Direct migration of ambient seismic data

Aaron J. Girard, Jeffrey Shragge

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Utilising ambient seismic energy naturally propagating in the Earth as an alternative approach to active body-wave seismic investigations has been a topic of interest for a number of decades. However, because ambient surface-wave arrivals typically are of much greater amplitude than ambient body-wave energy, significant data signal processing and long recording times are required to mitigate this and other coherent noise sources, and to correlate sufficient reflected body-wave energy to converge to a stable image. Even for these scenarios, identifying and validating imaged body-wave reflection events remain challenging. In active-source investigations, extended imaging condition gathers are used to examine velocity (in)accuracy. Herein, we develop an ambient direct migration approach that uses a novel ambient (deconvolution) extended imaging condition. We simulate synthetic ambient-wavefield seismic data for two different models and use a field data set from Lalor Lake in Manitoba, Canada, to conduct a series of numerical experiments to demonstrate the velocity sensitivity and long-term stationarity of ambient-wavefield seismic data in the migration image domain. Tests with varying global velocity perturbations show a characteristic reflector moveout in deconvolution extended imaging condition gathers that can serve as a diagnostic of reflected ambient body-wave energy. We illustrate that this imaging formalism, under idealised circumstances, gives comparable results to conventional seismic methods, which extends the use of extended imaging condition gather-based image validation to ambient-wavefield seismic data scenarios. We assert that this may be a valuable tool for the validation of ambient migration techniques that to date have yielded largely inconclusive results.

Original languageEnglish
Pages (from-to)270-292
Number of pages23
JournalGeophysical Prospecting
Issue number1
Early online date6 Apr 2019
Publication statusPublished - 1 Jan 2020


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