The signal-to-noise ratio (SNR) is a crucial performance metric in Brillouin distributed optical fiber sensors. Ensuring accurate noise characterization is essential for effective targeted denoising. However, collecting real noise data poses practical challenges. Gaussian noise, traditionally used in supervised methods, is somewhat effective but lacks accuracy. In this paper, we propose to utilize a self-consistent generative adversarial network (SCGAN) to model real noise distribution using collected Brillouin gain spectrum (BGS) data. This enables us to generate noise data for training denoising convolutional neural networks (CNNs). By training the SCGAN to replicate real noise intricacies, we can effectively train a CNN to discern between signal and noise, resulting in more precise noise reduction. By addressing the limitations of conventional Gaussian noise models, our method bridges the gap between artificial noise simulations and complex real-world BOTDA system noise patterns. This innovative approach has the potential to significantly enhance noise reduction techniques for BOTDA systems, improving accuracy and efficiency.

While generative adversarial networks (GANs) have showcased their effectiveness in modeling intricate noise distributions from extensive datasets, they harbor a notable training limitation. GANs optimize their generator networks by minimizing dissimilarities between generated and real samples. Unfortunately, this process might inadvertently prioritize prevalent training data patterns, sidelining other potential variations. To transcend this limitation, this paper introduces a SCGAN as a solution for noise modeling. Going beyond conventional GANs, SCGAN introduces a novel approach. It supplements the adversarial loss with three additional loss functions, effectively offering more guidance and constraints during network training. This augmentation facilitates a more holistic approach to noise modeling by steering the network towards a broader representation of noise patterns. To substantiate the differentiation between Gaussian noise and SCGAN-generated noise, we employ histogram statistics and amplitude spectrum analysis. Subsequently, both types of noise are harnessed to train three state-of-the-art denoising CNNs. The performances of networks are then compared across experimental BGS encompassing varying temperatures and SNRs. This approach reflects a holistic exploration, encompassing both noise modeling and denoising neural network evaluation.

To enable a thorough comparative analysis between SCGAN-generated noise and Gaussian noise, we employ histogram statistics and the Kolmogorov-Smirnov test for both noise sources. Furthermore, a two-dimensional Fourier transform is executed to acquire the noise amplitude spectrum, with the findings visualized in Figs. 10 and 11. These analyses distinctly display the divergences between Gaussian noise and real noise. To effectively showcase the enhanced SNR brought forth by our method, we assess denoising neural networks trained with distinct noise sources across various temperature settings and averaging times. The outcomes are tabulated in Table 1 and Table 2. Importantly, networks trained using SCGAN-generated noise consistently exhibit elevated SNR values compared with their Gaussian noise-trained counterparts. Following the acquisition of temperature data, we compute the corresponding root mean square error (RMSE) and standard deviation (SD). Figures 7 and 8 provide the comprehensive outcomes achieved by different neural networks trained with varying noise sources under diverse temperature conditions and SNRs. Remarkably, networks trained with SCGAN-generated noise consistently outperform their counterparts, delivering superior denoising outcomes characterized by precision and stability. These results underscore the efficacy of SCGAN-based noise training in achieving remarkable noise reduction, generating highly accurate and dependable measurement outcomes across a spectrum of temperature conditions and averaging times.

We introduce the utilization of SCGAN for modeling real noise data and generating paired noise data tailored for supervised training. The research entails a comparative study involving three supervised denoising neural networks—DnCNN, ADNet, and BRDNet—trained with both Gaussian and SCGAN-generated noise. The outcomes distinctly illustrate the method's efficacy in noise reduction for Brillouin distributed optical fiber sensor data, while preserving intricate details. Notably, networks trained on SCGAN-generated noise exhibit superior proficiency in identifying noise features, leading to enhanced measurement outcomes. This advantage remains consistent even under conditions of low averaging times, suggesting the potential for heightened data acquisition rates. Importantly, this paper pioneers the application of generative adversarial models in the domain of Brillouin distributed optical fiber sensor denoising, presenting a novel frontier. Leveraging the diverse arsenal of generative adversarial data generation methods, the technique introduced here has the potential for broader adoption in the realm of distributed optical fiber sensing. This pioneering approach sets the stage for substantial advancements in the accuracy and efficiency of noise reduction methods, ultimately contributing significantly to practical sensor data acquisition rates.