Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network
During the downhole microseismic monitoring for hydraulic fracturing, microseismic signals are constantly vulnerable to interference from different kinds of noise. Improving the signal-to-noise ratio of microseismic records is always beneficial for processing and interpreting microseismic data. Unli...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10721461/ |
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| Summary: | During the downhole microseismic monitoring for hydraulic fracturing, microseismic signals are constantly vulnerable to interference from different kinds of noise. Improving the signal-to-noise ratio of microseismic records is always beneficial for processing and interpreting microseismic data. Unlike traditional methods that often result in the loss of signal details, an improved attention mechanism is proposed that can effectively enhance feature extraction from microseismic data and accurately recover detailed components in this article. To denoise the noisy three-component microseismic record effectively, we design a denoising network model that combines a convolutional autoencoder with an improved attention mechanism. Using the attention network to assign weights, channels containing noise information are given lower weights and effectively suppressed. Conventional methods and deep learning methods for denoising rarely consider the influence of polarization characteristics. The method proposed in this paper leverages deep learning for denoising while simultaneously reducing the impact of polarization characteristics throughout the denoising process. Simulation experiments are conducted using waveform analysis, time-frequency analysis, first arrival picking, and polarization analysis methods to validate the effectiveness of the model. Comparing the popular bidirectional long and short-term neural network, our model demonstrates superior recovery capabilities under various signal-to-noise ratio conditions. |
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| ISSN: | 2169-3536 |