Abstract
Generating new seismic responses from existing recordings is generally referred to as seismic
interferometry (SI). Conventially, the new responses are retrieved by simple crosscorrelation of
recordings made by separate receivers: a first receiver acts as `virtual source' whose response
is retrieved at the other receivers. The newly retrieved responses can be used to extract
receiver-receiver phase velocities, which often serve as input parameter for tomographic
inverse problems, or which can be linked to temporally varying parameters such as
hydrocarbon production and precipitation. For all applications, however, the accuracy of the
retrieved responses is of great importance. In practice, this accuracy is often degraded by
irregularities in the illumination pattern: correct response retrieval relies on a uniform
illumination of the receivers. Reformulating the theory underlying seismic interferometry by
crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of
these non-uniform illumination patterns by means of a so-called point-spread function (PSF).
We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western
Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just
east of the Andean mountain range. The array has a T-shape: the receivers along one of the
two lines act as virtual sources whose responses are retrieved by the receivers along the other
(perpendicular) line of receivers. Because SI by MDD relies on one-way wavefields, we select
time windows dominated by surface-wave noise traveling in a favorable direction, that is,
traversing the line of virtual sources before arriving at the receivers at which we aim to
reconstruct the virtual-source responses. These time windows are selected through a
frequency-dependent slowness analysis along the two receiver lines. From the selected time
windows, virtual-source responses are retrieved by computation of ensemble-averaged
crosscorrelations. Similarly, ensemble-averaged crosscorrelations between virtual sources are
computed: the point-spread function. We use the PSF to deconvolve the effect of illumination
irregularities and the source function from the virtual-source responses. The combined effect of
time-window selection and MDD results in more accurate surface-wave responses.
interferometry (SI). Conventially, the new responses are retrieved by simple crosscorrelation of
recordings made by separate receivers: a first receiver acts as `virtual source' whose response
is retrieved at the other receivers. The newly retrieved responses can be used to extract
receiver-receiver phase velocities, which often serve as input parameter for tomographic
inverse problems, or which can be linked to temporally varying parameters such as
hydrocarbon production and precipitation. For all applications, however, the accuracy of the
retrieved responses is of great importance. In practice, this accuracy is often degraded by
irregularities in the illumination pattern: correct response retrieval relies on a uniform
illumination of the receivers. Reformulating the theory underlying seismic interferometry by
crosscorrelation as a multidimensional deconvolution (MDD) process, allows for correction of
these non-uniform illumination patterns by means of a so-called point-spread function (PSF).
We apply SI by MDD to surface-wave data recorded by the Malargüe seismic array in western
Argentina. The aperture of the array is approximately 60 km and it is located on a plateau just
east of the Andean mountain range. The array has a T-shape: the receivers along one of the
two lines act as virtual sources whose responses are retrieved by the receivers along the other
(perpendicular) line of receivers. Because SI by MDD relies on one-way wavefields, we select
time windows dominated by surface-wave noise traveling in a favorable direction, that is,
traversing the line of virtual sources before arriving at the receivers at which we aim to
reconstruct the virtual-source responses. These time windows are selected through a
frequency-dependent slowness analysis along the two receiver lines. From the selected time
windows, virtual-source responses are retrieved by computation of ensemble-averaged
crosscorrelations. Similarly, ensemble-averaged crosscorrelations between virtual sources are
computed: the point-spread function. We use the PSF to deconvolve the effect of illumination
irregularities and the source function from the virtual-source responses. The combined effect of
time-window selection and MDD results in more accurate surface-wave responses.
| Original language | English |
|---|---|
| Publication status | Published - 2016 |
| Event | 76. Jahrestagung der Deutschen Geophysikalischen Gesellschaft - Muenster, Germany Duration: 14 Mar 2016 → 17 Oct 2016 |
Conference
| Conference | 76. Jahrestagung der Deutschen Geophysikalischen Gesellschaft |
|---|---|
| Abbreviated title | DGG 2016 |
| Country/Territory | Germany |
| City | Muenster |
| Period | 14/03/16 → 17/10/16 |