Abstract

A sparse 3D seismic survey was acquired over the Blötberget iron-oxide deposits of the Ludvika Mines in south-central Sweden. The main aim of the survey was to delineate the deeper extension of the mineralisation and to better understand its 3D nature and associated fault systems for mine planning purposes. To obtain a high-quality seismic image in depth, we applied time-domain 3D acoustic full-waveform inversion (FWI) to build a high-resolution P-wave velocity model. This model was subsequently used for pre-stack depth imaging with reverse time migration (RTM) to produce the complementary reflectivity section. We developed a data preprocessing workflow and inversion strategy for the successful implementation of FWI in the hardrock environment. We obtained a high-fidelity velocity model using FWI and assessed its robustness. We extensively tested and optimised the parameters associated with the RTM method for subsequent depth imaging using different velocity models: a constant velocity model, a model built using first-arrival traveltime tomography and a velocity model derived by FWI. We compare our RTM results with a priori data available in the area. We conclude that, from all tested velocity models, the FWI velocity model in combination with the subsequent RTM step, provided the most focussed image of the mineralisation and we successfully mapped its 3D geometrical nature. In particular, a major reflector interpreted as a cross-cutting fault, which is restricting the deeper extension of the mineralisation with depth, and several other fault structures which were earlier not imaged were also delineated. We believe that a thorough analysis of the depth images derived with the combined FWIRTM approach that we presented here can provide more details which will help with better estimation of areas with high mineralization, better mine planning and safety measures.

Highlights

  • Application of reflection seismics has increased manifolds in the past decade for targets ranging from shallow to deep mineral deposits associated with the hardrock environment

  • We explore the potential of time-domain early-arrival acoustic full-waveform inversion (FWI) to build a high-resolution P-wave velocity model for subsequent depth imaging using sparse 3D seismic data acquired over an iron-oxide mineralisation target at Ludvika (Central Sweden)

  • We investigated the application of reverse-time migration (RTM) for subsequent depth imaging to produce high-quality depth images consistent with the FWI-derived velocity model, which may otherwise require some smoothing to be used in ray-based migrations (e.g., Kirchhoff pre-stack depth migration (PreSDM))

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Summary

Introduction

Application of reflection seismics has increased manifolds in the past decade for targets ranging from shallow to deep mineral deposits associated with the hardrock environment (see Malehmir et al, 2012 and references therein). FWI brings unprecedented resolution in elastic/anelastic parameter models as compared to ray-based methods, it requires good-quality data, ideally with enhanced low frequencies and various recorded arrivals sampling the subsurface targets over a broad range of scattering angles These conditions are hardly met by the seismic data acquired on land. We explore the potential of time-domain early-arrival acoustic FWI to build a high-resolution P-wave velocity model for subsequent depth imaging using sparse 3D seismic data acquired over an iron-oxide mineralisation target at Ludvika (Central Sweden). There is a thin but heterogeneous weathering layer (Maries et al, 2017; Bräunig et al, 2020), as well as a small velocity gradient, which limits the penetration depth of refracted arrivals Based on this Ludvika 3D dataset, we developed a data preprocessing workflow and a FWI strategy applicable to hardrock seismic data for building a high-resolution velocity model. We conclude our case study in the ‘Conclusions’ section

Geological background and earlier borehole and seismic studies
Seismic data
Full-waveform inversion
Reverse-time migration
Application to the Ludvika 3D dataset
FWI results
Data preprocessing
Implementation and computational aspects
RTM results
Findings
Conclusions
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