We experimentally validate a relatively recent electrokinetic formulation of the streaming potential (SP) coefficient as developed by Pride (1994). The start of our investigation focuses on the streaming potential coefficient, which gives rise to the coupling of mechanical and electromagnetic fields. It is found that the theoretical amplitude values of this dynamic SP coefficient are in good agreement with the normalized experimental results over a wide frequency range, assuming no frequency dependence of the bulk conductivity. By adopting the full set of electrokinetic equations, a full-waveform wave propagation model is formulated. We compare the model predictions, neglecting the interface response and modeling only the coseismic fields, with laboratory measurements of a seismic wave of frequency 500 kHz that generates electromagnetic signals. Agreement is observed between measurement and electrokinetic theory regarding the coseismic electric field. The governing equations are subsequently adopted to study the applicability of seismoelectric interferometry. It is shown that seismic sources at a single boundary location are sufficient to retrieve the 1D seismoelectric responses, both for the coseismic and interface components, in a layered model.

We formulated an anisotropic eikonal tomography approach for phase velocities based on a two‐dimensional elliptical‐anisotropic wave equation. We can fit the parameters of the ellipse directly from measured first‐order traveltime surface gradients and constrain these parameters to vary smoothly over space. The method is applied to Scholte waves in virtual seismic sources from stations in the Life of Field Seismic Ocean Bottom Cable array installed over the Ekofisk field. The fast directions of the azimuthally anisotropic Scholte wave velocities form a large circular pattern over the Ekofisk field. This pattern dominates the Scholte wave phase velocities at Ekofisk between 0.7 and 1.1 Hz. It results from the overburden stress state and from seafloor subsidence induced by decades of hydrocarbon extraction.

We show that a reliable and statistically significant group velocity time‐lapse difference between 2004 and 2010 can be retrieved from ambient seismic noise in an offshore hydrocarbon exploitation setting. We performed a direct comparison of Scholte wave group velocity images obtained using regularized tomography. We characterize the expected variation in group velocity images from the 2004 or 2010 recordings that result from fluctuations in the cross correlations by looking at cross correlations of portions of the recordings. We prove that the time‐lapse difference is statistically significant. The time‐lapse group velocity image from ambient noise data shows strong similarities with a time‐lapse phase velocity map obtained from controlled source data. The most striking features are a northern and a southern group velocity increase due to compaction and subsidence as a result of reservoir production.

With the advent of large and dense seismic arrays, novel, cheap, and fast imaging and inversion methods are needed to exploit the information captured by stations in close proximity to each other and produce results in near real time. We have developed a sequence of fast seismic acquisition for dispersion curve extraction and inversion for 3D seismic models, based on wavefield gradiometry, wave equation inversion, and machine-learning technology. The seismic array method that we use is Helmholtz wave equation inversion using measured wavefield gradients, and the dispersion curve inversions are based on a mixture of density neural networks (NNs). For our approach, we assume that a single surface wave mode dominates the data. We derive a nonlinear relationship among the unknown true seismic wave velocities, the measured seismic wave velocities, the interstation spacing, and the noise level in the signal. First with synthetic and then with the field data, we find that this relationship can be solved for unknown true seismic wave velocities using fixed point iterations. To estimate the noise level in the data, we need to assume that the effect of noise varies weakly with the frequency and we need to be able to calibrate the retrieved average dispersion curves with an alternate method (e.g., frequency wavenumber analysis). The method is otherwise self-contained and produces phase velocity estimates with tens of minutes of noise recordings. We use NNs, specifically a mixture density network, to approximate the nonlinear mapping between dispersion curves and their underlying 1D velocity profiles. The networks turn the retrieved dispersion model into a 3D seismic velocity model in a matter of seconds. This opens the prospect of near-real-time near-surface seismic velocity estimation using dense (and potentially rolling) arrays and only ambient seismic energy.