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Abstract

Summary

We present the results of high-resolution seismic data processing based on attribute analysis, involving unsupervised machine learning frameworks fully automated and developed using Python libraries. This integration offers a powerful tool for auto-detecting shallow subsurface hazards and, in general, for interpreting seismic data. The value of data-driven methods lies in how attribute analysis and machine learning algorithms (in our case k-means clustering) can complement classical interpretation techniques, providing a faster and more objective approach to seismic data analysis, with the potentiality for multifrequency data processing in the future.

The proposed technique enables initial qualitative interpretation, such as identifying the presence and distribution of gas deposits in sedimentary layers. Furthermore, it facilitates subsequent quantitative assessment, such as measuring seismic signal intensity to automatically detect and classify the most significant anomalies.

Future improvements will include further development of the current algorithm within a potential software environment, evaluation of alternative machine learning frameworks and integration of multifrequency and bathy-morphological data, aiming to get the maximum applicable information from the dataset.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202520036
2025-09-07
2026-02-13

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References

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/content/papers/10.3997/2214-4609.202520036
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Attribute and Machine Learning-Based Analysis of High-Resolution Seismic Data for Interpretation and Offshore Geohazard Detection
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202520036
/content/papers/10.3997/2214-4609.202520036
/content/papers/10.3997/2214-4609.202520036
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