Multiresolution analysis and learning for computational seismic interpretation

Abstract

Multiresolution analysis and learning for computational seismic interpretation, " The Leading Edge 37.6 (2018): 443-450.

Robust Concurrent Detection of Salt Domes and Faults in Seismic Surveys Using an Improved UNet Architecture

“…Specifically, the Gaussian pyramid, the Discrete Wavelet Transform, Gabor filters, and the Curvelet Transform. In [45], the Curvelet Transform provided the best results, with a detection accuracy = 0.7955, IoU = 0.2656 for faults, and IoU = 0.5261 for salt domes, using only four inline images from the F3 block for validation. Our results are significantly better with IoU = 0.6588 compared to 0.2656 for faults, and IoU = 0.9776 compared to 0.7953 for salt domes using the U-Net-VGG19 pre-trained network.…”

“…As mentioned before, most proposed approaches tackle one particular seismic event using different state-of-art DL models. Only two approaches consider more than one seismic event, the handcrafted-based work in [45] and [46]. Different metrics were reported in [45], standard metrics such as IoU and AUC, but also new ones were introduced, such as pixel accuracy (PA), mean intersection over union (MIU), and frequency-weighted intersection over union (FWIU).…”

“…Only two approaches consider more than one seismic event, the handcrafted-based work in [45] and [46]. Different metrics were reported in [45], standard metrics such as IoU and AUC, but also new ones were introduced, such as pixel accuracy (PA), mean intersection over union (MIU), and frequency-weighted intersection over union (FWIU). In the case of [46], only qualitative evaluation is carried out through visualization of segmentation results on field test data.…”

“…Lastly, we compare our proposed method with the multiresolution approach developed in [45]. Four multiresolution techniques, based on texture attributes, were used to label seismic structures from the Netherland F3 block.…”

Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

“…In a separate study [53], multiresolution attributes in the frequency domain were examined for seismic volume labeling, including the discrete wavelet transform and its nonsubsampled version, Gabor filters, the steerable pyramid, the contourlet transform and its nonsubsampled counterpart, and the curvelet transform. Effective singular values are extracted from each transformed subband and then concatenated into a feature vector.…”