Preparation of initial seismic data for modeling tectonic fault in coal body

Seismic exploration is involved in all phases of geological–geophysical activities. An important component of seismic exploration is recording of waves from the test geological boundaries and the analysis of the wave characteristics. The relevance of the study is governed by the currentness of the attribute analysis to be performed in the conditionы when a dedicated software is unavailable. The limited choice of software programs is connected with the proprietary nature of applications, or with the infeasibility of a software circulation in Russia. This study aims to compose a set of formulas for handling seismic waves so that seismic trace processing is possible without dedicated program products. The study included examination of functions of seismic processing program RadExPro and ascertainment of operating algorithm of the Seismic Attribute Analysis module. The formulas most suitable for the seismic calculations are selected and discussed. The features of calculations using the Python programming language are described. The comparison of the program outputs with the actual values revealed no significant deviations. The scientific novelty of the study lies in the found mathematical apparatus which coincides with the apparatus of the mentioned program, and which is usable as an alternative technique for seismic processing and analysis without outside software tools.

Keywords: geophysics, seismic exploration, seismic attribute analysis, Python, RadExPro, data processing, data analysis, mathematical apparatus.
For citation:

Stepanov I. Yu., Dorn E. V., Stepanov Yu. A. Preparation of initial seismic data for modeling tectonic fault in coal body. MIAB. Mining Inf. Anal. Bull. 2024;(5):5-16. [In Russ]. DOI: 10.25018/0236_1493_2024_5_0_5.

Acknowledgements:
Issue number: 5
Year: 2024
Page number: 5-16
ISBN: 0236-1493
UDK: 550.34.03
DOI: 10.25018/0236_1493_2024_5_0_5
Article receipt date: 01.06.2023
Date of review receipt: 01.08.2023
Date of the editorial board′s decision on the article′s publishing: 10.04.2024
About authors:

I.Yu. Stepanov1, Graduate Student, e-mail: zextel1995@gmail.com, ORCID ID: 0000-0002-7938-8049,
E.V. Dorn1, Student, e-mail: Kate-Kard-2010@ya.ru, ORCID ID: 0000-0002-6558-5658,
Yu.A. Stepanov1, Dr. Sci. (Eng.), Professor, e-mail: dambo290@yandex.ru, ORCID ID: 0000-0001-7552-6857,
1 Kemerovo State University, 650000, Kemerovo, Russia.

 

For contacts:

E.V. Dorn, e-mail: Kate-Kard-2010@ya.ru.

Bibliography:

1. Dobrohotova M. V. Features of the transition of the russian coal industry to the best available technologies. Ugol'. 2022, no. 9, pp. 34—40. [In Russ]. DOI: 10.18796/0041-5790-2022-9-34-40.

2. Hyder Z., Ripepi N., Karmis M. A life cycle comparison of greenhouse emissions for power generation from coal mining and underground coal gasification. Mitigation and Adaptation Strategies for Global Change. 2014, vol. 21, pp. 515—546. DOI: 10.1007/s11027-014-9561-8.

3. StepanovYu.A. Razvitie teoreticheskikh osnov geoinformatsionnykh sistem dlya prognozirovaniya sostoyaniya ugleporodnogo massiva pri vedenii ochistnykh rabot [Development of the theoretical foundations of geoinformation systems for predicting the state of the carboniferous massif during cleaning operations], Doctor’s thesis, Ekaterinburg, 2016, 32 p.

4. Tsvetkov A. B. Mathematical and software for numerical modeling of the geomechanical state of a carboniferous massif. Tomsk State University Journal of Control and Computer Science. 2022, no. 60, pp. 52—58. [In Russ].

5. Cichowicz A., Spottiswoode S., Linzer L., Drent D., Heyns S., Handley M. Improved seismic locations and location techniques. Pretoria: University of Pretoria, 2005. DOI: 10.13140/2.1.3131.6166.

6. Pozdnjakov V. A., Hudjakov S. S. Object-oriented technology for creating seismogeological models in reflected and scattered waves. Journal of Siberian federal university. Engineering & technologies. 2011, no. 4, pp. 419—428. [In Russ].

7. Kong Q., Trugman D., Ross Z., Bianco M., Meade B., Gerstoft P. Machine learning in seismology: Turning data into insights. Seismological Research Letters. 2018, vol. 90, no. 1, pp. 3—14. DOI: 10.1785/0220180259.

8. Nanishvili O. A. Taking into account the heterogeneity of the upper part of the section (HCR) when processing seismic data. Yugra State University Bulletin. 2017, no. 4(47), pp. 17—24. [In Russ].

