Recovering Linkage Between Seismic Images and Velocity Models

Abstract

Abstract Seismic processing and interpretation involves resource intensive processing in the petroleum exploration domain. By employing various types of models, seismic interpretations are often derived in an iterative refinement process, which may result in multiple versions of seismic images. Keeping track of the derivation history (a.k.a. provenance) for such images thus becomes an important issue for data management. Specifically, the information about what velocity model was used to generate a seismic image is useful evidence for measuring the quality of the image. The information can also be used for audit trail and image reproduction. However, in practice, existing seismic processing and interpretation systems do not always automatically capture and maintain this type of provenance information. In this paper, by employing state-of-the-art techniques in text analytics, semantic processing and machine learning, we propose an approach that recovers the linkage between seismic images and their ancestral velocity models when no provenance information is recorded. Our approach first retrieves information from file/directory names of the images and models, such as project names, processing vendors, and algorithms involved in the seismic processing and interpretation. Along with the creation timestamps, the retrieved information is associated with corresponding images and models as metadata. The metadata of a seismic image and its ancestral models usually satisfy certain relationships. In our approach, we detect and represent such relationships as rules, and a matching process utilizes the rules and retrieved metadata to find the best-matching images and models. In practice, images’ and models’ file names often do not adhere to naming standards and they are stored without following well established record keeping practices. Users may also use different terms to express the same information in file/directory names. We employ Semantic Web technologies to address this challenge. We develop domain ontologies with OWL/RDFs, based on which we provide an interactive way for users to semantically annotate terms contained in file/directory names. All metadata used by the image-model matching process is represented as ontology instances. Matching can be performed using the standard semantic query language. The evaluation results show that our approach can achieve satisfying accuracy.