Application of à trous DWT in the detection of seismic thin layers

Authors

Abstract

In hydrocarbon exploration, detecting thin layers is one of the most important parts of geophysical interpretation. Seismic data received from horizons whose distances are less than the transmitted wavelength interferes in the sections will represent a single layer. It is possible to use à trous DWT domain to detect thin layers in seismic data acquired from oilfields at the first high-pass scale. In this paper, the use of instantaneous attributes in the à trous DWT domain to detect thin layers is investigated. The data is decomposed by 1-D à trous DWT. In this way, the events are filtered out and, the values matched by the mother wavelet are calculated at each scale. Once the decomposition step is applied, at high scales, the events in higher frequencies remain, and this can be a significant aid in the detection of thin layers. The results of this step indicate that applying this method to synthetic and real data can have a significant improvement in the separation of events from each other. Conventional seismic attributes have also been used to highlight the results in real data.

Keywords


پاک‌منش، پ.، گودرزی، ع.، کورکی، م.، 1397، مقایسه واهمامیخت تنک داده‌های لرزه‌ای به روشMM  و حداقل مربعات با رویکرد تشخیص لایه‌های نازک،. پژوهش‌های ژئوفیزیک کاربردی، 4 (1)، 15-26.
قانع عزآبادی، م. و جواهریان، ع.، 1391، کاربرد تحلیل طیفی لحظه ای در شناسایی سایه های فرکانس پایین وابسته به هیدروکربورها، فصلنامه زمین، 7 (23)، 31-50.
گودرزی، ع.، ملایی، ف.، ۱۳۹۷،  افزایش توان تفکیک داده های لرزه ای با استفاده از تبدیل موجک گسسته مختلط، نشریه پژوهش های ژئوفیزیک کاربردی، ۴ (۲)، ۲۱۱-۲۲۳.  
Baraniuk, R., Coates, M. and Steeghs, P., 2001. Hybrid linear/quadratic time-frequency attributes. IEEE Transactions on Signal Processing, 49 (4) , 760-766.
Castagna, J., Sun, S. and Siegfried, R., 2003. Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22 (2) , 120-127.
Cohen, L., 1989. Time-frequency distributions-a review. Proceedings of the IEEE, 77 (7) , 941-981.
Jinghuai Gao, Xiaolong Dong, Wen-Bing Wang, Youming Li and Cunhuan Pan, 1999. Instantaneous parameters extraction via wavelet transform. IEEE Transactions on Geoscience and Remote Sensing, 37 (2) , 867-870.
Liu, J. and Marfurt, K., 2007. Instantaneous spectral attributes to detect channels. GEOPHYSICS, 72 (2) , P23-P31.
Lu، J.M. Wang، Y., 2011, The Principle of Seismic Exploration; China Petroleum University Press: Beijing، China; ISBN 978-7-5636-2822-3.
Marfurt, K. and Kirlin, R., 2001. Narrow‐band spectral analysis and thin‐bed tuning. GEOPHYSICS, 66 (4) , 1274-1283.
Partyka, G., Gridley, J. and Lopez, J., 1999. Interpretational applications of spectral decomposition in reservoir characterization. The Leading Edge, 18 (3) , 353-360.
Quan, Y. and Harris, J., 1997. Seismic attenuation tomography using the frequency shift method. GEOPHYSICS, 62 (3) , 895-905.
Roshandel Kahoo, A. and Gholtashi, S., 2015, An Improvement in Temporal Resolution of Seismic Data Using Logarithmic Time-frequency Transform Method, Iranian Journal of Oil & Gas Science and Technology, 4 (2), 27-39.
Widess, M., 1973. How Thin is a Thin Bed?. Geophysics, 38 (6), 1176-1180.
Yang, C.; Wang, Y.; Lu, J.; Chen, B.C.; Shi, L. 2019, A Low-Order Series Approximation of Thin-Bed PP-Wave Reflections. Appl. Sci., 9, 709.
Yuan, S., Wang, S., Ma, M., Ji, Y. and Deng, L., 2017. Sparse Bayesian Learning-Based Time-Variant Deconvolution. IEEE Transactions on Geoscience and Remote Sensing, 55 (11) , 6182-6194.
Zhou, J., Ba, J., Castagna, J., Guo, Q., Yu, C. and Jiang, R., 2019. Application of an STFT-Based Seismic Even and Odd Decomposition Method for Thin-Layer Property Estimation. IEEE Geoscience and Remote Sensing Letters, 16(9) , 1348-1352.