T his study presents a comparative analysis of two image enhancem ent techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute anal ysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute ana lysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.
Lake, L.W. and Carroll, H.B. (1986) Reservoir Characterization. Academic Press, Cambridge.
Chopra, S. and Marfurt, K.J. (2007) Seismic Attributes for Prospect Identification and Reservoir Characterization. Society of Exploration Geophysicists and European Association of Geoscientists and Engineers. https://doi.org/10.1190/1.9781560801900
Zhao, J., Zeng, Z., Zhou, S., Yan, J. and An, B. (2023) 3-D Inversion of Gravity Data of the Central and Eastern Gonghe Basin for Geothermal Exploration. Energies, 16, Article 2277. https://doi.org/10.3390/en16052277
Kang, B., Jung, H., Jeong, H. and Choe, J. (2020) Characterization of Three-Dimensional Channel Reservoirs Using Ensemble Kalman Filter Assisted by Principal Component Analysis. Petroleum Science, 17, 182-195. https://doi.org/10.1007/s12182-019-00362-8
Kuuskraa, V. (1982) Unconventional Natural Gas. In: Auer, P., Ed., Advances in Energy Systems and Technology, Academic Press, Cambridge, 1-126. https://doi.org/10.1016/B978-0-12-014903-2.50006-3
Oumarou, S., Mabrouk, D., Tabod, T.C., Marcel, J., Ngos Iii, S., Essi, J.M.A. and Kamguia, J. (2021) Seismic Attributes in Reservoir Characterization: An Overview. Arabian Journal of Geosciences, 14, Article No. 402. https://doi.org/10.1007/s12517-021-06626-1
Senosy, A.H., Ewida, H.F., Soliman, H.A. and Ebraheem, M.O. (2020) Petrophysical Analysis of Well Logs Data for Identification and Characterization of the Main Reservoir of Al Baraka Oil Field, Komombo Basin, Upper Egypt. SN Applied Sciences, 2, Article No. 1293. https://doi.org/10.1007/s42452-020-3100-x
Qodri, M.N., Mulyani, M.C., Kaisagara, A.W., Sukmono, S. and Ambarsari, D.S. (2019) Evaluation of Continuous Wavelet Transform (CWT) Attribute in Analysis of Gas Reservoir Distribution on Talang Akar Reservoir in “QDR” Field of Northwest Java Basin. IOP Conference Series: Earth and Environmental Science, 318, Article ID: 012043. https://doi.org/10.1088/1755-1315/318/1/012043
Alvarado, V. and Manrique, E. (2010) Enhanced Oil Recovery Concepts. In: Alvarado, V. and Manrique, E., Eds., Enhanced Oil Recovery, Gulf Professional Publishing, Houston, 7-16. https://doi.org/10.1016/B978-1-85617-855-6.00008-5
Castagna, J.P. and Sun, S. (2006) Comparison of Spectral Decomposition Methods. First Break, 24, 75-79. https://doi.org/10.3997/1365-2397.24.1093.26885
Komorowski, D. and Pietraszek, S. (2016) The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-Channel Electrogastrography Recordings. Journal of Medical Systems, 40, Article No. 10. https://doi.org/10.1007/s10916-015-0358-4
Farfour, M., Yoon, W.J., Gaci, S. and Ouabed, N. (2020) Spectral Decomposition and Avo-Based Amplitude Decomposition: A Comparative Study and Application. Journal of Seismic Exploration, 29, 261-273.
Pandey, G., Vachak, H.S., Naithani, A.C. and Singh, D. (2017) Comparative Study of Spectral Decomposition Methods and Their Implication in Delineation of Geological Features: A Case Study from North Assam Shelf, India. SPG-India.
Ribeiro, K.M., Júnior, R.A. B., Sáfadi, T. and Horgan, G. (2013) Comparison between Fourier and Wavelets Transforms in Biospeckle Signals. Applied Mathematics, 4, 11-22. https://doi.org/10.4236/am.2013.411A3003
Cerna, M. and Harvey, A.F. (2000) The Fundamentals of FFT-Based Signal Analysis and Measurement (Application Note 041 340555B-01). National Instruments Corporation.
