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Surface-related multiple elimination ͑SRME͒ is an algorithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal preprocessing is required. Once predicted, the multiples are removed from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on assumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and primaries. In exchange for this "freedom from the subsurface," SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algorithmic requirements. There are several approaches to implementing 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and trueazimuth SRME. Field data examples confirm that ͑1͒ multiples predicted using true-azimuth 3D SRME are more accurate than those using zero-azimuth 3D SRME and ͑2͒ on-thefly interpolation produces excellent results.

Transcranial ultrasound imaging (TUI) is a diagnostic modality with numerous applications, but unfortunately, it is hindered by phase aberration caused by the skull. In this article, we propose to reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. First, the compressional sound velocity of the aberrator (i.e., the skull) is estimated using the bidirectional headwave technique. The medium is described with four layers (i.e., lens, water, skull, and water), and a fast marching method calculates the travel times between individual array elements and image pixels. Finally, a delay-and-sum algorithm is used for image reconstruction with coherent compounding. The point spread function (PSF) in a wire phantom image and reconstructed with the conventional technique (using a constant sound speed throughout the medium), and the proposed method was quantified with numerical synthetic data and experiments with a bone-mimicking plate and a human skull, compared with the PSF achieved in a ground truth image of the medium without the aberrator (i.e., the bone plate or skull). A phasedarray transducer (P4-1, ATL/Philips, 2.5 MHz, 96 elements, pitch = 0.295 mm) was used for the experiments. The results with the synthetic signals, the bone-mimicking plate, and the skull indicated that the proposed method reconstructs the scatterers with an average lateral/axial localization error of 0.06/0.14 mm, 0.11/0.13 mm, and 1.0/0.32 mm,

A virtual acoustic source inside a medium can be created by emitting a time-reversed point-source response from the enclosing boundary into the medium. However, in many practical situations the medium can be accessed from one side only. In those cases the time-reversal approach is not exact. Here, we demonstrate the experimental design and use of complex focusing functions to create virtual acoustic sources and virtual receivers inside an inhomogeneous medium with single-sided access. The retrieved virtual acoustic responses between those sources and receivers mimic the complex propagation and multiple scattering paths of waves that would be ignited by physical sources and recorded by physical receivers inside the medium. The possibility to predict complex virtual acoustic responses between any two points inside an inhomogeneous medium, without needing a detailed model of the medium, has large potential for holographic imaging and monitoring of objects with single-sided access, ranging from photoacoustic medical imaging to the monitoring of induced-earthquake waves all the way from the source to the earth’s surface.

Although joint migration inversion has been proposed for several years, a thorough derivation and description of the involved gradients was not published. In this paper, we derive the gradient of both the angle‐independent reflectivity and the velocity in a framework of acoustic angle‐independent joint migration inversion. With some further approximations taken, the conclusions shown in previous publications can also be reached from our new derivation.