## 4 Application of Fiber Optic Cables in Geophysics Exploration
### ML Application in SMF Processing  
Efficient automated processing schemes for raw DAS data have achieved significant importance. SMF cables have the capacity to consistently monitor acoustic signals and vibrations across extensive distances, offering high sensitivity and an elevated update rate [68; 69]. DAS SMF measurement conventionally captures numerous complex backscatter profiles in a short time and performs real-time data processing. Given the substantial volume of generated data, it is essential to devise automatic, efficient, and precise tools for signal processing. As optical data are complex, have a high bitrate, and must be processed in real time, state-of-the-art machine learning techniques are ideal for extracting relevant information [70]. Furthermore, ML can potentially decrease the amount of storage space needed for data and minimize the time required for data processing to facilitate comprehensive analysis [71]. Figure 6 shows general ML processing applied to DAS data and its output. Recent research reported in [72] applied classical ML and deep learning algorithms to SMF DAS event detection. ML was compared with the deep learning result, and the benefits and limitations of both methods were analyzed for each applied algorithm. The classical ML workflow involved DAS data collection, preprocessing, feature extraction and classification, model creation, tracking, and evaluation of event detection. DAS data were acquired using a simple sensor unit with standard SMF and connected to an interferometer. SMF cable  
Figure 6: General ML processing workflow on DAS data. was deployed in a 17 km cable length, with a gauge length of 10 m and a phase-sensitive OTDR interrogator unit. The DAS data were collected in a suburban area near Vienna, Austria. The used ML algorithms were random forest, decision tree, and support vector machine (SVM), while the deep learning algorithms were deep neural networks (DNNs) and convolutional neural networks (CNNs). Deep learning techniques were employed for SMF event detection, utilizing the ability to automatically extract relevant attributes from the raw DAS data. This approach obviates the necessity for extensive domain knowledge. The implementation of this method offers an achievable approach for optimum sensor fusion in SMF sensors in DAS systems. A comparative analysis of outcomes between ML and deep learning methodologies demonstrated that DAS could detect various continuous events along the pipeline, such as human digging and tapping. Moreover, it can identify significant events in other SMF systems. Despite the numerous benefits, DAS data can still be contaminated by various types of noise from the ambient environment and the optical instruments. Supervised ML techniques were applied to target specific noise types to improve the SNR and denoising (noise reduction) of DAS data [73]. Recent research implemented supervised ML method DAS-N2N (Noise2 Noise) to denoise DAS data without clean data [74]. The DAS data were acquired in SMF cable laid out in a 1 km cable length, with sampling at 1000 Hz, channel spacing of 1 m, and a 10 m gauge length. SMF cables placed within the cable jacket were spliced at one end to build a looping cable. The results obtained from DAS-N2N were contrasted with the outcomes of conventional bandpass filtering. In this comparison, DAS-N2N demonstrated its ability to attenuate noise within a frequency range identical to that of the target signal, a task that frequency-based filtering cannot achieve. Furthermore, DAS-N2N exhibited a processing speed approximately ten times faster than self-supervised learning methods. The result indicated that DAS data are remarkably lightweight and efficient and more capable of data processing compared to the acquisition time when fine-tuned with a single GPU. DAS-N2N outperforms conventional frequency bandwidth filtering routines enhanced using compiled low-level computation regarding processing speed.