Distributed Acoustic Sensing Noise Attenuation Using CNN

The patent replaces conventional mechanical signal processing methods with machine learning-based noise attenuation. A neural network model is trained to distinguish between seismic signals and noise, using deep learning to achieve more accurate noise removal without distorting seismic events. This substitution of processing methodology enables effective noise attenuation while preserving signal integrity.

The patent changes the approach from fixed conventional filtering parameters to adaptive machine learning models. The neural network dynamically adjusts its noise attenuation characteristics based on training data, allowing it to adapt to different seismic conditions and avoid misidentifying signals as noise, thereby maintaining signal integrity during attenuation.

The patent substitutes traditional mechanical filtering with neural network-based processing that learns to distinguish between noise and seismic events. This allows aggressive noise removal while maintaining detection accuracy, as the neural network understands the temporal and spectral characteristics of both noise and signals, preventing distortion of seismic events during attenuation.

The patent employs feedback mechanisms where the neural network continuously refines its noise attenuation based on training data and performance evaluation. This feedback loop ensures that noise is effectively removed while preserving seismic event characteristics, as the model adjusts its attenuation strength based on the presence and quality of detected seismic signals.