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Burton, C.; Rastin, S.J.; Taylor-Offord, S. 2024 Defining ‘good’ in pursuit of ‘better’: a consultation-driven effort to define and measure seismic data quality in Aotearoa New Zealand. Lower Hutt, NZ: GNS Science. GNS Science report 2022/20. 48 p.; doi: 10.21420/FHVD-6307
Abstract:
Historically, there has not been enough data to satisfy data uses. Today, data is increasingly abundant, and the value of improving data quality is now comparable with the value
of expanding data collection. However, to improve data quality requires an understanding of what makes data ‘good’. For some aspects of data quality this is obvious, but for others it
is not. The same is true for how to measure data quality and identify data issues. In this work we pose the question ‘What makes seismic data good?’ to a group of expert seismic data
users from Aotearoa New Zealand and consult with this expert group in three stages, working to present and test: (1) our understanding of the concept of ‘good’, (2) possible definitions
of ‘good’ and (3) potential methods of measuring ‘good’ in practice. From our consultation we produce a conceptual model accounting for the variation of seismic data uses and associated definitions of ‘good’. By exploring the nature of data use within this model, we derive a definition of ‘good’ that accommodates many of these applications and definitions. Using consultation outputs as a guide, we translate our definition of ‘good’ into a form that can be measured against real data. We explore the application of our definition through examples of its use and detail some of its limitations, which could be addressedin future work. To conclude, we present a vision of the future where data quality information is as abundant as the data it describes.
