Chaotic Reflection Instance Segmentation in Seismic Images: COCO Format Dataset Generation and Chaotic Region Identification Using Detectron2
Abstract
Abstract Geological segmentation is considered a difficult task, specifically on seismic images, due to the complexity of the captured data. Artificial intelligence techniques have opened several unrevealed and restricted research directions in geoscience domain by providing robust deep learning computational models. A notable non-trivial research problem is the detection of complex and chaotic seismic reflection features. Accurate detection of these seismic features is necessary for precise delineation of geological structures and associated regions of interest for exploration study. This research paper presents an empirical work for chaotic reflection segmentation. Instance segmentation is performed for extracting out in-depth information of chaotic seismic sections. Each individual subtask is inherently challenging and requires dedicated attention and a specialized methodological approach, encompassing processes from chaotic-region data preparation to seismic section segmentation. Chaotic segmented data transformation is performed with the support of OpendTect and Label Studio open-source tools. Further, the segmented region dataset is converted to COCO style dataset for instance segmentation and used as input to Detectron2 framework. Several variations in deep learning models are experimented with for each subnetwork of Detectron2. ResNet50, ResNet101, and ResNext101 in backbone network are validated along with Mask-RCNN in Region of Interest (RoI) Heads. Result shows…
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