Improvement Wavelet Signal for 2D Seismic Data Using Wiener Deconvolution Method in Western Iraq

One of the geophysical techniques for processing seismic data for coherence noise removal and signal enhancement is the Wiener deconvolution method, which was applied to seismic data for a two-dimensional seismic survey project in the western Iraqi desert. In this project, the seismic data is largely composed of multiple wavelets and is considered to have a weak signal. Wiener deconvolution affects time-varying and spatially-varying spike or gapped deconvolution. The method was used in numerous experiments involving spiking deconvolution on seismic data. It was discovered that 200 milliseconds is the ideal operator length, considering that the prediction lag is 2 milliseconds, the equivalent of the sample rate. The results were compared with the raw data using an amplitude spectrum graph and autocorrelogram. The results discovered that it effectively compresses the wavelet and strengthens the final signal. A second method, wiener gapped deconvolution, was applied through various examinations of the seismic data, the results showed that the best values for the operator length of 200 milliseconds and prediction lag were 20 milliseconds. The accuracy of the results is compared to the raw data using an amplitude spectrum graph and an autocorrelogram. The second method was discovered to be averagely efficient in removing multiples from the data. As a result, it is thought that Wiener's method is more effective for spike deconvolution in compressing the waves and strengthening the signal than gapped deconvolution.