Method for automatically identifying and segmenting salt body through weak supervised learning

Abstract

The invention discloses a method for automatically identifying and segmenting a salt body through weak supervised learning. The method comprises the steps of: preprocessing a salt body picture by using a Grabcut algorithm; preliminarily segmenting a salt body contour; obtaining a training set, establishing a weak supervision segmentation model by using a convolutional neural network, utilizing a loss function to guide a model to learn features of a salt body, continuously correcting labels in a training set in an iterative mode, wherein the labels refer to salt-containing labels and salt-freelabels, the labels in the training set are more accurate pixel by pixel, and a trained weak supervision segmentation model is obtained and used for segmenting the position contour of the salt body ina to-be-tested picture. According to the method, whether the salt body exists or not and the existing position and contour can be automatically and accurately recognized by a machine, the characteristics of the salt body can be learned under the condition that data set labeling is incomplete and inaccurate, and efficient and accurate segmentation of the salt body is achieved.

Description

technical field [0001] The invention belongs to the technical fields of machine learning, computer image processing and geological modeling, relates to weak supervision and semantic segmentation in deep learning, and is a method for automatically identifying and segmenting salt bodies through weak supervision learning. Background technique [0002] An area of ​​the Earth where large deposits of oil and natural gas are stored, with deposits of deposited salt beneath the surface. But it's not easy to pinpoint exactly where there's deposited salt, and professional seismic imaging still requires expert human judgment on salt deposits, leading to a very subjective and highly variable rendering process. In addition, this creates potential hazards for oil and gas extraction. Therefore, it is particularly important to find an algorithm that can automatically and accurately identify whether there is salt body and the outline of salt body on the surface. [0003] Image semantic segm...

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is: the main problem of supervised semantic segmentation is that the labeling information is incomplete and inaccurate, that is, the precise boundary information of the target is missing

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