## 1 Introduction  
The last decades have seen a significantly increasing scientific interest in studying the thermomechanical effects on jointed rock masses caused by near-surface temperature fluctuations. The in situ monitoring and quantification of thermally induced damaging processes on jointed rock masses constitutes a frontier nowadays in the field of landslide risk mitigation of natural and anthropized rock slopes. The analysis of thermomechanical effects on jointed rock masses has been widely approached through comparative analyses (statistical and observational-based approaches) of temperature and stress-strain long-term monitoring timeseries. Several studies established a strong correlation between near-surface temperature fluctuations and rock mass deformations [1; 2; 3; 4; 5; 6; 7], demonstrating the existence of inelastic trends even in the absence of transient and violent phenomena such as heavy rainfalls, strong winds, or local seismicity, which are frequently considered to be the primary triggering factors of plastic strain. More recently, passive seismic monitoring techniques have been widely exploited to identify and study thermally driven deformations in rock masses. Ambient seismic noise, microseismic activity, and acoustic emission monitoring techniques showed the greatest potential in providing new insights into rock mass fracturing processes. These techniques proved to be useful in highlighting how daily and annual oscillations of near-surface temperatures can cause evident changes in the stability of jointed rock masses, ultimately impacting the evolution in time of already recognized slope instabilities on various dimensional scales [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The exploitation of high-sensitivity seismic sensors for studying rock mass instabilities has well-constrained the existence of cause-and-effect relationships between growing instability conditions and microseismic activity [8; 15; 18]. Furthermore, they have shown great potential in providing precursory patterns of failure events [19; 20]. In the framework of structural health monitoring of natural and anthropic structures, microseismic (MS) signal analysis nowadays represents one of the most widespread diagnostic tools in several fields of application [12; 17; 21; 22; 23]. MS events are low-energy seismic signals genetically related to the occurrence of plastic deformations within a medium. It is well known that when internal relative slips or the propagation of fractures occur due to the reaching of a strain threshold, the energy stored in a medium is sharply released in the form of elastic waves, leading to infrasonic, sonic, or MS signals [24]. MS monitoring mainly focuses on analyzing signals characterized by a wide range of frequency contents, spanning from \(10^{1}\) to \(10^{3}\) Hz, thus ranking between the traditional earthquake and seismological studies and the acoustic emission domain (Figure 1) [25]. Depending on the characteristics of the site under investigation, MS sensors can be arranged in different array configurations (e.g., varying their location, distance, and density) to surround the monitoring target properly [26]. MS data processing generally starts with the detection and extraction of signals of interest embedded within ambient seismic noise recordings. The recognition of MS waveforms related to fracturing processes can strongly influence the size and accuracy of collected datasets to be analyzed. Numerous techniques exist for recognizing MS events and are based on the analysis of frequency- and time-domain features [27; 28]. After MS events are identified, they can be analyzed, classified, and localized to investigate source parameters and failure mechanisms [26]. MS event classification is a critical step in data processing as it guarantees that no suspicious or artificial waveforms are contained in a dataset, also enabling the recognition of different MS event classes [29].