# Research on a 3D Seismic Horizon Automatic-Tracking Method Based on Corrugated Global Diffusion
###### Abstract  
The core challenges to automatic full-horizon tracking are how to establish a potential local connection relationship between the horizon points, conduct accurate global diffusion in a three-dimensional space, and finally, how to form a complex horizon surface. The existing attribute-based horizon-tracking methods based on waveform similarity, dip guidance, and RGT (relative geological time) can not solve the problems of local connection and global diffusion at the same time. In view of this challenge, this paper proposes an automatic 3D seismic horizon-tracking method based on global corrugated diffusion, which can completely integrate local connection and global diffusion so that all horizons in the whole data volume can be interpreted simultaneously. For the problem of local horizon-point connection, this paper uses the correlation between seismic trace pairs based on DTW (dynamic time warping) correlation to mine the connection mode between horizon points. For the global diffusion problem, this paper proposes the realization of global modeling based on the relationship between seismic samples, constructing a complex 3D horizon through a central ripple-diffusion process. The example shows that the horizon tracked by this method well reflects the original stratum occurrence and stratum-contact relationship, retains the structural details, accurately reflects the structural shape, and realizes automatic tracking across faults. automatic full-horizon tracking; dynamic time planning; corrugated global diffusion; 3D seismic data; correlation coefficients +
Footnote †: journal: Medical  
In order to address these core challenges, people have made various attempts, which can be specifically divided into three categories: horizon tracking methods based on waveform similarity [3], on dip, or on RGT. The former directly processes horizon waveforms in the time domain. The most typical work is the waveform similarity algorithm based on the cost function, as proposed by [PERSON] et al. [4], to achieve automatic horizon tracking. Horizon-tracking methods based on dip map waveform similarity into the attribute of dip, and then use dip to indicate the horizon extension direction. One typically cited work is the seismic dip-driven method by [PERSON] et al. [5], which recognizes the horizon under the guidance of dip. As for horizon-tracking methods based on RGT, these use an extension of the dip method and represent this to another new domain, the instantaneous phase domain, which is essentially another attribute. The typically cited work in this area includes [PERSON] et al. [6; 7], [PERSON] et al. [8], and [PERSON] et al. [9], who applied seismic instantaneous-phase-unraveling methods to achieve automatic horizon tracking. In practical applications, noise interference and complex region waveform distortion pose serious challenges to the assumption of the same horizon waveform similarity, leading to incorrect tracking results. In order to overcome dependence on human assumptions, people have developed AI-based methods that do not require any assumptions and hope to track the horizon in a data-driven manner. For example, [PERSON] et al. [10] applied artificial neural networks to horizon tracking; [PERSON] et al. [11] applied clustering algorithms to horizon tracking, which was mainly based on the statistical characteristics of horizon waveforms. [PERSON] et al. [12] used supervised deep learning, and [PERSON] et al. [13] adopted unsupervised deep learning methods to track horizon positions.