A combined deep learning and morphology approach for DFS identification and parameter extraction

Abstract

Since the concept of the Distributive Fluvial System (DFS) was introduced, understanding DFS river parameters has been vital for oil and gas reservoirs. Traditional measurement methods are often time-consuming and labour-intensive. This paper presents a deep learning and morphology-based method for the automatic extraction of DFS river parameters. We propose an optimized model, Seg_ASPP, which integrates Segformer and ASPP (Atrous Spatial Pyramid Pooling) to generate river network masks. The river centerline is then extracted via accumulation cost and polynomial fitting algorithms, allowing for length, width, and sinuosity calculations. Using the Geermu DFS area in the Qaidam Basin for evaluation, we compare the parameters extracted via our method against manual measurements. The average relative errors for length, width, and curvature are 10.22%, 13.57%, and 5.41%, respectively, demonstrating the strong performance of the model. Our experiments show that the DFS parameter extraction method proposed in this paper has great potential for practical applications.