Irregularity and coarse spatial sampling of seismic data strongly affect the performances of processing and imaging algorithms. Therefore, interpolation is a usual pre-processing step in most of the processing workflows. In this work, we propose a seismic data interpolation method based on the deep prior paradigm: an ad-hoc Convolutional Neural Network is used as a prior to solve the interpolation inverse problem, avoiding any costly and prone-to-overfitting training stage. In particular, the proposed method leverages a multi resolution U-Net with 3D convolution kernels exploiting correlations in cubes of seismic data, at different scales in all directions. Numerical examples on different corrupted synthetic and field datasets show the effectiveness and promising features of the proposed approach.

Buried unexploded landmines are a serious threat in many countries all over the World. As many landmines are nowadays mostly plastic made, the use of ground penetrating radar (GPR) systems for their detection is gaining the trend. However, despite several techniques have been proposed, a safe automatic solution is far from being at hand. In this paper, we propose a landmine detection method based on convolutional autoencoder applied to B-scans acquired with a GPR. The proposed system leverages an anomaly detection pipeline: the autoencoder learns a description of B-scans clear of landmines, and detects landmine traces as anomalies. In doing so, the autoencoder never uses data containing landmine traces at training time. This allows to avoid making strong assumptions on the kind of landmines to detect, thus paving the way to detection of novel landmine models.

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