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Improved Seismic Residual Diffracted Multiple Suppression Method Based on Object Detection and Image Segmentation
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DOI：10.1109/TGRS.2023.3234568.png)
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Abstract
En 中文
Seismic multiple is one of the most common noises in marine seismic data, which heavily affects subsequent processing and interpretation. To eliminate the influence of seismic multiples, many methods have been developed, while surface-related multiple elimination (SRME) is one of the most widely deployed methods. However, results of SRME always contain a few strong residual diffracted multiples (RDMs) in practice because of the unprecise prediction of diffracted multiples compared to reflection multiples. If we try to apply further multiple suppression methods to SRME results, it not only tends to damage the signals, but also spends lots of unnecessary computations where there is no RDM. In this article, we propose an improved RDM suppression method based on object detection and image segmentation. First, we employ an object detection network to locate bounding boxes containing RDMs in the SRME results. Then a threshold-based image segmentation method is utilized to identify regions of strong RDMs in the detected boxes. According to the segmentation results, parameters for weak multiples and strong multiples are provided for the adaptive multiple subtraction (AMS) in different regions to generate different results. At last, we combine the suppression results of strong RDMs and weak RDMs as the final results. Application on field data demonstrates that our method is able to suppress RDMs with little loss of signal.
Keywords:
Image segmentation
Object detection
Reflection
Dictionaries
Training
Surface waves
Surface cracks
Adaptive multiple suppression
dictionary learning (DL)
image segmentation
object detection
seismic diffracted multiple
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2.1W
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10.7W
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