Liu, Y. and Fomel, S. (2013) Seismic Data Analysis Using Local Time-Frequency Decomposition. Geophysical Prospecting, 61, 516-525. https://doi.org/10.1111/j.1365-2478.2012.01062.x
Rioul, O. and Duhamel, P. (1992) Fast Algorithms for Discrete and Continuous Wavelet Transforms. IEEE Transactions on Information Theory, 38, 569-586. https://doi.org/10.1109/18.119724
Akin, M. (2002) Overview of FFT and CWT Techniques. Journal of Medical Systems, 26, 241-247. https://doi.org/10.1023/A:1015075101937
Tary, J.B., Herrera, R.H. and Van Der Baan, M. (2018) Analysis of Time-Varying Signals Using Continuous Wavelet and Synchrosqueezed Transforms. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376, Article ID: 20170254. https://doi.org/10.1098/rsta.2017.0254
Wang, Y. and He, P. (2023) Comparisons between Fast Algorithms for the Continuous Wavelet Transform and Applications in Cosmology: The 1D Case. RAS Techniques and Instruments, 2, 307-323. https://doi.org/10.1093/rasti/rzad020
Biswas, A. and Si, B.C. (2011) Application of Continuous Wavelet Transform in Examining Soil Spatial Variation: A Review. Mathematical Geosciences, 43, 379-396. https://doi.org/10.1007/s11004-011-9318-9
Bouganssa, I., Sbihi, M. and Zaim, M. (2017) Implementation in an FPGA Circuit of Edge Detection Algorithm Based on the Discrete Wavelet Transforms. Journal of Physics: Conference Series, 870, Article ID: 012016. https://doi.org/10.1088/1742-6596/870/1/012016
Omachi, M. and Omachi, S. (2007) Fast Calculation of Continuous Wavelet Transform Using Polynomial. 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, 2-4 November 2007, 1688-1691. https://doi.org/10.1109/ICWAPR.2007.4421725
Arfken, G.B., Weber, H.J. and Harris, F.E. (2013) Mathematical Methods for Physicists: A Comprehensive Guide. 7th Edition, Academic Press, Cambridge.
Nainggolan, T.B., Manai Muh, N.I. and Subarsyah, S. (2018) Spectral Decomposition with Continuous Wavelet Transform for Hydrocarbon Zone Detection of North Bali Waters. Bulletin of the Marine Geology, 33, 79-92. https://doi.org/10.32693/bomg.33.2.2018.556
Torrence, C. and Compo, G.P. (1998) A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79, 61-78. https://doi.org/10.1175/1520-0477(1998)079 2.0.CO;2
Mateo, C. and Talavera, J.A. (2020) Bridging the Gap between the Short-Time Fourier Transform (STFT), Wavelets, the Constant-Q Transform and Multi-Resolution STFT. Signal, Image and Video Processing, 14, 1535-1543. https://doi.org/10.1007/s11760-020-01701-8
Naseer, M.T. and Asim, S. (2018) Characterization of Shallow-Marine Reservoirs of Lower Eocene Carbonates, Pakistan: Continuous Wavelet Transforms-Based Spectral Decomposition. Journal of Natural Gas Science and Engineering, 56, 629-649. https://doi.org/10.1016/j.jngse.2018.06.010
Wirsing, K. (2021) Time Frequency Analysis of Wavelet and Fourier Transform. In: Mohammady, S., Ed., Wavelet Theory, IntechOpen, Rijeka, 3-20. https://doi.org/10.5772/intechopen.94521
Hu, J., Jia, F. and Liu, W. (2023) Application of Fast Fourier Transform. Highlights in Science, Engineering and Technology, 38, 590-597. https://doi.org/10.54097/hset.v38i.5888
Arts, L.P.A. and Van Den Broek, E.L. (2022) The Fast Continuous Wavelet Transformation (FCWT) for Real-Time, High-Quality, Noise-Resistant Time-Frequency Analysis. Nature Computational Science, 2, 47-58. https://doi.org/10.1038/s43588-021-00183-z
Candra, A.D., Suranta, B.Y., Sulistiyono, Maulidiyah, N.L., Syafriya, A., Widya, D. and Sungkono, (2021) Application of Continuous Wavelet Transform to Layer Boundaries Detection from Gamma Ray Log. 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020), Magelang, 18 November 2020, 215-221. https://doi.org/10.2991/aer.k.210810.036
De Figueiredo, L.P., Grana, D. and Le Ravalec, M. (2020) Revisited Formulation and Applications of FFT Moving Average. Mathematical Geosciences, 52, 801-816. https://doi.org/10.1007/s11004-019-09826-4
Vega, N.R. (2003) Reservoir Characterization Using Wavelet Transforms. Master’s Thesis, The University of Texas, Austin.
Yu, Z. (2015) A CG-FFT Based Fast Full Wave Imaging Method and Its Potential Industrial Applications. Ph.D. Thesis, Duke University, Durham.
Cormen, T.H., Leiserson, C.E., Rivest, R.L. and Stein, C. (2022) Introduction to Algorithms. 4th Edition, The MIT Press, Cambridge.
