## 1 Introduction
This task is generally accomplished through manual or automatic analyses of several parameters, such as envelope shape, event duration, frequency, and energy content of signals. On this topic, several authors have proposed the semi-automatic and automatic classification of MS events through visual screening and supervised or unsupervised machine learning techniques [21; 29; 30; 31; 32]. The recent development of high-sensitivity sensors capable of detecting very low amplitude seismic signals paved the way for innovative applications of MS monitoring in civil engineering [33; 34; 35; 36], mining and quarrying activities [37; 38; 39], tunneling excavation [40], and slope stability [8; 13; 19; 41; 42; 43; 44]. Concerning the application of MS monitoring techniques for rock slope stability, several case studies are present in the literature where high-sensitivity seismic networks were deployed to test their performance in deciphering the evolution of slope instabilities (i.e., both in soil and rock) [18; 19; 43]. When dealing with the study of  
Figure 1: Seismic motion wave frequency spectrum and field of application of MS monitoring. microseismicity in jointed rock masses, the analysis and characterization of temporal rates, peak ground acceleration, energy, and frequency content, are among the most employed strategies to study the evolution of rock mass failures. Correlations between microseismicity and environmental factors (e.g., rainfalls, snowfalls, air temperature, and wind) have been studied by several authors since seismic monitoring networks have proven to be valuable tools for the identification of fracturing processes in jointed rock masses. Most of these studies are focused on high- mountainous regions, where thermal regimes are characterized by continuous fluctuations in temperature around freezing conditions [15; 19; 44]. The continuous action of thermal cycles can be regarded as a \"preparatory\" factor of progressive failure in rock masses. In this framework, the investigation of rock mass damaging (RMD) requires the implementation of monitoring systems capable of dealing with the timescale of failure mechanisms and the dimensional scale of rock mass instabilities. Therefore, it is good practice to collect multiparametric datasets to consider all possible factors contributing to reversible or irreversible changes in mechanical and dynamic parameters over different timescales. To better constrain the role of temperature fluctuations in driving reversible and irreversible deformations of jointed rock masses, we present the results obtained from the analysis of a 20 month long MS monitoring dataset collected at the Acuto field laboratory [45]. The test-site is located within an abandoned quarry in Central Italy, and it is managed by the Department of Earth Sciences and the CERI--Research Center for Geological Risks of Sapienza University of Rome. It hosts an integrated geotechnical and geophysical monitoring system installed on a 20 m\({}^{3}\) prone-to-fall rock block. The here presented experimental activities were designed to investigate the role of near-surface temperature fluctuations in causing the progressive growth and propagation of cracks in prone-to-fall rock masses. For this purpose, we analyzed the continuous ambient vibrations collected over almost two years of monitoring campaigns. At first, a semi-automatic approach was implemented to identify the most intense events and perform a preliminary classification of the recorded waveforms. This task was also designed to exclude false events from further consideration. Hence, the monitoring dataset was analyzed by comparing the evolution and characteristics of the local microseismicity with environmental factors to shed light on potential correlations that could reveal precursory patterns for rock mass instabilities.