Version 1
: Received: 28 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (07:13:11 CET)
How to cite:
Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E.J. Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning. Preprints2024, 2024021696. https://doi.org/10.20944/preprints202402.1696.v1
Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E.J. Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning. Preprints 2024, 2024021696. https://doi.org/10.20944/preprints202402.1696.v1
Fraser, A.I.; Landauer, J.; Gaffney, V.; Zieschang, E.J. Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning. Preprints2024, 2024021696. https://doi.org/10.20944/preprints202402.1696.v1
APA Style
Fraser, A.I., Landauer, J., Gaffney, V., & Zieschang, E.J. (2024). Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning. Preprints. https://doi.org/10.20944/preprints202402.1696.v1
Chicago/Turabian Style
Fraser, A.I., Vincent Gaffney and Elizabeth Jean Zieschang. 2024 "Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning" Preprints. https://doi.org/10.20944/preprints202402.1696.v1
Abstract
The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated [1]. In specific areas, notably remote sensing, data sets have increased in extent and resolution to the point that manual interpretation is problematic and availability of skilled interpreters to undertake such work is limited. Interpretation of geophysical data sets associated with prehistoric submerged landscapes is particularly challenging. Following the last glacial, sea levels rose 120 metres globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing data sets were provided by the offshore energy sector. In this paper, we provide results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c.188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7,0000 BP, along with the cultural heritage it contained [2]. As part of this project, machine learning tools were applied to detect and interpret features of archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for further, more complex leveraging of AI interpretation for the study of submarine palaeolandscapes.
Image: Adobe Firefly Generative AI using keywords - marsh landscape deep underwater, auroch skull half-buried in peat
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
