We present a tomographic deconvolution procedure for highresolution imaging of velocity anomalies between reflecting interfaces. The key idea is to first invert reflection or transmission traveltimes for the background velocity model. A convolutional neural network (CNN) model is then trained to estimate the inverse to the blurred tomogram consisting of small scatterers in the background velocity model. We call this CNN a tomographic deconvolution operator because it deconvolves the blurring artifacts in traveltime slowness tomograms. This procedure is similar to that of migration deconvolution which deconvolves the migration butterfly artifacts in migration images. Results with synthetic examples show the effectiveness of this procedure in significantly sharpening the tomographic images of small scatterers.

Keywords:
reservoir characterization, schuster king abdullah university, slowness model, neural network, anomaly, artificial intelligence, applied geoscience, king abdullah university, tomographic deconvolution, slowness anomaly
Subjects:
Reservoir Characterization, Information Management and Systems, Seismic processing and interpretation
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