Short Note Enhanced prestack noise removal

In a previous paper, Abma and Claerbout(1994), presented a technique for separating signal and noise. This technique was fairly expensive since it used an inversion routine that required many iterations to get a reasonable result. Generally the process worked well, but occasionally the traces that contained high amplitude noise showed weak signal on the output. The cause of both the high cost and erratic results can be attributed to the difficulty the inversion routine had in predicting noise. With spiky noise, the inversion was unlikely to reconstruct the noise accurately, since noise is generally broadband and will not be easily reconstructed from the response of a filter that has a bandlimited response. In this paper, I improve the results of the inversion by initializing the noise estimate with the result of a lateral prediction filter. This initialization is similar to that used in Abma(1995) except that the process is applied to prestack data and the inversion is unchanged from Abma and Claerbout(1994). This initialization improves the results and reduces the cost of the process. I show comparisons of the results of using the inversion with the initial noise estimate being zero as in Abma and Claerbout(1994) and then with the initial noise estimate being the result of a lateral prediction filter on both synthetic and real data.