# Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Core
## 1 Introduction  
A gravity piston sampler is an important geological tool that uses its gravity as the driving force to obtain in situ samples. Since the design proposal of the Kullenberg-type gravity sampler in 1947 [1], it has been widely used for the acquisition of deep-sea ultra-long in situ samples [2, 3], nearshore sediment samples [4], and in situ lake sediment samples [5]. In recent years, due to the research and development of gas hydrate, the acquisition of high-fidelity in situ samples based on a gravity piston sampler [6, 7, 8] has become a hot research topic. Whether for the acquisition of deep-sea, nearshore, or lake sediments, how to improve the sampling efficiency and ensure continuous and undisturbed sediment samples have been the focus of much research. When sampling with a gravity piston sampler, the length of the installed sampling tube may be different from the actual penetration depth, which could lead to accidents, such as breakage of the sample tube when the size of the sample tube is greater than the penetration depth or failure to obtain the maximum length of a continuous in situ sample [9] when the size is less than the penetration depth. Therefore, the prediction of penetration depth according to the geological characteristics of the sampling area before the release of the gravity sampler can help improve the safety and efficiency of the sampler. In previous studies of gravity sampler penetration depth, scholars mainly used force analysis and energy conservation to study analytical solutions. [PERSON] et al. [10] and [PERSON] et al. [9]obtained the analytical solution of the penetration depth equation based on the energy conservation equation through force analysis of the gravity sampler. They verified it with actual offshore sampling data. However, due to the complicated penetration process in the sampling area and problems of recording and operating errors in the real measured data at sea, the measured penetration data were not accurate, and the amount of data was small. To study the penetration process and factors of gravity samplers more accurately and controllably, [PERSON] [11] designed a gravity sampling physical test model in 2014 to address the problem of the small amount of data and lack of accurate data recording of the penetration process, conducted dozens of sets of tests, obtained a large amount of accurately recorded data, and proposed a new analytical solution model based on the tests. In recent years, scholars have also tried to discuss more influencing factors of gravity sampling penetration depth, such as friction coefficient and sediment characteristics [12; 13]. However, the basic idea of past research was still to use the energy balance of penetration work and friction consumption work to solve the equation to determine the penetration depth, and there has been no substantial breakthrough in the modeling tools but only reduces the error value by parameters with less influencing factors. The complexity of the sampler penetration process, inaccuracies and other factors, and errors caused by human operation remain challenges to be eliminated. Therefore, there is an urgent need to propose a new means to model and study the penetration depth of gravity samplers. Machine learning, the best-known class of artificial intelligence algorithms, is capable of efficiently approaching a wide range of data problems. Machine learning is divided into two main categories: supervised machine learning and unsupervised machine learning [14]. Supervised machine learning is used for training data with outcome labels, and unsupervised machine learning filters feature data, clustering without outcome labels. Supervised machine learning is further divided into two types of problems, classification and regression, depending on the result labels. Classification applies to data with a definite outcome, and regression applies to data with an uncertain type of outcome.