First break picking

Abstract: The increase in the number of geophone groups in production records during recent years and the requirement for accurate basic static corrections for high resolution records have made it necessary to develop sufficiently accurate automatic techniques for the determination of static corrections. A fully automatic method is presented which makes use of the delay-time method in order to compute static corrections at each shot position. Delay times, weathering and subweathering velocities are determined from automatic picks of the first arrivals on common-offset trace collections. It is assumed that the weathering is a single layer and that the dip of the subweathering layer under the geophone groups is small. The picking routine is fully automatic and successful in most cases, provided the signal-to-noise ratio is sufficiently high. The subsequent filtering of erroneous values for picked times is performed by means of statistical techniques, using curves of picked times on common-offset trace collections. If the distance between receivers and shot-points on the profile is sufficiently short, one can expect only little change in the picked times of two contiguous traces. The method is well adapted to end-on spreads with a great number of traces, where distances between geophone groups are short. Examples are presented showing the possibilities of the method for the determination of long wavelength as well as short wavelength components of static corrections.

Abstract: We have developed three methods for the automatic pickingoffirstbreaksthatcanbeusedformarine,dynamite,orvibroseisshotrecords:amodifiedCoppens’smethod,anentropy-based method, and a variogram fractal-dimension method.Thetechniquesarebasedonthefactthatthetransitionbetweennoiseandnoiseplussignalcanbeautomaticallyidentified by detecting rapid changes in a certain attribute energy ratio, entropy, or fractal dimension, which we calculate withinmovingwindowsalongtheseismictrace.Theapplication of appropriate edge-preserving smoothing operators to enhancethesetransitionsallowedustodevelopanautomated strategy that can be used to easily signal the precise location ofthefirst-arrivalonset.Furthermore,weproposeamispickcorrectingtechniquetoexploitthebenefitsofthedatapresent in the entire shot record, which allows us to adjust the traceby-trace picks and to discard picks associated with bad or dead traces. As a result, the consistency of the first-break picks is significantly improved. The methods are robust under noisy conditions, computationally efficient, and easy to apply. Results using dynamite and vibroseis field data show that accurate and consistent picks can be obtained in an automated manner even under the presence of correlated noise, badtraces,pulsechanges,andindistinctfirstbreaks.

Abstract: A new algorithm is proposed for the automatic picking of seismic first arrivals that detects the presence of a signal by analyzing the variation in fractal dimension along the trace. The “divider-method” is found to be the most suitable method for calculating the fractal dimension. A change in dimension is found to occur close to the transition from noise to signal plus noise, that is the first arrival. The nature of this change varies from trace to trace, but a detectable change is always found to occur. The algorithm has been tested on real data sets with varying S/N ratios and the results compared to those obtained using previously published algorithms. With an appropriate tuning of its parameters, the fractal-based algorithm proved more accurate than all these other algorithms, especially in the presence of significant noise. The fractal method proved able to tolerate noise up to 80% of the average signal amplitude. However, the fractal-based algorithm is considerably slower than the other methods and hence is intended for use only on data sets with low S/N ratios.

Abstract: A back-propagation neural network is successfully applied to pick first arrivals (first breaks) in a background of noise. Network output is a decision whether each half-cycle on the trace is a first or not. 3D plots of the input attributes allow evaluation of the attributes for use in a neural network. Clustering and separation of first break from non-break data on the plots indicate that a neural network solution is possible, and therefore the attributes are suitable as network input. Application of the trained network to actual seismic data (Vibroseis and Poulter sources) demonstrates successful automated first-break selection for the following four attributes used as neural network input: (1) peak amplitude of a half-cycle; (2) amplitude difference between the peak value of the half-cycle and the previous (or following) half-cycle; (3) rms amplitude ratio for a data window (0.3 s) before and after the half-cycle; (4) rms amplitude ratio for a data window (0.06 s) on adjacent traces. The contribution of the attributes based on adjacent traces (4) was considered significant and future work will emphasize this aspect.