06-06-2023 дата публикации
Номер: CN116229141A
Принадлежит:
The invention discloses a shale identification method based on improved Resnet, and the method employs an AdaGrad optimization algorithm for parameter updating, the algorithm can properly adjust the learning rate for each element of a parameter, learning is carried out at the same time, W represents a to-be-updated weight parameter, h is the sum of squares of the loss of the previous parameter with respect to the gradient, is the gradient of a loss function with respect to the weight, and is the sum of squares of the loss of the previous parameter with respect to the weight. The method is very ideal in application in a Lonmaxi shale reservoir section, the improved residual network is very suitable for being applied to image recognition, and the problem that a deep neural network cannot process image data is solved; according to the residual network identification method provided by the invention, the features after image processing are transmitted into the multi-layer perceptron to be output ...
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