A Velocity Spectrum Picking Method Based on Detection Fine Tuning Depth Recognition Technology

Ma, Haoyang

doi:10.1109/ccet52649.2021.9544220

Cited by 1 publication

(4 citation statements)

References 13 publications

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“…Zhang & Lu, 2016). In addition, hybrid methods, combining both the information of velocity spectrum and other information, for example, prestack gather and stacked gather slice, were introduced to pick the stack velocity (Ferreira et al, 2020;H. Ma, 2021;H.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the above classification or regression methods, H. Zhang et al (2019) applied YOLO to detect the velocity points and utilized long short-term memory to correct the picked points. Later on, H. Ma (2021) proposed a new network for object detection to improve picking accuracy. Additionally, H. Wang et al (2022) proposed a fusion method of the spectrum and other information based on the semantic segmentation method to resolve auto-picking in the case of low signal-to-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

“…Also, some researchers picked the stack velocity directly based on the prestack gather (Biswas et al., 2019; Carlos et al., 1998; Y. Ma et al., 2018; P. Zhang & Lu, 2016). In addition, hybrid methods, combining both the information of velocity spectrum and other information, for example, prestack gather and stacked gather slice, were introduced to pick the stack velocity (Ferreira et al., 2020; H. Ma, 2021; H. Wang et al., 2022). In the current paper, we will focus on discussing the automatic picking method based on the semblance velocity spectra.…”

Section: Introductionmentioning

confidence: 99%

“…(2019) applied YOLO to detect the velocity points and utilized long short‐term memory to correct the picked points. Later on, H. Ma (2021) proposed a new network for object detection to improve picking accuracy. Additionally, H. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic stack velocity picking using a semi‐supervised ensemble learning method

Wang,

Zhang,

Zhang

et al. 2024

Geophysical Prospecting

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Picking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

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Section: Introductionmentioning

confidence: 99%