A review of seismic attribute taxonomies, discussion of their historical use, and presentation of a seismic attribute communication framework using data analysis concepts

Beginning in the 1970s, seismic attributes have grown from a few simple measurements of wavelet amplitude, frequency, and phase to an expanded attribute toolbox that measures not only wavelet properties but also their context within the 3D seismic volume. When using multiple seismic attributes, the interpreter must understand not only each individual attribute but also the relationships between them. Researchers communicate these relationships via seismic attribute taxonomies, which group attributes by their signal property, mathematical formulation, or their interpretive value. The first attempts to organize seismic attributes began in the 1990s, and with new attributes and their increasing breadth of applications, continues to this day. Most scientific papers that use seismic attributes focus on a specific application, new algorithms, or a novel interpretation workflow, rather than how a specific attribute fits within the greater whole, leading to confusion for the less experienced interpreter. We have analyzed more than 2100 citing works, identified the 231 papers that discuss the taxonomies specifically, and found out how the authors use those citations. The result is a list of more than a dozen seismic attribute classification systems, which we reduce to a smaller subset by including only those that apply to general use. An optimal seismic attribute taxonomy should not only be useful to the interpretation community today, but it should also adapt to the ever-changing needs of the profession, including changes appropriate for their use in modern machine-learning algorithms. The adaptability of prior work to modern workflows remains a shortcoming. However, as we develop our work in two parts — the first covering the evolution of seismic attribute taxonomies and their use through time and the second proposing a new seismic attribute communication framework for the larger community — we link attributes together via data analysis principles and provide an extensible model as the profession and research expand.