Nikhil Shah

A Strategy for Waveform Inversion without an Accurate Starting Model

SUMMARY A key limitation of waveform inversion as currently implemented is the need for a starting model of high accuracy or field data with low frequencies. Here we present a new approach - staged waveform inversion - designed to mitigate this need and thereby permit the application of waveform inversion to a much wider range of datasets.

A Phase-unwrapped Solution for Overcoming a Poor Starting Model in Full-wavefield Inversion

We present a new phase-unwrapped full-wavefield inversion (FWI) methodology for applying the technique to seismic data directly from a poor or simple starting model in an automated, robust manner. The well-known difficulty that arises with a poor starting model is a ‘cycle-skipped’ relationship between predicted and observed data at useable inversion frequencies. The local minimum convergence of cycle-skipped data is one of the root causes for inaccurately recovered models in practical applications of FWI. Further it is why practical applications to date have focussed on favourable datasets possessing very low frequencies and an accurate velocity model already known prior to applying FWI. Here we tackle the cycle-skipping problem by inverting the lowest useable frequency of the data using an unwrapped phase-only objective function. We minimise a smooth, phase-unwrapped residual, extracted from the data by exploiting the spatial continuity existing between adjacent traces. The majority of field datasets acquired today are spatially well enough sampled to be manipulated in this way. An application to highly cycle-skipped synthetic data from the Marmousi model shows the benefit of applying phase-unwrapped inversion to a dataset prior to starting conventional FWI.

Waveform inversion of surface seismic data without the need for low frequencies

Summary Waveform inversion is a technique with capability of generating velocity models with unprecedented resolution and clarity from seismic data. However it often requires unrealistically low frequencies in the data to achieve this. We propose a scheme designed to mitigate this need ‐ a necessary key step for realising the potential of the technique in a far wider range of datasets and targets than currently possible. The scheme operates by preceding the inversion of the field data by inversion of intermediate datasets ‐ synthesised by extracting the irrotational component of the phase mismatch at the lowest useable frequency. We demonstrate its effectiveness over the corresponding conventional approach by inverting data from the Marmousi model with a minimum frequency of 5Hz.

The Application of Full Waveform Seismic Inversion to a Narrow-azimuth Marine Dataset

We apply 3D anisotropic acoustic full-waveform inversion to a North Sea narrow-azimuth, marine-streamer dataset. We use a windowed strategy, with 3 stages, first focusing on mainly refracted arrivals with offsets up to (a) 1 km, (b) 2 km and then (c) 3 km with increasing iterations. We demonstrate that our recovered velocity model is realistic.

Waveform inversion from a poor starting model – using a residual ‘drip-feed’ strategy

We present a new waveform inversion scheme designed to avert the need for an accurate starting model and low frequency content in the data – a necessary key step in making the technique work on a much wider range of exploration datasets and targets than it currently can. The scheme operates by preceding the inversion of the field data by inversion of intermediate target datasets – synthesised out of the curl-free (irrotational) part of the phase mismatch at the lowest useable frequency. We demonstrate its effectiveness over the corresponding conventional approach by inverting data from the Marmousi model with a 1-D starting model and minimum frequency of 5Hz.