The largest Precambrian gasfield in China has been found from the Ediacaran (Sinian) carbonates in the central Sichuan Basin. The deep ancient reservoirs were generally attributed to the high-energy mound-shoal body in the carbonate platform. However, there is still little understanding on the distribution of the mound-shoal bodies that hampers further gas exploitation from the deep subsurface. Based on the seismic data, a large strike-slip fault system was identified by new 3D seismic data in the central Sichuan Basin. Further, it was found that the strike-slip fault had some effects on the mound-shoal bodies of the Ediacaran Dengying Formation. First, the platform margin was divided by strike-slip faults into three distinct segments to show two weak-rimmed margins and one interbedded rimmed margin. Second, the platform margin could be offset or migrated with the strike-slip faults. Third, there is varied margin thickness across the strike-slip fault zone. In the inner platform, more carbonate mound-shoal bodies developed behind the weak-rimmed margin that was divided by the strike-slip fault zones. In addition, the mound-shoal bodies may be separated by faulted sag. Further, the mound-shoal bodies may have developed along the faulted higher position in one side of the strike-slip fault zone. These patterns of the mound-shoal bodies suggest that the strike-slip fault had an important role in the sedimentary microfacies’ diversity in the intracratonic carbonates. The low displacement of the strike-slip fault is chiefly responsible for a weaker controlling effect on the microfacies change in the intracratonic basin rather than other tectonic settings.

Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NSCT) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NSCT has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NSCT coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components.

The resistivity imaging system is specifically designed for the precise measurement of resistivity distributions within drilled columnar core samples. Its coaxial symmetric configuration enables the non-destructive characterization of electrical properties, with broad applications in oil and gas exploration, reservoir evaluation, and geological research. By integrating a ring return electrode and full-circumference electrode arrays, the system can acquire core-scale resistivity data in conductive media environments. The self-developed imaging software employs advanced processing algorithms—including depth correction, amplitude normalization, and image enhancement—to transform raw resistivity measurements into high-resolution surface imaging maps. Experimental results demonstrate that the system can resolve features such as cracks with a minimum width of 0.5 mm and pores with a minimum inner diameter of 0.4 mm in granite core, providing a novel technical approach for the fine-scale characterization of core materials.