Detection of defects in highly attenuating materials using ultrasonic least-squares reverse time migration with preconditioned stochastic gradient descent.

Accurate detection and characterization of defects in high-density polyethylene (HDPE) pipe materials are very important in assessing the structural integrity of critical structures in the nuclear industry. One specific challenge here is the presence of the viscoelastic attenuation of this material, which can lead to resolution degradation and loss of detail in ultrasound imaging. In this work, an effective ultrasonic imaging technique using the least-squares reverse time migration with preconditioned stochastic gradient descent (LSRTM-PSGD) is developed to improve image quality. Compared with standard ultrasonic imaging methods which only consider the direct ray path of ultrasound, least-squares reverse time migration (LSRTM) is a powerful wave-equation-based approach and it has the ability to account for rapid spatial velocity variations and to utilize all wavefield information. The LSRTM is an inversion method, which iteratively updates the reflectivity model by minimizing the modeled data and measured data. The proposed LSRTM-PSGD combines the advantages of stochastic gradient descent (SGD) and adaptive learning rate. The SGD updates the parameter on each transmitter and the fluctuation of SGD can enable it to reach a better minimum, thus improving the imaging quality. Compared with the conventional LSRTM algorithm using a fixed step size, the proposed LSRTM-PSGD algorithm can use the adaptive moment estimation to calculate the adaptive learning rate for the parameter, thereby updating the parameter appropriately. The performance of the LSRTM-PSGD algorithm is tested with experimental data. The results show high-quality reconstructed images with good resolution for defect identification in HDPE pipe materials, especially for deep defects.