Аннотация
Abstract This article presents a detailed analytical review of distributed acoustic sensing (DAS) systems for seismic monitoring, with emphasis on their optical infrastructure, signal processing methodologies, and integration with machine learning (ML) approaches. DAS leverages existing fiber-optic cables by utilizing Rayleigh backscattering to convert them into high-resolution seismic sensing networks. These networks offer spatial resolution down to one meter and can operate effectively over distances up to 250 kilometers in real time. The system’s responsiveness to pressure and environmental fluctuations has been captured through mathematical modeling. To enhance signal quality, seismic data is subjected to advanced processing techniques, including Fourier analysis, wavelet transformation, and adaptive noise filtering, yielding signal-to-noise improvements of up to 15 dB. In terms of data interpretation, machine learning models such as support vector machines (SVM), long short-term memory networks (LSTM), and gradient boosting classifiers have achieved high performance, often surpassing 90% accuracy in seismic event detection. Furthermore, scalable insights are supported through unsupervised and semi-supervised learning strategies. To address challenges related to model transparency, explainability tools like SHAP and LIME are applied to aid in the interpretation of predictive outputs. Field deployments of DAS systems, combined with intelligent analytics, demonstrate significant promise for large-scale seismic detection across both terrestrial and marine environments. This article presents a detailed analytical review of distributed acoustic sensing (DAS) systems for seismic monitoring, with emphasis on their optical infrastructure, signal processing methodologies, and integration with machine learning (ML) approaches. DAS leverages existing fiber-optic cables by utilizing Rayleigh backscattering to convert them into high-resolution seismic sensing networks. These networks offer spatial resolution down to one meter and can operate effectively over distances up to 250 kilometers in real time. The system’s responsiveness to pressure and environmental fluctuations has been captured through mathematical modeling. To enhance signal quality, seismic data is subjected to advanced processing techniques, including Fourier analysis, wavelet transformation, and adaptive noise filtering, yielding signal-to-noise improvements of up to 15 dB. In terms of data interpretation, machine learning models such as support vector machines (SVM), long short-term memory networks (LSTM), and gradient boosting classifiers have achieved high performance, often surpassing 90% accuracy in seismic event detection. Furthermore, scalable insights are supported through unsupervised and semi-supervised learning strategies. To address challenges related to model transparency, explainability tools like SHAP and LIME are applied to aid in the interpretation of predictive outputs. Field deployments of DAS systems, combined with intelligent analytics, demonstrate significant promise for large-scale seismic detection across both terrestrial and marine environments. This article presents a detailed analytical review of distributed acoustic sensing (DAS) systems for seismic monitoring, with emphasis on their optical infrastructure, signal processing methodologies, and integration with machine learning (ML) approaches. DAS leverages existing fiber-optic cables by utilizing Rayleigh backscattering to convert them into high-resolution seismic sensing networks. These networks offer spatial resolution down to one meter and can operate effectively over distances up to 250 kilometers in real time. The system’s responsiveness to pressure and environmental fluctuations has been captured through mathematical modeling. To enhance signal quality, seismic data is subjected to advanced processing techniques, including Fourier analysis, wavelet transformation, and adaptive noise filtering, yielding signal-to-noise improvements of up to 15 dB. In terms of data interpretation, machine learning models such as support vector machines (SVM), long short-term memory networks (LSTM), and gradient boosting classifiers have achieved high performance, often surpassing 90% accuracy in seismic event detection. Furthermore, scalable insights are supported through unsupervised and semi-supervised learning strategies. To address challenges related to model transparency, explainability tools like SHAP and LIME are applied to aid in the interpretation of predictive outputs. Field deployments of DAS systems, combined with intelligent analytics, demonstrate significant promise for large-scale seismic detection across both terrestrial and marine environments.