## 1 Introduction  
Rock strength is an important parameter for analyzing geological problems. In the field, geological engineers are always concerned with how to estimate the strength of rocks rapidly, accurately, and conveniently. There are many methods for estimating rock strength. The most widely used is the unconfined compressive strength (UCS) test [1]. However, this method damages rock mass, and is time-consuming and laborious in many cases. Moreover, it is sometimes impossible to damage the rocks to determine their strength, especially in the field. For these reasons, many non-destructive methods have been presented to predict the UCS indirectly, such as the rebound method and sonic technique. The rebound method establishes the relationship between rock strength and rebound value produced by rebound hammers. However, variations in operational procedures may lead to large deviations, and there are too many equations used in this method, making it difficult to select a suitable one. The sonic technique method establishes the relationship between rock strength and sonic information. However, there are operational challenges in using sonic instruments, which are often costly and impractical for use in the field.  
Due to the lack of facile methods for determining rock strength during geology surveys, experienced geologists usually prefer to determine rock strength from hearing the sounds produced by hammering a rock with a geological hammer. The rationale is that the hammering sound can reflect the surface strength (the outer surface at a depth of 30-50 mm) of the rock to some extent [2], which is directly related to its UCS. This method is convenient but subjective. The estimation heavily depends on the experience of geological engineers and is easily affected by many factors, such as noise, dust, and even the health condition of the geological engineer.  
In recent years, deep learning, as one of the emerging fields of machine learning, has gained increasing interest in various fields. More and more researchers find that many complex problems can be solved well based on deep neural networks (DNNs). Generally, the two most active application fields of deep learning are speech recognition and image processing [3,4]. Transfer learning is also an active field of machine learning and has gained more attention because it can help build effective deep learning networks without vast amounts of data [5,6].  
In this research, we aimed to develop a rapid, accurate, and convenient method for measuring the surface strength of rocks in the field. Inspired by the success of deep learning and the practice of estimating rock strength from hammering sounds, a retrained DNN was developed as the core of the proposed method. In the following of this paper, a literature review is firstly presented, and the development of non-destructive methods of rock strength and the usage of DNN in geology are introduced. Then the methodology of the research is described in detail, including the overall process, the DNN technique, the short-time Fourier transformation, the data filtering algorithm, and the regression algorithms. Finally, an experiment is carried out to test the proposed method.