• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.240-249
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• 2013

The seismic data quality of marine geological and engineering survey deteriorates because of the sea swell. We often conduct a marine survey when the swell height is about 1 ~ 2 m. The swell effect correction is required to enhance the horizontal continuity of seismic data and satisfy the resolution less than 1 m. We applied the swell correction to the 8 channel high-resolution airgun seismic data and 3.5 kHz subbottom profiler (SBP) data. The correct sea bottom detection is important for the swell correction. To detect the sea bottom, we used maximum amplitude of seismic signal around the expected sea bottom, and picked the first increasing point larger than threshold value related with the maximum amplitude. To find sea bottom easily in the case of the low quality data, we transformed the input data to envelope data or the cross-correlated data using the sea bottom wavelet. We averaged the picked sea bottom depths and calculated the correction values. The maximum correction of the airgun data was about 0.8 m and the maximum correction of two kinds of 3.5 kHz SBP data was 0.5 m and 2.0 m respectively. We enhanced the continuity of the subsurface layer and produced the high quality seismic section using the proper methods of swell correction.

• Journal of the korean society of oceanography
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• v.32 no.4
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• pp.163-170
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• 1997

Some CMP gathers acquired from shallow marine seismic reflection survey in offshore Korea do not show the hyperbolic trend of moveout. It originated from so-called swell effect of source and streamer, which are towed under rough sea surface during the data acquisition. The observed time deviations of NMO-corrected traces can be entirely ascribed to the swell effect. To correct these time deviations, a residual statics is introduced using Genetic Algorithms (GA) into the swell correction. A new class of global optimization methods known as GA has recently been developed in the field of Artificial Intelligence and has a resemblance with the genetic evolution of biological systems. The basic idea in using GA as an optimization method is to represent a population of possible solutions or models in a chromosome-type encoding and manipulate these encoded models through simulated reproduction, crossover and mutation. GA parameters used in this paper are as follows: population size Q=40, probability of multiple-point crossover P$_c$=0.6, linear relationship of mutation probability P$_m$ from 0.002 to 0.004, and gray code representation are adopted. The number of the model participating in tournament selection (nt) is 3, and the number of expected copies desired for the best population member in the scaling of fitness is 1.5. With above parameters, an optimization run was iterated for 101 generations. The combination of above parameters are found to be optimal for the convergence of the algorithm. The resulting reflection events in every NMO-corrected CMP gather show good alignment and enhanced quality stack section.

• Geophysics and Geophysical Exploration
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• v.18 no.4
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• pp.181-196
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• 2015

A 3.5 kHz or chirp sub-bottom profiling survey is widely used in the marine geological and engineering purpose exploration. However, swells in the sea degrade the quality of the survey data. The horizontal continuity of profiler data can be enhanced and the quality can be improved by correcting the influence of the swell. Accurate detection of sea bottom location is important in correcting the swell effect. In this study, we tried to pick sea bottom locations by finding the position of crossing a threshold of the maximum value for the raw data and transformed data of envelope or energy ratio. However, in case of the low-quality data where the sea bottom signals are not clear due to sea wave noise, automatic sea bottom detection at the individual traces was not successful. We corrected the mispicks for the low quality data and obtained satisfactory results by picking a sea bottom within a range considering the previous average of sea bottom, and excluding unreliable big-difference picks. In case of trace by trace picking, fewest mispicks were found when using energy ratio data. In case of picking considering the previous average, the correction result was relatively satisfactory when using raw data.

• Geophysics and Geophysical Exploration
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• v.22 no.3
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• pp.116-131
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• 2019

In order to obtain high-quality high-resolution marine seismic data, the survey needs to be carried out at very low-sea condition. However, the survey is often performed with a slight wave, which degrades the quality of data. In this case, it is possible to improve the quality of seismic data by detecting the exact location of the sea bottom signal and eliminating the influence of waves or swells automatically during data processing. However, if noise is included or the sea bottom signal is weakened due to sea waves, sea bottom detection errors are likely to occur. In this study, we applied a method reducing such errors by estimating the sea bottom location, setting a narrow detection range and detecting the sea bottom location within this range. The expected location of the sea bottom was calculated using previously detected sea bottom locations for each channel of multi-channel data. The expected location calculated in each channel is also compared and verified with expected locations of other channels in a shot gather. As a result of applying this method to the noisy 8-channel high-resolution air-gun seismic data acquired off Yeosu, the errors in selecting the strong noise before sea bottom or the strong subsurface reflected signal after the sea bottom signal are remarkably reduced and it is possible to produce the high-quality seismic section with the correction of ~ 2.5 m swell effect.