Abstract:
Seismic facies analysis (SFA) plays a pivotal role in the interpretation of subsurface structures, with a pressing need to develop automated techniques for analyzing 4-D ...Show MoreMetadata
Abstract:
Seismic facies analysis (SFA) plays a pivotal role in the interpretation of subsurface structures, with a pressing need to develop automated techniques for analyzing 4-D prestack seismic data. Various automated SFA methods, encompassing both supervised and unsupervised paradigms, have shown encouraging potential in fulfilling this demand. Nonetheless, supervised methods heavily hinge upon precious labeled seismic datasets of high caliber, and unsupervised methods handle all seismic samples indiscriminately during training, resulting in pronounced biases toward the majority classes of seismic data. As an alternative, this letter proposes class-imbalanced deep embedding clustering (CDEC), an unsupervised deep clustering methodology devised to analyze seismic data with class-imbalanced facies distributions. Within CDEC, a focal loss meticulously tailored to address class imbalance challenges is seamlessly integrated into the classic deep convolutional embedding clustering (DCEC). By balancing weights between the minority and majority seismic samples during network training, this approach adeptly attenuates biases toward the majority classes while concurrently bolstering the efficacy of SFA. Experimental evaluations conducted on synthetic and real field datasets compellingly underscore the effectiveness and utility of the proposed CDEC method.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)