The solution of the task of dynamic interpretation of seismic data using machine learning methods

   This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.

1. Amani, M. M. M. (2019). Primenenie metodov geostatistiki v faktorno-regressionnom prognozirovanii poristosti kollektorov po seysmicheskim atributam. Molodezh' i sovremennye informatsionnye tekhnologii : sbornik trudov XVI Mezhdunarodnoy nauchno-prakticheskoy konferentsii studentov, aspirantov i molodykh uchenykh. Tomsk, TPU Publ., pp. 156-157. (In Russian).

2. Tarantola, A. (2005). Inverse problem theory and methods for model parameter estimation. Philadelphia, Society for industrial and applied mathematics, 342 p. (In English). DOI: 10.1137/1.9780898717921

3. Wang, Z., Di, H., Shafiq, M. A., Alaudah, Y., & AlRegib, G. (2018). Successful leveraging of image processing and machine learning in seismic structural interpretation : A review. The Leading Edge, 37(6), pp. 451-461. (In English). DOI: 10.1190/tle37060451.1

4. Obinnaya Chikezie Victor, N., & Oghenechodja Daniel, L. (2023). Automated Seismic Interpretation: Machine Learning Technologies are Being Used to Develop Automated Seismic Interpretation to Identify Geological Features, Such as Faults and Stratigraphic Horizons, 3(2), pp. 74-98. (In English). DOI: 10.51483/IJAIML.3.2.2023.74-98

5. Alatefi, S., Abdel Azim, R., Alkouh, A., & Hamada, G. (2023). Integration of multiple bayesian optimized machine learning techniques and conventional well logs for accurate prediction of porosity in carbonate reservoirs. Processes, 11(5), рр.1339-1361. (In English). DOI: 10.3390/pr11051339

6. Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems, pp. 1-15. Berlin, Heidelberg, Springer Berlin Heidelberg. (In English). DOI: 10.1007/3-540-45014-9_1

7. Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and systems magazine, 6(3), рр. 21-45. (In English). DOI: 10.1109/MCAS.2006.1688199

8. Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of artificial intelligence research, 11, рр.169-198. (In English). DOI: 10.1613/jair.614

9. Bühlmann, P., & Yu, B. (2003). Boosting with the L 2 loss: regression and classification. Journal of the American Statistical Association, 98(462), рр. 324-339. (In English). DOI: 10.1198/016214503000125

10. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), рр. 119-139. (In English). DOI: 10.1006/jcss.1997.1504

11. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31, pp. 6638-6648. (In English). DOI: 10.48550/arXiv.1706.09516

12. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 1, pp. 1189-1235. (In English). DOI: 10.1214/aos/1013203451

13. Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees // Machine learning, 63, pp. 3-42. (In English). DOI: 10.1007/s10994-006-6226-1

14. Breiman, L. (2001). Random Forests. Machine Learning, 45, pp. 5-32. (In English). DOI: 10.1023/A:1010933404324

15. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,... Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of machine Learning research, 12, pp. 2825-2830. (In English). DOI: 10.5555/1953048.2078195

16. Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, Springer, 2, pp. 1-758. (In English).

17. GridSearchCV. Scikit-learn. (In English). Available at: https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html

18. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25, pp. 197-227. (In English). DOI: 10.1007/s11749-016-0481-7

19. Long, P. M., & Servedio, R. A. (2010). Random classification noise defeats all convex potential boosters. Mach Learn, 78, pp. 287-304. (In English). DOI: 10.1007/s10994-009-5165-zs

20. Natekin, A., & Knoll, A. (2013). Gradient Boosting Machines, a Tutorial. Frontiers in Neurorobotics, 7(21). (In English). DOI: 10.3389/fnbot.2013.00021