Effect of low‐frequency seismic exploration signals on the cetaceans of the Gulf of Mexico

1998 ◽  
Vol 103 (5) ◽  
pp. 2908-2908 ◽  
Author(s):  
Shannon Rankin ◽  
William E. Evans
Keyword(s):  
Gulf Of Mexico ◽  
Low Frequency ◽  
Seismic Exploration

Environmental Effects on Low Frequency Transmission Loss in the Gulf of Mexico

1980 ◽  
Author(s):  
Burlie A. Brunson ◽  
M. M. Truxillo ◽  
Richard B. Evans
Keyword(s):  
Gulf Of Mexico ◽  
Environmental Effects ◽  
Transmission Loss ◽  
Low Frequency

scholarly journals Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR

2021 ◽  
Vol 11 (11) ◽  
pp. 5028
Author(s):  
Miaomiao Sun ◽  
Zhenchun Li ◽  
Yanli Liu ◽  
Jiao Wang ◽  
Yufei Su
Keyword(s):  
Reflection Coefficient ◽  
Compressed Sensing ◽  
Objective Function ◽  
Low Frequency ◽  
Original Data ◽  
Seismic Exploration ◽  
High Signal ◽  
Inversion Process ◽  
Frequency Information ◽  
Low Snr

Low-frequency information can reflect the basic trend of a formation, enhance the accuracy of velocity analysis and improve the imaging accuracy of deep structures in seismic exploration. However, the low-frequency information obtained by the conventional seismic acquisition method is seriously polluted by noise, which will be further lost in processing. Compressed sensing (CS) theory is used to exploit the sparsity of the reflection coefficient in the frequency domain to expand the low-frequency components reasonably, thus improving the data quality. However, the conventional CS method is greatly affected by noise, and the effective expansion of low-frequency information can only be realized in the case of a high signal-to-noise ratio (SNR). In this paper, well information is introduced into the objective function to constrain the inversion process of the estimated reflection coefficient, and then, the low-frequency component of the original data is expanded by extracting the low-frequency information of the reflection coefficient. It has been proved by model tests and actual data processing results that the objective function of estimating the reflection coefficient constrained by well logging data based on CS theory can improve the anti-noise interference ability of the inversion process and expand the low-frequency information well in the case of a low SNR.

Influence of frequency-dependent spherical-wave effect on low-frequency seismic exploration: An example of AVA analysis

2017 ◽  
Author(s):  
Jingnan Li ◽  
Xinchao Yang ◽  
Shangxu Wang ◽  
Chunhui Dong ◽  
Shuangquan Chen
Keyword(s):  
Spherical Wave ◽  
Low Frequency ◽  
Seismic Exploration ◽  
Wave Effect ◽  
Frequency Dependent

scholarly journals Low‐frequency bottom scattering strengths for a shallow water site in the Gulf of Mexico

1993 ◽  
Vol 93 (4) ◽  
pp. 2395-2395
Author(s):  
Peter G. Cable ◽  
Theo Kooij ◽  
Mike Steele
Keyword(s):  
Gulf Of Mexico ◽  
Shallow Water ◽  
Low Frequency

scholarly journals Gulf of Mexico low-frequency ocean soundscape impacted by airguns

2016 ◽  
Vol 140 (1) ◽  
pp. 176-183 ◽  
Author(s):  
Sean M. Wiggins ◽  
Jesse M. Hall ◽  
Bruce J. Thayre ◽  
John A. Hildebrand
Keyword(s):  
Gulf Of Mexico ◽  
Low Frequency

scholarly journals Low-frequency noise suppression for desert seismic data based on a wide inference network

2019 ◽  
Vol 16 (4) ◽  
pp. 801-810
Author(s):  
Yue Li ◽  
Wei Yu ◽  
Chao Zhang ◽  
Baojun Yang
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Seismic Exploration ◽  
Frequency Noise ◽  
Layer By Layer ◽  
Low Frequency Noise ◽  
Time Space ◽  
Manual Adjustment ◽  
Inference Network ◽  
Seismic Records

Abstract The importance of seismic exploration has been recognized by geophysicists. At present, low-frequency noise usually exists in seismic exploration, especially in desert seismic records. This low-frequency noise shares the same frequency band with effective signals. This leads to the limitation or failure of traditional methods. In order to overcome the shortcomings of traditional denoising methods, we propose a novel desert seismic data denoising method based on a Wide Inference Network (WIN). The WIN aims to minimize the error between the prediction and target by residual learning during training, and it can obtain a set of optimal parameters, such as weights and biases. In this article, we construct a high-quality training set for a desert seismic record and this ensures the effective training of a WIN. In this way, each layer of the trained WIN can automatically extract a set of time–space characteristics without manual adjustment. These characteristics are transmitted layer by layer. Finally, they are utilized to extract effective signals. To verify the effectiveness of the WIN, we apply it to synthetic and real desert seismic records, respectively. In addition, we compare WIN with f – x deconvolution, variational mode decomposition (VMD) and shearlet transform. The results show that WIN has the best denoising performance in suppressing low-frequency noise and preserving effective signals.

Effects of averaging and sampling on the statistics of reflection coefficients

Geophysics ◽  
1991 ◽  
Vol 56 (1) ◽  
pp. 50-58 ◽  
Author(s):  
K. Hsu ◽  
R. Burridge
Keyword(s):  
Reflection Coefficient ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Exploration ◽  
Reflection Coefficients ◽  
Continuous Formation ◽  
Sampling Intervals ◽  
Frequency Components ◽  
Analytical Formulas

The reflection coefficients derived from sonic and density logs are frequently used in seismic exploration. Even though they measure the in‐situ formation slowness and density, sonic and density tools do not measure the exact, continuous formation properties but locally averaged properties sampled at discrete depth points. Furthermore, the logs are frequently reinterpolated to form a Goupillaud medium for many applications such as synthetic seismogram computation. Both the logging tools and the Goupillaud interpolation introduce averaging and sampling effects into the reflection coefficients and significantly alter the autocorrelation of the reflection coefficient sequence. Analytical formulas are derived to show how the autocorrelation is altered and to calculate how the autocorrelation depends on the averaging and sampling intervals. Essentially, these effects impose sincsquared envelopes on the power spectrum of the reflection coefficient sequence and alias high‐frequency components to low‐frequency components in the spectral domain. These findings are verified using synthetic and real examples.

The Wolfspar® Field Trial: Design and execution of a low-frequency seismic survey in the Gulf of Mexico

2018 ◽  
Author(s):  
Robert Pool ◽  
Chinaemerem Kanu ◽  
Andrew Brenders ◽  
Joseph Dellinger
Keyword(s):  
Gulf Of Mexico ◽  
Field Trial ◽  
Trial Design ◽  
Low Frequency ◽  
Seismic Survey
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