A multi-task learning method for relative geologic time, horizons, and faults with prior information and transformer

• Published in: IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
• Main Authors: Yang, Jiarun, Wu, Xinming, Bi, Zhengfa, Geng, Zhicheng
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Boundary control; Computer applications; Detection; Estimation; fault; Fault detection; Fault lines; Feature extraction; Feature maps; Geological faults; Geological mapping; Geological structures; Geology; Horizon; Learning; Methods; multi-task learning; Neural networks; Receptive field; Relative Geologic Time; Task analysis; Three-dimensional displays; Training; transformer; Transformers
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3264593

Horizon extraction and fault detection are essential in seismic interpretation and closely related to each other. Most existing methods tend to deal with these two tasks independently, and may not work well in interpreting seismic images with complex geologic structures. We propose a multi-task learning (MTL) network with two branches to extract all horizons and detect faults simultaneously by estimating a relative geologic time (RGT) map as well as computing a fault map. These two branches share training datasets, feature maps, and network parameters during the training. The RGT estimation branch, constructed with a transformer architecture, is more lightweight compared to previous CNN methods but provides a larger and structure-oriented receptive field to adaptively capture global structural information for estimating a globally optimal RGT map. The fault detection branch is a simple convolutional neural network (CNN) which merges feature maps shared by the transformer and the derivatives of the estimated RGT to compute a fault map. The fault detection branch provides boundary control for the RGT estimation branch while the later provides global constraints for the former to improve its robustness to noise. Note that our RGT estimation by globally fitting all structures in a seismic image is a volumetric horizon interpretation method with which we are able to obtain a whole volume of horizons, all at once, by simply extracting contours of the RGT map. In our method, we further enable convenient human interactions by integrating manually interpreted horizons (or horizon segments) into the network, which imposes expert knowledge on the network to estimate reasonable RGT results from seismic images with complex fault systems, unconformities, and poor data quality. Moreover, when using 3D horizons as constraints, we are able to decompose the computationaly expensive 3D RGT estimation from a seismic volume into independently parallel 2D estimations slice by slice and combine them to obtain a laterally consistent 3D result.