Abstract

Abstract. Seismic amplitude-versus-angle (AVA) methods are a powerful means of quantifying the physical properties of subglacial material, but serious interpretative errors can arise when AVA is measured over a thinly-layered substrate. A substrate layer with a thickness less than 1/4 of the seismic wavelength, λ, is considered "thin", and reflections from its bounding interfaces superpose and appear in seismic data as a single reflection event. AVA interpretation of subglacial till can be vulnerable to such thin-layer effects, since a lodged (non-deforming) till can be overlain by a thin (metre-scale) cap of dilatant (deforming) till. We assess the potential for misinterpretation by simulating seismic data for a stratified subglacial till unit, with an upper dilatant layer between 0.1–5.0 m thick (λ / 120 to > λ / 4, with λ = 12 m). For dilatant layers less than λ / 6 thick, conventional AVA analysis yields acoustic impedance and Poisson's ratio that indicate contradictory water saturation. A thin-layer interpretation strategy is proposed, that accurately characterises the model properties of the till unit. The method is applied to example seismic AVA data from Russell Glacier, West Greenland, in which characteristics of thin-layer responses are evident. A subglacial till deposit is interpreted, having lodged till (acoustic impedance = 4.26±0.59 × 106 kg m−2 s−1) underlying a water-saturated dilatant till layer (thickness < 2 m, Poisson's ratio ~ 0.5). Since thin-layer considerations offer a greater degree of complexity in an AVA interpretation, and potentially avoid misinterpretations, they are a valuable aspect of quantitative seismic analysis, particularly for characterising till units.

Highlights

  • Seismic reflection methods provide a powerful means of imaging the bed of glaciers and ice masses, and are increasingly used for quantifying substrate material properties (e.g. Smith, 1997; Nolan and Echelmeyer, 1999; Anandakrishan, 2003; Peters et al, 2007, 2008)

  • We propose an interpretation strategy that honours the structure of the till deposit, and apply this to seismic data acquired on the Russell Glacier outlet of the West Greenland Ice Sheet

  • Seismic AVA analysis is a powerful method for quantifying the physical properties of subglacial material, serious misinterpretations can result where thin stratifications are present in the substrate

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Summary

Introduction

Seismic reflection methods provide a powerful means of imaging the bed of glaciers and ice masses, and are increasingly used for quantifying substrate material properties (e.g. Smith, 1997; Nolan and Echelmeyer, 1999; Anandakrishan, 2003; Peters et al, 2007, 2008). The method has had notable use in glaciology for identifying subglacial lakes (Peters et al, 2008), thick sequences of dilatant (deforming) till (Anandakrishnan, 2003; Peters et al, 2007) and transient changes in subglacial hydrology (Nolan and Echelmeyer, 1999; Kulessa et al, 2008). A till deposit can be structurally complex, with abrupt variations (both vertical and lateral) in physical properties (Evans et al, 2006) that, critically, are on a smaller spatial scale than the seismic wavelength (∼ 10 m in glaciology, depending on source characteristics and ice thickness; Smith, 2007). We propose an interpretation strategy that honours the structure of the till deposit, and apply this to seismic data acquired on the Russell Glacier outlet of the West Greenland Ice Sheet. By acknowledging the potential for a thinly-layered structure, we are able to recover more information from the AVA response than is possible with conventional interpretation methods

Reflection coefficients and amplitude-versus-angle responses
Thin-layers in glaciological AVA analysis
Ray-tracing of reflected travel-times
Effective reflectivity
Forward modelling of seismic data
Composite AVA curves
Glaciological interpretation of thin-layer AVA responses
Conventional interpretation of AVA responses
Thin-layer interpretation for AVA responses
Application to real data
Data pre-processing
AVA analysis
Thin-layer analysis
Findings
Discussion and implications
Conclusions
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