## 1 Introduction  
Subsurface imaging is a crucial aspect of geophysical exploration that involves characterizing the subsurface properties, which frequently contain essential information that can be utilized for hydrocarbon exploration and event detection. Seismic and reservoir monitoring activities offer geophysicists an array of data sources that can be examined to extract essential details regarding the attributes and physical properties. The data furnished by these sources can establish a connection with subsurface physical attributes, either individually or synergistically. Other commonly used methods include well logging for sonic and gamma-ray log for the characterization of reservoirs [1]. However, certain data interpretations encompass intricate and non-linear characteristics. Seismic data provide detailed images of structural features present in the subsurface with high imaging resolution, but their acquisition work is associated with high costs [2]. The information obtained from these data sources can be interpreted quantitatively in terms of subsurface physical properties to provide pivotal information. The engineering sector has developed complex time- and cost-effective methods to infer geophysics measurements from seismic and fiber optic technology. The oil and gas sector has been recognized as one of the major players in adopting DAS technology to replace geophones in VSP, geophysics exploration in applied machine learning (ML), and the monitoring of reservoir integrity and imaging performance [3]. Numerous documented advantages of fiber optic technology surpass those of conventional empirical correlations within the scope of geophysics exploration through DAS [4; 5]. The main reasons for these advantages of DAS include that it is a valuable tool in geophysics exploration, offering improved performance, reliability, and versatility compared to conventional empirical acquisition tools such as geophones [4]. DAS appears as an advanced seismic technology that utilizes fiber optic cable along the measurement length to detect acoustic waves. This makes it useful for continuous sensing, with the ability to perform real-time monitoring. This real-time monitoring of fiber optic cables by DAS helps achieve savings in terms of finances, time, and equipment. An elevated and refined subsurface imaging process necessitates a substantial and sustained level of resolution for further analysis and interpretation. This resilience serves to mitigate potential losses in terms of human resources and investments [6]. Over the past two decades, DAS has been utilized with typical fiber optic cables made of two important parts, namely a silica glass core and silica glass cladding. The two major types of fiber optics used for DAS applications are SMF and MMF [7]. This review focuses on these fiber types. Over the years, SMF has evolved as a type of fiber optic cable in DAS systems for various applications and is commonly used in subsurface seismic monitoring. The most widely used technique for SMF employed in DAS is the detection principle of Rayleigh backscattered light. This technique enables distributed strain sensing, where the fiber optic cable itself acts as the sensing element in DAS. This allows for the detection of acoustic frequency strain signals over large distances [8]. This capability of DAS can be applied to address various applications requiring distributed strain sensing [9]. Some advantages of using SMF is that it reduces signal dispersion and external interference, and SMF with a single light input can reduce light scattering. On the other hand, the main disadvantage of SMF is the presence of tighter tolerances that create coupling light due to the smaller size of the fiber core [3; 10]. According to [PERSON] et al. (2017), the revolution in fiber optic sensing technology has brought about a paradigm shift in distributed multi-parameter acquisition, accompanied by applications that harness its potential [11].