Multitask Learning for Pre-Stack Three-Parameter Inversion

Meng, Jinyu; Wang, S.; Geng, Weiheng; Wang, Pu; Yang, Lei; Sang, Wenjing

doi:10.3997/2214-4609.202210237

Cited by 1 publication

(2 citation statements)

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“…Many researchers have applied MTL to solve practical geophysical problems such as seismic image processing (Wu, Liang, Shi, Geng, et al, 2019), P/S wave separation and reverse time migration for vertical seismic profiling (VSP) (Y. Wei et al, 2020), super-resolution of seismic velocity model (Li et al, 2020), seismic structural curvature volume extraction (Ao et al, 2020) and seismic inversion (Meng et al, 2022;Zheng et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, training data are insufficient to train an accurate network, and the shared training data and parameters of MTL play a role in data augmentation (Y. Zhang & Yang, 2021). Many researchers have applied MTL to solve practical geophysical problems such as seismic image processing (Wu, Liang, Shi, Geng, et al., 2019), P/S wave separation and reverse time migration for vertical seismic profiling (VSP) (Y. Wei et al., 2020), super‐resolution of seismic velocity model (Li et al., 2020), seismic structural curvature volume extraction (Ao et al., 2020) and seismic inversion (Meng et al., 2022; Wang, Wang, et al., 2021; Zheng et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

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Deep carbonate fault–karst reservoir characterization by multi‐task learning

Zhang,

Li,

Yan

et al. 2023

Geophysical Prospecting

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The carbonate fault–karst reservoir is a special and significant reservoir in the Shunbei area, and the development of the cave has been controlled by strike‐slip faults. Due to the complex subsurface structures, fault–karst reservoir characterization is generally divided into fault and cave detection tasks. The potential spatial relationships between faults and caves might be neglected by using the separate detection scheme. The multi‐task learning network can perform multiple tasks simultaneously and exploit the potential features of training data by using a deep neural network. In this study, we built fault–karst models based on the geological background of the Shunbei area and synthesized fault–karst training data using three‐dimensional point spread function convolution. Then, we developed a multi‐task learning network to learn fault–karst features and detect faults and caves simultaneously. The test result demonstrates that the multi‐task learning network trained by synthetic fault–karst data can effectively identify the faults and caves in field seismic data. The comparisons of the multi‐task learning network, single‐task learning networks and conventional methods demonstrate the importance of spatial relationships between faults and caves and show the superiority of the multi‐task learning network. This technique could significantly assist in the exploration, development and well deployment for an ultra‐deep carbonate reservoir.

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