Borehole dipole shear reflection imaging has attracted widespread attention in the evaluation of heterogeneous reservoirs in recent years due to its good performance in reflection amplitude, detecting depth and directivity. However, the conventional migration method based on one-way acoustic equation has been proved to be improper for the wave excited by dipole source in this paper. An elastic wave reverse time migration (RTM) is developed for more exactly imaging of dipole reflected wave to find the location of effective reservoir. The problem of storage in RTM is solved by a boundary storage strategy improved for borehole environment in our research, which has been proved to be very effective by the simulated data. The processing of 2D simulated data of formation models with cave, crack and formation interface indicate the good performance of this migration method. The migration results of the field data with this method agree well with the borehole electric imaging logging data. Furthermore, our researches reveal that the effects of the unknown formation parameters, shielding interface of former reflector, would make the interpretation of the migration image difficult.
Motivation: SASHA T1 has high accuracy but low precision due to the low SNR of T1-weighted images. Convolutional neural network has the potential to improve SASHA T1 precision by using spatio-temporal correlations. Goal(s): The aim of this study is to develope a convolutional neural network for improving SASHA T1 precision. Approach: We implemented a convolutional neural network (DeepDenoiseNet) and trained it using synthesized SASHA images from co-registered high-quality T1, T2, and M0 images. Different-level noise was added to simulate low SNR SASHA images. Results: DeepDenoiseNet could reduce the impaction from noise and improve SASHA T1 precision. Impact: The deep convolutional neural network trained with synthesized images and simulated noise could improve SASHA T1 precision.
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