{'fraction_non_alphanumeric': 0.01952807160292921, 'fraction_numerical': 0.0032546786004882017, 'mean_word_length': 5.162907268170426, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Interpreter aided salt boundary segmentation using SVM regression shape deformation technique

Problem Introduction For the seismic imaging project in offshore oil and gas exploration, a good velocity model is a must in order to achieve the best seismic image quality. Accurate salt body geometry extraction is a key component in the velocity model building workflow. However, salt-body boundary picking is a very human-intensive procedure even with the help of currently available automatic picking tools. It is very unlikely that there can be a super algorithm that can fully automates the boundary extraction procedure without sacrificing the result quality. Therefore this report addresses the problem of automatically and accurately segmenting the salt body in a series of neighboring seismic slices given only the accurate salt segmentation result on one single reference slice. This is achieved with a landmark based shape deformation technique plus SVM style regression. In this way, we can achieve the optimum trade-off between the amount of manpower and the quality of segmentation result. Due to the large noise in seismic data, simple image processing filter plus local boundary tracker algorithm gives very poor result or fails easily; very often some parts of the salt boundary are just missing due to poor illumination or miscellaneous source of noise (see Fig 1). Human interpreter's inputs at these trouble regions are essential (Halpert, 2011). However as today's seismic imaging practices evolve to 3-dimensions (think that the 2-D images now becomes 3-D volume), it would be too time-consuming for the human to manually take care every single slice. Therefore the idea of manually segmenting only a small amount of “key” slices and then intelligently propagating these results to the entire volume comes into play. Here I use a land-mark based shapedeformation technique to propagate a single slice of manual segmentation result into its neighboring slices, thus yielding much better segmentation result than simply applying automatic methods. The design goal for such intelligent boundary propagation consists of two parts: • First, I ask the new boundary to honor the available boundary information that can be extracted from the image. • Secondly, the new boundary should preserve the shape information known from the manual segmentation input, such that the boundary will deform reasonably where we don’t have well-defined boundaries.