First Break Picking Seismic

[Heartbreak Writhe]

Inseismology, first-break picking is the detecting or picking the onset arrivals of refracted signals from all the signals received by receiver arrays and produced by a particular source signal generation. It is also called first arrival picking or first break detection. First-break picking can be done automatically, manually or as a combination of both. With the development of computer science and the size of seismic surveys, automatic picking is often preferred.[1]

First-break picks associated with the refracted arrival times are used in an inversion scheme to study the near-surface low-velocity zone and subsequent determination of static corrections.Static correction is a correction applied to geophysical data, especially seismic data, to compensate for the effect of near-surface irregularities, differences in the elevation of shots and geophones, or any application to correct the positions of source and receivers.

Michael D. McCormark et al.[4](1993) introduced a backpropagation neural network (BNN) method. The Neural network which edits seismic data or pick first breaks was trained by users, who were just selecting and presenting to the network examples of trace edits or refraction picks. The network then changes internal weights iteratively until it can reproduce the examples accurately provided by the users.

Fabio Boschetti et al.[5](1996) introduce a fractal-based algorithm, which detects the presence of a signal by analyzing the variation in fractal dimension along the trace. This method works when signal-to-noise ratio is small, but it is considerably slow.

A direct correlation method was introduced by Joseph et al.[6](1999) which was developed for use in highly time-resolved, low-noise signals acquired in the laboratory. In this method, the greatest value of Pearson's correlation coefficient between segments of observed waveforms near the pulse onset and at an appropriate reference serves as the time determination criterion.

Zuolin Chen, et al.[7](2005) introduced a multi-window algorithm to detect the first break. In this method, three moving windows were used and the averages of absolute amplitudes in each window need to be calculated, then ratios based on the averages of the windows provide standards to differentiate signals from unwanted noise.

Wong et al.[8](2009) introduced STA/LTA ratio method. This method is similar as Coppens'[3] algorithm. The difference is to do the ratio of two averages of energy between a short-term window and a long-term window, which is denoted as STA/LTA (short-term average/long-term average), instead of calculating the ratio of energy of seismogram of the two windows in Coppens' algorithm.

This method is similar as Coppens' (1985) algorithm. The difference is to do the ratio of two averages of energy between a short-term window and a long-term window, which is denoted as STA/LTA (short-term average/long-term average), instead of calculating the ratio of energy of seismogram of the two windows in Coppens' algorithm. The numerical derivative of the ratio can be defined as,

When the instantaneous absolute amplitude exceeds an automatically adjusted threshold, ratios based on the averages of the windows over previous time samples provide standards to differentiate signals from unwanted noise.

3. H1(t) is defined larger than most pre-existing noise levels, and the instantaneous absolute amplitude at the trigger time point is higher than H1(t), according to the configuration of the first arrival of an event the real onset time must be earlier than the trigger time point. A waveform correction should be used to compensate this belated onset time. For an impulsive first arrival, the height of the absolute amplitude and the representative gradient at the trigger point can be used to accomplish the correction.

Potash SU is a package including Seismic Unix style codes developed by Balazs Nemeth, it provides a subroutine called simple window-based first break picker, the figure shows the seismic images before and after the application of subroutine.

Methods of Picking: automatic first-break picking has played an important role in seismic data processing, and directly influences the quality of seismic sections. Because of the increase of seismic survey size, more efficient and fast first break picking methods are needed, with parallel methods being preferred.

First-break picking can be done automatically, interactively, manually, or as a combination thereof. To make reliable picks, first apply linear moveout (LMO) to the data. Once picking is done, the LMO correction is reversed. Note that effectiveness of both reflection- and refraction-based methods of statics corrections depends on the reliability of the picking process. Apart from the signal-to-noise ratio, indistinct first breaks (such as in vibroseis) sometimes can make picking consistent first breaks difficult.

The first-break picks associated with the refracted arrival times are then used in an inversion scheme to estimate the near-surface model parameters. In this section, we discuss ray-theoretical methods such as plus-minus and its generalized form, the reciprocal method, and the least-squares inversion methods. The basic assumption made is that the refractor is flat or nearly flat, with a smoothly varying shape along the seismic profile. As demonstrated by the field data examples, these methods appear to remove medium- to long-wavelength statics anomalies associated with various types of near-surface models. Combined with the reflection-based residual statics corrections to resolve any remaining short-wavelength statics variations that affect the stack quality, we get a final stacked section ready for poststack processing.

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Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Abstract: Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high. Keywords: first-break picking; large-offset seismic data; deep learning; convolutional neural network; tomography images

The manual detection of the first break (FB) in seismic data relies on visual recognition of amplitude and waveform variations by experts. However, for vast datasets generated during seismic acquisitions, this manual process becomes highly time-consuming and subject to individual interpretation. In this study, we propose an automated FB picking method based on neural networks for detecting the initial arrival of synthetic data resembling the characteristics of the Middle Magdalena Valley in Colombia, a basin historically associated with hydrocarbon exploration. Our approach involves supervised training of a neural network (NN) comprising a 1D convolutional layer and two dense layers. The NN categorizes reprocessed trace samples into two groups: pre-FB and post-FB. Subsequently, through post-processing, we identify the most likely sample corresponding to the FB. Our method successfully detects the first arrival in 77.36%, 91.19%, and 95.86% of cases, allowing for a margin of error of 10 samples when signal-to-noise ratios (SNRs) are 0 dB, 6 dB, and 20 dB, respectively. This demonstrates its effectiveness, particularly for noisy signal conditions.

Current methods of automated first break picking used in seismic data processing software are mostly based on the unsupervised algorithms. Due to the variety of the near-surface structures, the behavior of the signal in the regions of first arrivals may vary from shot to shot. This results in manual parameters selection for different seismic exploration regions. As a result, it takes a lot of time and effort of a seismologist to properly complete this stage. In this work to overcome this problem, we propose a supervised method based on the popular nowadays convolutional neural network. Such approach, however, raises another challenge - CNNs require a large amount of well-labeled data.

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