A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. V79-V89 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail ◽  
Ayman Al-Lehyani
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Convex Sets ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Color Image Segmentation ◽  
High Signal ◽  
Data Sets ◽  
First Arrival

We introduce a new method for first-arrival picking based on digital color-image segmentation of energy ratios of refracted seismic data. The method uses a new color-image segmentation scheme based on projection onto convex sets (POCS). The POCS requires a reference color for the first break and one iteration to segment the first-break amplitudes from other arrivals. We tested the segmentation method on synthetic seismic data sets with various amounts of additive Gaussian noise. The proposed method gives similar performance to a modified version of Coppens’ method for traces with high signal-to-noise ratio and medium-to-large offsets. Finally, we applied our method and used as well the modified first-arrival picking method based on Coppens’ method to pick the first arrivals on four real data sets, where both were compared to the first breaks that were picked manually and then interpolated. Based on an assessment error of a 20-ms window with respect to manual picks that are interpolated, we find that our method gives comparable performance to Coppens’ method, depending on the data difficulty of picking first arrivals. Therefore, we believe that our proposed method is a good new addition to the existing methods of first-arrival picking.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

scholarly journals Amplitude-Preserved Wave Equation: An Example to Image the Gas Hydrate System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3700
Author(s):  
Jiachun You ◽  
Sha Song ◽  
Umberta Tinivella ◽  
Michela Giustiniani ◽  
Iván Vargas-Cordero
Keyword(s):  
Wave Equation ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Matrix Decomposition ◽  
Real Data ◽  
High Signal ◽  
Imaging Result ◽  
Imaging Results ◽  
Seismic Records

Natural gas hydrate is an important energy source. Therefore, it is extremely important to provide a clear imaging profile to determine its distribution for energy exploration. In view of the problems existing in conventional migration methods, e.g., the limited imaging angles, we proposed to utilize an amplitude-preserved one-way wave equation migration based on matrix decomposition to deal with primary and multiple waves. With respect to seismic data gathered at the Chilean continental margin, a conventional processing flow to obtain seismic records with a high signal-to-noise ratio is introduced. Then, the imaging results of the conventional and amplitude-preserved one-way wave equation migration methods based on primary waves are compared, to demonstrate the necessity of implementing amplitude-preserving migration. Moreover, a simple two-layer model is imaged by using primary and multiple waves, which proves the superiority of multiple waves in imaging compared with primary waves and lays the foundation for further application. For the real data, the imaging sections of primary and multiple waves are compared. We found that multiple waves are able to provide a wider imaging illumination while primary waves fail to illuminate, especially for the imaging of bottom simulating reflections (BSRs), because multiple waves have a longer travelling path and carry more information. By imaging the actual seismic data, we can make a conclusion that the imaging result generated by multiple waves can be viewed as a supplementary for the imaging result of primary waves, and it has some guiding values for further hydrate and in general shallow gas exploration.

scholarly journals A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1599
Author(s):  
Bowen Wu ◽  
Liangkuan Zhu ◽  
Jun Cao ◽  
Jingyu Wang
Keyword(s):  
Image Segmentation ◽  
Time Delay ◽  
Optimization Algorithm ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Evolutionary State

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation

Color Image Segmentation Using Entropy of Fuzziness and Markov Random Field

1997 ◽  
Vol 30 (7) ◽  
pp. 269-274
Author(s):  
R. Boussarsar ◽  
P. Martin ◽  
R. Lecordier ◽  
M. Ketata
Keyword(s):  
Image Segmentation ◽  
Random Field ◽  
Markov Random Field ◽  
Color Image ◽  
Color Image Segmentation ◽  
Markov Random
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