Data Continuation for Data Regularization and Internal Multiples

Seismic data collected in the field are often not ideal for processing. The process known as data continuation computes data not recorded from those that are recorded so that data requirements for processing techniques can be met. Although there are many techniques of data continuation currently used in seismic processing, the majority of these assume that the seismic wave velocity is either constant or varying only with depth. A notable exception is the downward continuation of data, often referred to as survey sinking, for which techniques applicable in most velocity models exist. We extend data continuation techniques used to fill in missing data to velocity models in which caustics are generated in the wavefield. To do this, we use a method based on the composition of Fourier integral operators. To demonstrate that this method doesn’t introduce false reflections, we show that the composite operator is also a Fourier integral operator. We illustrate the utility of this theory with a synthetic example, with caustics, in which we fill in missing traces in a shot record. This method is computationally more expensive than similar methods that assume simple velocity models. First order internal multiples are a source of errors seismic imaging. Artifacts caused by internal multiples are often similar to true reflectors and thus can be difficult to attenuate. Typically multiples are estimated in the data and then subtracted from the data before an image is created. We propose a method by which artifacts in the image are estimated as part of the imaging process; an integral part of this method is the downward continution of data. i i

Variability of magnetic soil properties in Hawaii

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