Nmr relaxation time inversion method based on unsupervised neural network

Abstract

An NMR relaxation time inversion method based on an unsupervised neural network includes simulating inversion kernel matrix, simulating continuous NMR relaxation time spectrum, simulating noise, calculating NMR relaxation signals as samples, various samples forming a sample set, constructing an unsupervised neural network model, and defining a loss function of the unsupervised neural network model; and taking the samples in the training sample set as an input of the unsupervised neural network model, to obtain an optimal mapping relationship between the NMR relaxation signals and the NMR relaxation time spectrum with a minimum loss function. The present invention provides the possibility of using experimental data as the sample for training since the trading sample does not need to be labeled, can automatically learn the optimal regularization parameters without depending on the initial value and manual experience, and predicts fast.