Abstract:
The Unsupervised Learning project focuses on the binary classification of seismic signals using datasets containing P-wave, S-wave, and noise. The project incorporates advanced techniques such as multi-head attentions, BiLSTM layers, and convolutions to effectively classify seismic signals. Additionally, filtering and segmentation methods are employed to enhance the interpretability of seismic data, aiding geological researchers in their analysis.
The primary objective of the project is to develop a robust classification model capable of accurately distinguishing between different types of seismic signals. By leveraging state-of-the-art machine learning algorithms and signal processing techniques, the project aims to improve the efficiency and accuracy of seismic data analysis.
Key features of the project include:
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Utilization of Multi-Head Attentions: Multi-head attention mechanisms are employed to capture complex relationships and dependencies within the seismic signals, enhancing the classification performance of the model.
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Integration of BiLSTM Layers: Bidirectional Long Short-Term Memory (BiLSTM) layers are incorporated to effectively capture temporal dependencies in the seismic data, allowing the model to learn from both past and future information.
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Implementation of Convolutional Layers: Convolutional layers are used to extract hierarchical features from the input seismic signals, enabling the model to automatically learn discriminative features for classification.
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Application of Filtering and Segmentation: Filtering techniques are applied to preprocess the seismic data, removing noise and artifacts that may interfere with the classification process. Segmentation methods are also employed to partition the seismic signals into smaller, more manageable segments for analysis.
Overall, the Unsupervised Learning project aims to advance the field of seismic data analysis by developing innovative methods for signal classification and interpretation. By providing geological researchers with powerful tools and techniques, the project seeks to facilitate a deeper understanding of seismic phenomena and improve the accuracy of geological interpretations.