An adaptive linear mode decomposition for effective separation of linear and non-linear seismic events, ground roll and random noise
| العنوان: | An adaptive linear mode decomposition for effective separation of linear and non-linear seismic events, ground roll and random noise |
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| المؤلفون: | Salman Abbasi, Siwei Yu, Jubran Akram, Md Iftekhar Alam, Bakhtawer Sarosh |
| المصدر: | GEOPHYSICS. :1-54 |
| بيانات النشر: | Society of Exploration Geophysicists, 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Geophysics, Geochemistry and Petrology |
| الوصف: | Ground roll and random noise usually mask primary reflections in land seismic data. Different sets of signal processing methods are used to suppress these two noises based on statistical and/or transformation filtering. Among these methods, linear mode decomposition (LMD) decomposes linear and nonlinear seismic events into amplitude-frequency modulated modes using the Wiener filter. Different combinations of these decomposed linear modes can then be used to represent different seismic events. However, LMD requires predefining the level of decomposition that must be selected carefully to avoid suboptimal binning, which can influence the fidelity of the decomposed seismic modes. To that end, we introduce an adaptive linear mode decomposition (ALMD) that optimally separates seismic events, ground roll, and random noise. ALMD uses the correlation between the decomposed modes and the input data to determine the decomposition level. Consequently, an optimum decomposition divides the data into linear modes with minimum mixing. In addition, unlike conventional ground roll suppression methods, ALMD does not require estimating the slope or the frequency bandwidth of the ground roll. Moreover, ALMD automates the random noise segregation by separating modes as the signal, noise, and mixed modes, based on the permutation entropy and kurtosis criteria. ALMD iteratively decomposes mixed modes with remnant random noise until a signal or noise criterion is met. Using synthetic and real data examples, we show that the proposed ALMD is an effective method for separating desired linear and nonlinear events, unwanted ground roll energy, and random noise from the seismic data. |
| تدمد: | 1942-2156 0016-8033 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::14321c16e1dedb0470383e90f0dc07ac https://doi.org/10.1190/geo2022-0470.1 |
| رقم الأكسشن: | edsair.doi...........14321c16e1dedb0470383e90f0dc07ac |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19422156 00168033 |
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