Seismic imaging provides much of our information about the Earth's crustal structure. The principal source of imaging errors derives from simplistic modelled predictions of the complex, scattered wavefields that interact with each subsurface point to be imaged. A new method of wavefield extrapolation based on inverse scattering theory in mathematical physics produces accurate estimates of these subsurface scattered wavefields, while still using relatively little information about the Earth's properties. We use it for the first time to create real target-oriented seismic images of a North Sea field. We synthesise underside illumination from surface reflection data, and use it to reveal subsurface features that are not present in an image from conventional migration of surface data. To reconstruct underside reflections, we rely on the so-called downgoing focusing function, whose coda consists entirely of transmission-born multiple scattering. As such, with the method presented here, we provide the first field data example of reconstructing underside reflections with contributions from transmitted multiples, without the need to first locate or image any reflectors in order to reconstruct multiple scattering effects.

Heterogeneous, non-stationary noise sources can cause traveltime errors in noise-based seismic monitoring. The effect worsens with increasing temporal resolution. This may lead to costly false alarms in response to safety concerns and limit our confidence in the results when these systems are used for quasi real-time monitoring of subsurface changes. We therefore develop a new method to quantify and correct these traveltime errors to more accurately monitor subsurface conditions at daily or even hourly timescales. This is based on the inversion of noise correlation asymmetries for the time-dependent distribution of noise sources. The source model is then used to simulate time-dependent ambient noise correlations. The comparison to correlations computed for homogeneous noise sources yields 1 Downloaded 04/23/17 to 132.239.1.231. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ traveltime errors that translate into spurious changes of the subsurface. The application of our method to data acquired at Statoil's SWIM array, a permanent seismic installation at the Oseberg field, demonstrates that fluctuations in the noise source distribution may induce apparent velocity changes of 0.25 % within one day. Such biases thereby likely mask realistic subsurface variations expected on these timescales. These errors are systematic, dependent primarily on the noise source location and strength, and not on inter-station distance. Our method can then be used to correct for source-induced traveltime errors by subtracting these quantified biases in either data or model space. It can furthermore establish a minimum threshold for which we may reliably attribute traveltime changes to actual subsurface changes, should we not correct for these errors. In addition to the aforementioned real data scenario, we apply our method to a synthetic case for a daily passive monitoring overburden feasibility study. Both synthetics and field experiments validate the method's theory and application.2 Downloaded 04/23/17 to 132.239.1.231. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/ source distribution. Finally, instead of blindly performing waveform inversion with these biased traveltimes, we formulate how to remove these errors so that our velocities are unaffected by noise source non-stationarity. Preferably, this is done in the data space before proceeding to the tomography. We now discuss these steps in further detail.The SWIM array, shown in Figure 1, consists of 172 four-component receivers (MEMS accelerometer and hydrophone) linked through a single ocean-bottom cable. The configuration lies along the ocean floor, roughly 108 m below mean sea level, about 150 m south of the Oseberg C platform. Its purpose is to monitor cuttings injected into the overburden.Receiver spacing is ∼ 50 m for receivers on the outside boundaries of the array and reduces to ∼ 25 m for receivers in its inner portion. The spread of the array's outer right-side is around 1.74...

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[1] We present the first results from applying the wavelet correlation analysis to study physical properties of near top marine sediments. This technique applied to the synthetic and field data yields well resolved group velocity dispersion curves for the first two shear modes and surface waves. This method can be potentially useful for detecting of different seismic modes at frequencies masked by other neighboring modes. This novel technique is superior to traditional cross-correlation and Fourier analysis for inferring the physical properties of near surface marine sediments, such as density, porosity variations in depth and distance under diverse field conditions.

Methods for wavefield injection are commonly used to extrapolate seismic data in reverse time migration (RTM). Injecting a single component of the acoustic field, for example, pressure, leads to ambiguity in the direction of propagation. Each recorded wavefront is propagated both upward and downward, and spurious (or ghost) reflectors are created alongside real reflectors in the subsurface image. Thus, wavefield separation based on the combination of pressure and particle velocity data is generally performed prior to imaging to extract only the upgoing field from multicomponent seabed or towed marine seismic recordings. By instead combining vector-acoustic (VA) data with monopoleand dipole-type propagators in the extrapolation of shot or receiver gathers, we show that wavefield separation (or deghosting) can instead be performed "on-the-fly" at limited additional cost. This strategy was successfully applied to a line of a North Sea ocean-bottom cable data set, acquired over the Volve field. We then evaluate additional advantages over standard RTM with decomposed fields such as improved handling of the directivity information contained in the acquired VA data for clearer shallow sections and better focused space-lag common image gathers, and imaging of the downgoing component without the need for additional finite-difference modeling via mirror migration. Finally, we prove the robustness of our method with respect to sparse and irregular receiver sampling.