9. Feng W., Dong S., Wang Q., Yi X., Liu Z., Bai H. Improving the Hoek–Brown criterion based on the disturbance factor and geological strength index quantification. International Journal of Rock Mechanics and Mining Sciences. 2018, vol. 108, pp. 96—104. DOI: 10.1016/j.ijrmms.2018.06.004.

10. Feoktistov A. V., Feoktistov V. A. Problems and peculiarities of interpretation of geophysical materials during the work of the forecast geological section. Volga and Pricaspian region resources. 2011, no. 67, pp. 47—68.

11. Loginov D. V., Lavrik S. A. Some methods for determining an informative set of seismic attributes for predicting reservoir properties. Petroleum Geology. Theoretical and Applied Studies. 2010, no. 1, pp. 1—12. [In Russ].

12. Volkov D. S. Possibilities of quantitative interpretation of the results of spectral decomposition of seismic data CDPM-3D. Actual Problems of Oil and Gas. 2022, no. 1(36), pp. 25—41. [In Russ]. DOI: 10.29222/ipng.2078-5712.2022-36.art2.

13. Kosobokov V. G., Solov'ev A. A. Pattern recognition in seismic hazard assessment tasks. Chebyshevskii Sbornik. 2018, no. 4(68), pp. 55—90. [In Russ].

14. Lobusev A. V., Gol' E. M., Avdeev N. S. Improving the efficiency of geological interpretation through the use of multi-wave seismic data. Neftegas Territory Journal. 2018, no. 12, pp. 18—21. [In Russ].

15. Varlamov A. I., Gogonenkov G. N., Mel'nikov P. N., Cheremisina E. N. The state and prospects of development of digital technologies in oil and gas geology and subsoil use in Russia. Russian oil and gas geology. 2021, no. 3, pp. 5—20. [In Russ].

16. Orekhov A. N., Amani M. M. M. Informative value of geometric attributes for predicting fracturing of reservoirs on the example of a hydrocarbon field in the Tomsk region. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering. 2019, no. 9, pp. 230—238. [In Russ]. DOI: 10.18799/ 24131830/2019/9.

17. Yuliatmoko R., Kurniawan T., Nur Vita A., Rohadi S., Florida N., Gunawan I., Karnawati D. Estimation site effect from the seismogram. AIP Conference Proceedings. 2021, vol. 2320, no. 1, pp. 040023—040023-6. DOI: 10.1063/5.0037541.

18. Azhgaliev D. K., Isenov S. M., Karimov S. G. New possibilities of processing and interpretation of seismic data in assessing the prospects of local objects. News of the Ural State Mining University. 2019, no. 1 (53), pp. 48—59. [In Russ]. DOI: 10.21440/2307-2091-2019-1-48-59.

19. Panagiotakis C., Kokinou E., Vallianatos F. Automatic P-Phase picking based on local-maxima distribution. IEEE Transactions on Geoscience and Remote Sensing. 2008, vol. 46, no. 8, pp. 2280— 2287. DOI: 10.1109/TGRS.2008.917272.

20. Butcher A., Luckett R., Kendall J.-M., Baptie B. Seismic magnitudes, corner frequencies, and microseismicity: Using ambient noise to correct for high-frequency attenuation. Bulletin of the Seismological Society of America. 2020, vol. 110, no. 3, article 110. DOI: 10.1785/0120190032.

21. Jiang W., Ding W., Xinke Z., Hou F. A recognition algorithm of seismic signals based on wavelet analysis. Journal of Marine Science and Engineering. 2022, vol. 10, no. 8, article 1093. DOI: 10.3390/ jmse10081093.

22. Belousov A. V. Standard assessments of the quality of field seismic material. Pribory i sistemy razvedochnoy geofiziki. 2011, no. 03(37), pp. 31—36. [In Russ].

23. Xue S., Kasztenny B., Voloh I., Oyenuga D. Power system frequency measurement for frequency relaying. Western Protective Relay Conference, Spokane, WA, 2007.

24. Bentz C., Baudzus L., Krummrich P. Signal to noise ratio (SNR) enhancement comparison of impulse-, codingand novel linear-frequency-chirp-based optical time domain reflectometry (OTDR) for passive optical network (PON) monitoring based on unique combinations of wavelength selective mirrors. Photonics. 2014, vol. 1, pp. 33—46. DOI: 10.3390/photonics1010033.

Подписка на рассылку

Раз в месяц Вы будете получать информацию о новом номере журнала, новых книгах издательства, а также о конференциях, форумах и других профессиональных мероприятиях.