## 4 Application of Fiber Optic Cables in Geophysics Exploration
### Microsiseismic of MMF  
Fiber optic cables have the capability to be permanently installed by cementing them behind a protective casing or temporarily deployed using a wireline method [77, 78]. Once a fiber optic cable has been deployed in a downhole environment, it can serve various functions, including the monitoring of microseismic activity. Despite the utilization of MMF, DAS can effectively produce estimations and source mechanisms that provide sufficient coverage for obtaining a singular-moment tensor solution in microseismic measurements. Characterization of source parameter estimation using DAS with MMFrecorded microseismic data was conducted in [79]. An MMF cable was deployed within a treatment well located in the Meramec Shale Formation in Oklahoma. This cable spanned the whole vertical distance of the well, extending from the surface down to the desired depth. Figure 7 shows an illustration of microseismic event measurement in hydraulic fracturing by DAS \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Scope of Work** & **MMF Deployment** & **Outcome** \\ \hline [PERSON] et al. [75] & DAS VSP & Deployed within hybrid optical-electric wireline cable & Excellent vertical and lateral imaging resolution and detailed subsurface structure \\ \hline [PERSON] et al. [56] & Real-time DAS VSP & Permanently deployed behind casing in vertical and lateral sections of a well & Reduction in SNR for DAS VSP and MMF had sufficient capacity for the DAS interrogator \\ \hline [PERSON] et al. [76] & 4D reservoir monitoring & Deployed in active deep-water wells & DAS 4D images exhibited qualitative similarity to OBN 4D outcomes and subsurface images, facilitating the assessment of water flood injection to the reservoir \\ \hline [PERSON] et al. [61] & Subsea reservoir monitoring & Attached to subsea infrastructure and deployed in a borehole & The repercussions of increased noise for subsets DAS image quality \\ \hline [PERSON] et al. [62] & Autonomous permanent reservoir monitoring & Cemented behind casing inside a borehole & Evident in time-lapse operations carried out using the DAS SMF system, with favorable data quality attributes and encompassing SNR \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of VSP applications of MMF cable used in relevant studies. fiber optic cables. As a result, almost 1000 channels were recorded through this installation. During the treatment of two wells, comprehensive measurements were conducted to assess DTS, strain, and microseismic activity along the full depth of the wells. DAS on MMF achieved excellent waveform imaging for two microseismic events with S-wave polarity reversals. This is because DAS microseismic events with consistent amplitude patterns were observed using event classification considering polarity reversals and horizontal and vertical MMF distance along the fiber. The resulting variation in nodal planes for DAS microseismic events showed a shear-wave amplitude pattern and even managed to generate a nodal plane angle picked manually by considering the horizontal and vertical axis distances of the microseismic event from the treatment well. The result of using DAS for seismic monitoring indicated that the MMF was more accurate in characterizing source features, specifically in estimating moment tensors. Future endeavors might to improve signal processing techniques and consider anisotropy in subsurface properties and its effects on the propagation of seismic waves detected by DAS systems. ML Application in MMF Processing  
Machine learning methodologies have been extensively employed to enhance the efficiency and precision of data processing [80, 81, 82]. Several ML strategies have been tested in academic literature to increase the rate of processing of DAS microseismic data; another such strategy is the utilization of deep learning [83]. The authors of [84] proposed a new and efficient data processing workflow for MMF DAS microseismic data using an ML-assisted approach.