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Fault Detection From a Large Perspective With Transformers
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AI
DOI：10.1109/TGRS.2024.3470536.png)
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Abstract
En 中文
Fault detection is one of the core tasks in fault interpretation, which is of great significance to the study of underground oil and gas transport and reservoir distribution. In recent years, with the development of artificial intelligence (AI), especially artificial neural network technology, many new intelligent fault detection methods have emerged. They often have fixed input sizes. When detecting faults in field seismic data, it is necessary to split the data into patches that fit the input size and then combine the detection results. Thus, the information used for fault detection is derived from a single data patch at most, without considering the relations between patches. We propose a Transformer-based network that considers relations between patches and incorporates a larger range of information for 3-D fault detection. Our network employs an encoder-decoder architecture. We construct the encoder using Transformers, which are more effective at extracting global features compared to convolutional neural networks. Moreover, we propose a feature fusion block based on Transformers, which is able to introduce the relations between adjacent data patches, allowing our network to utilize information beyond a single patch and extend to multiple patches. Compared to some existing studies, our network can cover a larger perspective. We apply our method on several datasets, including synthetic and field data, and our method performs well and shows improvements in noise resistance, fault continuity, and the ability to handle complex fault situations.
Keywords:
Artificial intelligence (AI)
fault
fault detection
seismic interpretation
transformer
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W