Automatic extraction of outcrop cavity based on a multiscale regional convolution neural network

Determination of pore space characteristics is important for carbonate reservoir interpretation and evaluation. Field outcrops can reflect the geology of the subsurface reservoir. The most common method of traditional outcrop research is field investigation, which is likely to cause human errors. Digital image recognition of geological outcrop areas can reduce the problem of inaccurate descriptions of geological structures caused by human factors. Automatic extraction based on convolutional neural networks can greatly reduce the amount of such work. By enhancing the deep-learning model of a convolution neural network, the mask region-convolutional neural network (Mask R-CNN) is proposed. This method adapts to the multiscale features of the cavity by manipulating the scales of the input image. To verify the applicability of the model, the method is applied to automatic cavity identification in the digital outcrop profile of the Dengying Formation (2nd Member) in Xianfeng, Ebian. The parameters are calculated layer by layer, and their distribution characteristics are quantitatively analysed, indicating good application results. To verify the advanced nature of the model, the cavity extraction results of this method are compared with those of traditional image segmentation, machine learning and other depth learning methods. Precision analysis shows that the performance of the method proposed in this paper is superior to the traditional methods. In addition, the cavity feature parameters extracted by this method are compared with the manual extraction. The accuracy for the cavity number, surface porosity, and average cavity area is over 80%, 88%, and 72%, respectively for the proposed method. The results show that the proposed method is reliable and accurate, hence it will help to provide a basis for reservoir prediction in the process of regional geological exploration.