Granero-Belinchón, C., Roux, S.G. and Garnier, N.B. (2021) Quantifying Non-Stationarity with Information Theory. Entropy, 23, Article 1609. https://doi.org/10.3390/e23121609
Sysel, P. and Rajmic, P. (2012) Goertzel Algorithm Generalized to Non-Integer Multiples of Fundamental Frequency. EURASIP Journal on Advances in Signal Processing, 2012, Article No. 56. https://doi.org/10.1186/1687-6180-2012-56
Viswanathan, M. (2019) Digital Modulations Using Python. Mathuranathan Viswanathan. https://www.gaussianwaves.com
Mallat, S.G. (2009) A Wavelet Tour of Signal Processing: The Sparse Way. 3rd Edition, Academic Press, Cambridge.
Bischoff, F.A. (2019) Computing Accurate Molecular Properties in Real Space Using Multiresolution Analysis. Advances in Quantum Chemistry, 79, 3-52. https://doi.org/10.1016/bs.aiq.2019.04.003
Gogolewski, D. (2020) Influence of the Edge Effect on the Wavelet Analysis Process. Measurement, 152, Article ID: 107314. https://doi.org/10.1016/j.measurement.2019.107314
Ieng, S.H., Lehtonen, E. and Benosman, R. (2018) Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals. Frontiers in Neuroscience, 12, Article 373. https://doi.org/10.3389/fnins.2018.00373
Johnson, D. (2023) Electrical Engineering. Open Education Resource (OER) LibreTexts Project. https://libretexts.org
Bozhokin, S., Suslova, I. and Tarakanov, D. (2019) Elimination of Boundary Effects at the Numerical Implementation of Continuous Wavelet Transform to Nonstationary Biomedical Signals. Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, Prague, 22-24 February 2019, 21-32. https://doi.org/10.5220/0007254900210032
Hlawatsch, F. and Matz, G. (2003) Time Frequency Signal Analysis and Processing. Academic Press, Cambridge.
Ayu, H.D. and Sarwanto, S. (2019) Analysis of Seismic Signal in Order to Determine Subsurface Characteristics. Journal of Physics: Conference Series, 1375, Article ID: 012079 https://doi.org/10.1088/1742-6596/1375/1/012079
Lu, A. and Honarvar Shakibaei Asli, B. (2023) Seismic Image Identification and Detection Based on Tchebichef Moment Invariant. Electronics, 12, Article 3692. https://doi.org/10.3390/electronics12173692
Rekapalli, R., Tiwari, R.K., Dhanam, K. and Seshunarayana, T. (2014) T-X Frequency Filtering of High Resolution Seismic Reflection Data Using Singular Spectral Analysis. Journal of Applied Geophysics, 105, 180-184. https://doi.org/10.1016/j.jappgeo.2014.03.017
Ali, A., Chen, S.C. and Shah, M. (2020) Continuous Wavelet Transformation of Seismic Data for Feature Extraction. SN Applied Sciences, 2, Article No. 1835. https://doi.org/10.1007/s42452-020-03618-w
Lapins, S., Roman, D.C., Rougier, J., De Angelis, S., Cashman, K.V. and Kendall, J.M. (2020) An Examination of the Continuous Wavelet Transform for Volcano-Seismic Spectral Analysis. Journal of Volcanology and Geothermal Research, 389, Article ID: 106728. https://doi.org/10.1016/j.jvolgeores.2019.106728
Yang, Y., Liu, C. and Langston, C.A. (2020) Processing Seismic Ambient Noise Data with the Continuous Wavelet Transform to Obtain Reliable Empirical Green’S Functions. Geophysical Journal International, 222, 1224-1235. https://doi.org/10.31223/OSF.IO/YQVNJ
Sang, Y.F., Wang, D., Wu, J.C., Zhu, Q.P. and Wang, L. (2013) Improved Continuous Wavelet Analysis of Variation in the Dominant Period of Hydrological Time Series. Hydrological Sciences Journal, 58, 118-132. https://doi.org/10.1080/02626667.2012.742194
Gao, H., Wu, X. and Liu, G. (2021) ChannelSeg3D: Channel Simulation and Deep Learning for Channel Interpretation in 3D Seismic Images. Geophysics, 86, IM73-IM83. https://doi.org/10.1190/geo2020-0572.1
Gao, H., Wu, X., Zhang, J., Sun, X., and Bi, Z. (2023) ClinoformNet-1.0: Stratigraphic Forward Modeling and Deep Learning for Seismic Clinoform Delineation, Geoscientific Model Development, 16, 2495-2513. https://doi.org/10.5194/gmd-16-2495-2023
