Sjoerd de Ridder

Kinematics of iterative interferometry in a passive seismic experiment

We study a novel approach to seismic interferometry; iterative interferometry. To utilize secondary Huygens sources that illuminate the medium from regions where primary sources are absent, we correlate the coda of correlations. We identify the leading terms in the second round of correlations and study their kinematics using correlation gathers. We discuss how iterative interferometry can improve the Green’s function estimation with respect to conventional interferometry.

Low frequency passive seismic interferometry for land data

Here we report results achieved by low-frequency seismic interferometry on a passive seismic land dataset recorded at a field in Saudi Arabia. Computed spectra for different portions of the data show a time varying ambient seismic wavefield displaying a diurnal pattern. At low frequencies (< 10 Hz) the ambient seismic wavefield mainly consists of surface waves in two modes, the fundamental mode propagates with a velocity of about 1250 ms. Results suggest that sufficient coherent energy is recorded between 1 Hz and 7 Hz for retrieval of a Rayleigh surface wave. The strength of the ambient seismic field affects the convergence rate of the correlations. The directionality in the ambient seismic field affects the radiation pattern of the virtual sources. Retrieved Rayleigh waves at low frequencies show spatial variation and dispersive behavior. Dispersion curve estimation opens opportunities for reservoir monitoring by background velocity estimation.

Seismic Attenuation from Ambient Noise across the North Sea Ekofisk Permanent Array

Quality factor (Q) or equivalently attenuationα 1⁄4 1 Qdescribes the amount of energy lost per cycle as a wave travels through a medium. This is important to correct seismic data amplitudes for near-surface effects, to locate subsurface voids or porosity, to aid seismic interpretation, or for characterizing other rock and fluid properties. Seismic attenuation can be variable even when there are no discernible changes in seismic velocity or density (Yıldırım et al., 2017, https://doi.org/10.1016/j.jappgeo.2016.11.010) and so provides independent information about subsurface heterogeneity. This study uses ambient noise recordings made on the Ekofisk Life of Field Seismic array to estimate Q structure in the near surface. We employ the method of X. Liu et al. (2015, https://doi.org/10.1093/gji/ggv357), which uses linear triplets of receivers to estimate Q—ours is the first known application of the method to estimate the Q structure tomographically. Estimating Q requires an estimate of phase velocity which we obtain using the method of Bloch and Hales (1968, https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/58/3/1021/116607/) followed by traveltime tomography. The Q structure at Ekofisk has features which can be related to local geology, showing that surface ambient noise recordings may provide a new and robust method to image Q. Our results suggest that there is a nonlinear relationship between Q and compression. They also may explain why it has been found that in the period range of 1 to 2 s considered here, ambient noise cross correlations along paths that span the North Sea Basin are unreliable: Such Q values would attenuate almost all ambient seismic energy during such a traverse.

Single- and Double-Sided Marchenko Imaging Conditions in Acoustic Media

In acoustic reflector imaging, we deploy sources and receivers outside a volume to collect a multisource, multioffset reflection response in order to retrieve the internal reflectivity of that volume. It has been shown that Greens functions inside the volume can be retrieved by single-sided wavefield focusing of the acquired reflection data, using so-called focusing functions, which can be computed by solving a multidimensional Marchenko equation. Besides the reflection data, this methodology requires a background model of the propagation velocity. We present several imaging conditions to retrieve the internal reflectivity of an acoustic medium with correct amplitudes and without artifacts, using the Greens functions and focusing functions that are derived from the Marchenko equation. We distinguish three types of imaging: 1) imaging by deconvolution, 2) imaging by double focusing, and 3) imaging by cross correlation. In all cases, reflectors can be approached either from above or from below. Imaging by deconvolution or double focusing requires single-sided illumination (meaning that sources and receivers are deployed at a single boundary above the volume only), whereas imaging by cross correlation requires double-sided illumination (meaning that sources and receivers are placed at two boundaries enclosing the volume). In order to achieve double-sided illumination, the required reflection response at the lower boundary can either be physically recorded or it can be retrieved from the reflection response at the upper boundary. When imaging by deconvolution or double focusing, the internal reflectivity is retrieved solely from primary reflections. When imaging by cross correlation, multiple reflections are focused at the image points, such that they contribute physically to the retrieved reflectivity values. This special feature can be beneficial for imaging weakly illuminated sections of strongly heterogeneous media.

To: “Introduction to special section: Ambient noise,” Sjoerd de Ridder, Florent Brenguier, Farnoush Forghani, Erica Galetti, Nori Nakata, and Cornelis Weemstra, Interpretation, 4, no. 3, SJi, doi: 10.1190/INT-2016-0627-SPSEINTRO.1.

The author names for the following two summaries originally appeared as Pascal and Yuan and Pascal and Halliday , but they should have read: Edme and Yuan formulate a novel acquisition and processing technique to derive surface-wave dispersion curves from seismic ambient noise. The authors show

Introduction to special section: Ambient noise

Many efforts of geophysical processing have traditionally been devoted to the separation, attenuation, and elimination of noise from seismic acquisition data. However, one geophysicist’s noise is another geophysicist’s signal. [Aki (1957)][1] formulated the spatial autocorrelation method, which