Artworks are an inseparable part of the cultural heritage of societies and provide us with a unique look at cultural developments through time and space. For the best possible conservation, it is paramount to know the constituent materials, condition, and construction techniques of the objects (e.g. painting on wood, fresco, sculpture). Such information is required not only for the surfaces of the objects, but also for the interiors; in the imaging discipline, this is known as depth imaging. Here, we introduce a new method for non-invasive depth imaging as an alternative to traditional non-invasive methods when the latter cannot be used to obtain the required information. We use ultrasonic transverse-wave transmission measurements and turn them into virtual reflection measurements. We achieve this by applying seismic interferometry with active sources. Obtaining reflection measurements by seismic interferometry allows us to apply an advanced imaging technique – prestack depth migration, as used in seismic exploration – to produce a high-resolution depth image of an object. We apply our method to ultrasonic data recorded on a mockup of a painting on a wooden support. We validate our method by comparing our results with an image from X-ray computed tomography.

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Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, the SI responses are retrieved by simple crosscorrelation of recordings made by separate receivers: one of the receivers acts as a ‘virtual source’ whose response is retrieved at the other receivers.When SI is applied to recordings of ambient seismic noise, mostly surface waves are retrieved. The newly retrieved surface wave responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the tempo- ral stability of the multiply scattered arrivals of the newly retrieved surface wave responses. Temporal variations in the stability and/or arrival time of these multiply scattered arrivals can often be linked to temporally varying parameters such as hydrocarbon production and precip- itation. For all applications, however, the accuracy of the retrieved responses is paramount. Correct response retrieval relies on a uniform illumination of the receivers: irregularities in the illumination pattern degrade the accuracy of the newly retrieved responses. In practice, the illumination pattern is often far from uniform. In that case, simple crosscorrelation of separate receiver recordings only yields an estimate of the actual, correct virtual-source response. Re- formulating the theory underlying SI by crosscorrelation as a multidimensional deconvolution (MDD) process, allows this estimate to be improved. SI by MDD corrects for the non-uniform illumination pattern by means of a so-called point-spread function (PSF), which captures the irregularities in the illumination pattern. Deconvolution by this PSF removes the imprint of the irregularities on the responses obtained through simple crosscorrelation. We apply SI by MDD to surface wave data recorded by theMalargue 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 recorded by the receivers along the other (perpendicular) line.We select time windows dominated by surface wave noise travelling in a favourable direction, that is, traversing the line of virtual sources before arriving at the receivers at which we aim to retrieve 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, estimates of virtual-source responses are retrieved by means of crosscorrelations. Similarly, crosscorrelations between the positions of the virtual sources are computed to build the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface wave responses.@en

Obtaining new seismic responses from existing recordings is generally referred to as seismic interferometry (SI). Conventionally, these seismic interferometric responses are retrieved by simple crosscorrelation of recordings made by separate receivers: a first receiver acts as a 'virtual source' whose response is retrieved at the other receivers. When surface waves are retrieved, the newly retrieved responses can be used to extract receiver-receiver phase velocities. These phase velocities often serve as input parameters for tomographic inverse problems. Another application of SI exploits the temporal stability of the multiply scattered arrivals (the coda). For all applications, however, the accuracy of the retrieved responses is paramount. 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, which makes it very well suited for the application of SI by MDD. 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 retrieve the virtual-source responses. These time windows are selected based upon the slownesses 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 the positions of the virtual sources are computed: the PSF. We use the PSF to deconvolve the effect of illumination irregularities and the source function from the virtual-source responses retrieved by crosscorrelation. The combined effect of time-window selection and MDD results in more accurate and temporally stable surface-wave responses.@en