Sara, U., Akter, M. and Uddin, M.S. (2019) Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study. Journal of Computer and Communications, 7, 8-18. https://doi.org/10.4236/jcc.2019.73002
Atta-Peters, D. and Garrey, P. (2014) Source Rock Evaluation and Hydrocarbon Potential in the Tano Basin, South Western Ghana, West Africa. International Journal of Oil, Gas and Coal Engineering, 2, 66-77. https://doi.org/10.11648/j.ogce.20140205.11
Bempong, F.K., Ozumba, B.M., Hotor, V., Takyi, B. and Nwanjide, C.S. (2019) A Review of the Geology and the Petroleum Potential of the Cretaceous Tano Basin of Ghana. Journal of Petroleum & Environmental Biotechnology, 10, Article ID: 1000395. https://www.researchgate.net/publication/338047008
Martin, J., Duval, G. and Lamourette, L. (2015) What Lies Beneath the Deepwater Tano Basin? Hunting for Jubilee-Like Prospects in Côte D’Ivoire. GeoexPro. https://www.cgg.com/sites/default/files/2020-11/cggv_0000025442.pdf
Owusu, P.A., Dehua, L. and Nagre, R.D. (2018) Prediction of Reservoir Characteristics in Western Ghana Oilfield (Tano Basin). Petroleum and Coal, 60, 483-495. https://www.vurup.sk/wp-content/uploads/2018/06/PC_3_2018_Owusu_21fin.pdf
Barnes, A.E. (1993) Instantaneous Spectral Bandwidth and Dominant Frequency with Applications to Seismic Reflection Data. Geophysics, 58, 419-428. https://doi.org/10.1190/1.1443425
Steeghs, P. and Drijkoningen, G. (1996) Time-Frequency Analysis of Seismic Reflection Signals. 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference, Atlanta, 9 May 1996, 2972-2975. https://doi.org/10.1109/ICASSP.1996.550178
Vasudevan, K. and Cook, F.A. (2001) Time-Frequency Analysis of Deep Crustal Reflection Seismic Data Using Wigner-Ville Distributions. Canadian Journal of Earth Sciences, 38, 1027-1035. https://doi.org/10.1139/e01-003
Dutta, N., Kaliannan, P. and Shanmugam, P. (2022) Application of Machine Learning for Inter Turn Fault Detection in Pumping System. Scientific Reports, 12, Article No. 12906. https://doi.org/10.1038/s41598-022-16987-6
Kong, L.J., Huang, Y.W., Yu, Q.B., Long, J.Y. and Yang, S. (2021) Joint Feature Enhancement Mapping and Reservoir Computing for Improving Fault Diagnosis Performance. IOP Conference Series: Materials Science and Engineering, 1207, Article ID: 012020. https://doi.org/10.1088/1757-899X/1207/1/012020
Cao, A., Stump, B. and DeShon, H. (2018) High-Resolution Seismic Data Regularization and Wavefield Separation. Geophysical Journal International, 213, 684-694. https://doi.org/10.1093/gji/ggy009
Blanchard, T.D. (2011) Time-Lapse Seismic Attenuation as a Tool for Monitoring Hydrocarbons and CO2 in Geological Materials. Ph.D. Thesis, University of Leeds, Leeds. https://core.ac.uk/download/pdf/1146024.pdf
Farfour, M. and Yoon, W.J. (2016) A Review on Multicomponent Seismology: A Potential Seismic Application for Reservoir Characterization. Journal of Advanced Research, 7, 515-524. https://doi.org/10.1016/j.jare.2015.11.004
Imhof, M.G. and Castle, J.W. (2005) Seismic Determination of Reservoir Heterogeneity: Application to the Characterization of Heavy Oil Reservoirs (Technical Report DE-FC26-00BC15301). U.S. Department of Energy Office of Scientific and Technical Information. https://www.osti.gov/servlets/purl/838022
Ajaz, M., Ouyang, F., Wang, G.H., Liu, S.L., Wang, L.X. and Zhao, J.G. (2021) Fluid Identification and Effective Fracture Prediction Based on Frequency-Dependent AVOAz Inversion for Fractured Reservoirs. Petroleum Science, 18, 1069-1085. https://doi.org/10.1016/j.petsci.2021.07.011
Terzariol, M., Park, J., Castro, G.M. and Santamarina, J.C. (2020) Methane Hydrate-Bearing Sediments: Pore Habit and Implications. Marine and Petroleum Geology, 116, Article ID: 104302. https://doi.org/10.1016/j.marpetgeo.2020.104302