. Imaging approaches based on surface wave propagation (e.g. MASW) are effective when characterizaing near-surface permafrost alteration (e.g. active zone freeze/thaw cycles) for at least two reasons: (1) energetic propagations within the top 10s of meters of the subsurface; (2) its direct indications on shear wave velocity, a sensitive indicator of soil matrix properties. We present a four-phase rock physics model developed for mapping frozen soil material properties to seismic observables. We predict seasonal variations in P- and S-wave velocities from the rock physics model based on existing in situ ground temperature measurements. We also conduct numerical simulations of seismic wave propagations based upon velocity models derived from rock physics model predictions. Surface wave dispersion analysis results generated from the resultant synthetic seismograms show that seismic methods, especially surface-wave-based approaches, are very promising approaches for delineating subsurface features in permafrost environments such as active layer thickness (ALT) variations, ice saturation, unfrozen water content, and soil texture, etc.
Uncertainties in marine controlled source electromagnetic (CSEM) data consist of two independent parts: measurement noise and position uncertainties. Measurement noise can be readily determined using stacking statistics in the Fourier domain. The uncertainties due to errors in position can be estimated using perturbation analysis given estimates of the uncertainties in transmitter-receiver geometries. However, the various geometric parameters are not independent (e.g. change in antenna dip affects antenna altitude, etc.) so how uncertainties derived from perturbation analysis can be combined to derive error-bars on CSEM data is not obvious. In this study, we use data from the 2009 survey of the Scarborough gas field to demonstrate that (a) a repeat tow may be used to quantify uncertainties from geometry, (b) perturbation analysis also yields a good estimate of data uncertainties as a function of range and frequency so long as the components are added arithmetically rather than in quadrature, and (c) lack of a complex error structure in inversion yields model results which are unrealistic and leads to “over-selling” of the capabilities of CSEM at any particular prospect.
The ability of the marine controlled source electromagnetic method to resolve anisotropy in the sediment conductivity is not very well understood. In this study, we address the resolvability of anisotropy using a Bayesian approach. Two markedly different methods, slice sampling and reversible jump Markov Chain Monte Carlo have been used for the Bayesian inversion of a synthetic model of a resistive oil reservoir trapped beneath the seabed. We use this to identify which components of data can provide the strongest constraints on anisotropy in the overburden, reservoir and underlying sediments.