Abstract: The present disclosure provides a training method and a prediction method for a diagenetic parameter prediction model based on an artificial intelligence algorithm, which includes: obtaining a plurality of diagenesis samples each including diagenetic condition parameters and an actual diagenetic parameter evolved therefrom; constructing an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters; and training the initial diagenetic parameter prediction model with the diagenesis samples so as to obtain a trained diagenetic parameter prediction model. The present disclosure can obtain a diagenetic parameter prediction model by training with the existing diagenesis samples, thereby solving problems of large amount of calculation, high uncertainty and large deviations in the prediction of the diagenetic parameters, which leads to a low evaluation accuracy of reservoirs and limits the oil and gas exploration.

Abstract: An electrode includes a microporous titanium substrate coated with a catalytic layer, and the catalytic layer includes magnetic SnO2—Sb particles. The magnetic SnO2—Sb particles are attached to the microporous titanium substrate through an external magnetic field. The microporous titanium substrate includes a plurality of membrane pores having a pore size of 5-50 ?m that is smaller than a particle size of the magnetic SnO2—Sb particles.