Through the analysis of core descriptions, well‐logs, seismic data, geochemical data and structural settings of the volcanic rock of the Yingcheng Formation in the Xujiaweizi fault depression, Songliao Basin, and the geological section of the Yingcheng Formation in the southeast uplift area, this work determined the existence of volcanic weathering crust exists in the study area. The identification marks on the volcanic weathering crust can be recognized on the scale of core, logging, seismic, geochemistry, etc. In the study area, the structure of this crust is divided into clay layer, leached zone, fracture zone and host rocks, which are 5–118 m thick (averaging 27.5 m). The lithology of the weathering crust includes basalt, andesite, rhyolite and volcanic breccia, and the lithofacies are igneous effusive and extrusive facies. The volcanic weathering crusts are clustered together in the Dashen zone and the middle of the Xuzhong zone, whereas in the Shengshen zone and other parts of the Xuzhong zone, they have a relatively scattered distribution. It is a major volcanic reservoir bed, which covers an area of 2104.16 km2. According to the geotectonic setting of the Songliao Basin, the formation process of the weathering crust is complete. Combining the macroscopic and microscopic features of the weathering crust of the Yingcheng Formation in Xujiaweizi with the logging and three‐dimensional seismic sections, we established a developmental model of the paleo uplift and a developmental model of the slope belt that coexists with the sag on the Xujiaweizi volcanic weathering crust. In addition, the relationship between the volcanic weathering crust and the formation and distribution of the oil/gas reservoir is discussed.
According to the well logging, drilling, and 3D seismic data, we identified the volcanic rock automatically by computer. The recognition system was an image recognition technology based on deep learning algorithm based on seismic data. During the identification process, the 3d seismic data are disassembled into 2d seismic profile by seismic interpretation technique in Dehui Fault Depression of Songliao Basin. And then, we think it was a two-dimensional picture as learning samples, classification of seismic data, establishment of labels, forming a training set. The training set is deeply learned to generate automatic recognition model. Finally, according to the generated automatic identification model, the volcanic rock is realized automatically based on seismic data.
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