## 1 Introduction  
Sandstone has connected pores and good hydrophilicity. Moisture can affect the pore distribution and permeability of sandstone, in turn influencing the storage and percolation capacity of the reservoir [1]. The distribution and production of energy resources, such as oil and natural gas, will change consequently. Moreover, structural stability is crucial in resource mining engineering with high stress and osmotic pressure, such as in petroleum exploitation and natural gas excavation. Moisture can change the mechanical properties of rock, and then promote the growth of rock cracks under loading. Collapse accidents may happen due to unqualified rock moisture content. Therefore, moisture identification in low-permeability reservoir rocks is of significance. The stress distribution is uneven under loading because of the differences in the crystal microstructure and the inhomogeneous distribution of pores in rock mass [2; 3]. Acoustic emission (AE) occurs when a high-energy state produced in a stress concentration region releases energy in the form of a transient elastic wave to achieve a steady state [4; 5]. The excitation mode of AE is diverse. Besides the solid medium, AE can also be generated through liquid medium, and its application in detection engineering is extensive. For example, AE can be generated by cavitation phenomena and turbulent flows associated with fluid leaks [6]. Indeed, even if some drawbacks with respect to other methods may be identified, AE analysis has been successfully adopted for leak detection in pipelines [7; 8]. In the field of rock engineering, AE stems from the particle slip and crack propagation in rock, and the frequency bands are mostly between 20-200 K, which are imperceptible to humans [9]. The signal feature parameters can be extracted through signal acquisition and processing technology; thus, the relation between the signal and rock damage state can be analyzed, and this method is called parametric analysis [10; 11; 12; 13]. This method has been used successfully by many scholars to conduct structural health monitoring. Static tensile loading was conducted on aluminum plates in aerospace systems by [PERSON] et al., and the identification of the damaged part was realized with a combination of artificial neural networks [14]. [PERSON] et al. [15] realized the valve abnormalities detected in a reciprocating compressor by means of AE technology and wave packet transform. [PERSON] et al. [16] completed estimations of the damage location in liquid-filled tanks with the methods of AE technology and probabilistic algorithms. [PERSON] et al. conducted various test configurations on carbon fiber-reinforced plastics including tension, compression, and compact tension (CT) to collect the AE data [17]; the signals were analyzed by a machine learning method, and the frequency domain rule of this material in damage was obtained. There are dozens of kinds of feature parameters in a sandstone AE signal, and each parameter can reflect some aspects of the signal characteristics [18]. On the one hand, the types of parameter that are needed are diverse due to the difference of reservoir material under research. On the other hand, the change rules of parameters are various because of differences in the geological environment, or there are no quantifiable change rules. Too many feature parameters not only hinder classification, but also complicate the exploration of mineral resources and monitoring of reservoir structures. Thus, it is important to conduct a reasonable selection of parameters and minimize redundancy while also ensuring differences among parameter types. Scholars from different countries have achieved several positive results in signal feature selection. For example, [PERSON] et al. [19] proposed a fault AE signal feature selection method based on a Fast Fourier Transform (FFT) for high-speed centrifugal equipment and verified the feasibility by experiments.