Break Picking Research Articles

UPNet: Uncertainty-Based Picking Deep Learning Network for Robust First Break Picking

UPNet: Uncertainty-Based Picking Deep Learning Network for Robust First Break Picking

3-D Seismic First Break Picking Based on Two-Channel Mask Strategy

3-D Seismic First Break Picking Based on Two-Channel Mask Strategy

First Break Traveltime Picking Using Unsupervised Machine Learning and Super‐Virtual Refraction Interferometry

AbstractAn accurate and trustworthy first break picking plays a key role in static correction calculations, velocity analysis, and deconvolution. First break traveltimes picking accuracy ensures correct and reliable seismic data processing results. Many methods are available to perform the first break pickings automatically. Most of these methods, however, are not robust when there is a low signal‐to‐noise ratio (SNR). In this study, to automatically determine the traveltimes of the first arrivals, we used the density‐based spatial clustering application with noise (DBSCAN) and the super‐virtual refraction interferometry (SVI). In the case of noisy data, using only DBSCAN for first break picking shows poor accuracy. To deal with this issue, SVI is employed to improve the SNR of the data, and then DBSCAN is applied again to the enhanced data for accurate picking. In the first step, DBSCAN is used to define a muting window for SVI. In the second step, SVI is employed to improve the first arrival SNR. In the third step, DBSCAN is applied to determine the final first arrival traveltime. The proposed approach is tested on synthetic and field data sets, where the manual and the cross‐correlation pickings are compared to the DBSCAN automatic pickings. We further test the proposed approach on a publicly available data set to benchmark our technique for first‐break accuracy with other established methods. Results show that the first arrival traveltime picks using the proposed approach are very accurate compared to manual picking.

Investigation of near-surface structures using seismic refraction and multi-channel analysis of surface waves methods—a case study of the University of Lagos main campus

The need to use locations which are hitherto swamps or recreation centers to construct more building facilities that range from medium to huge structures within the University main campus informed the decision to embark on this study. Structures within the subsurface layers were investigated by analyzing the distribution pattern of some geophysical properties such as compressional wave (P-wave) velocity, shear wave (S-wave) velocity, and Poisson ratio. The near-surface layers were identified from the first break picks of P-waves velocities. Moreover, the dispersive capability of the surface wave produced S-wave velocity information that identified different geological structures based on the orientations of the lithological layer’s rigidity. The P-wave velocities reveal three-layer structures of thicknesses between 3.0 m to 6.0 m and 2.0 m to 5.0 m for Layer 1 and Layer 2 respectively. These layers are in three categories of “Complex”, “Mild,” and “Simple” based on the distribution pattern of shear velocities. Poisson ratio analysis shows two categories of Topsoil/Lateritic layer of less than 2.0 m (thin) and between 2.0 m and 4.5 m (thick). Areas identified as “Complex” structure with thick Topsoil will require comprehensive foundation design than other areas especially when erecting huge building. Whereas, locations around the New Halls (NHs) may require simple foundation plan for building development compared to other areas since it is characterized by thin Topsoil and simple layer structure. The study will assist construction engineers with prior information of the structures of subsurface layers which thereby reduce time and cost of building development within the University campus.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.

Neuron curve as a tool for performance evaluation of MLP and RBF architecture in first break picking of seismic data

Abstract Recently wide applications of neural networks are reported in geophysical scientific papers, mostly lack the consideration of their mathematical evaluation and performance. In these general estimators/regression function/classifiers, parameters to be tuned are the number of layers, neurons, type of transfer function, minimum size of training set, etc. These will be carefully tuned per each physical problem. Among all, the number of hidden layers and the number of neurons in each hidden layer are the two important parameters to be decided and normally no rules are available for finding them precisely. In this paper a method to find the hidden layer size is described beside the main purpose of the paper which is to compare the performance of the first break picker networks. We used a known learning-curve and introduce a measure named “neuron-curve” to find the optimal layer size & minimum size of training set. This paper shows the application of these two curves in finding the first break picks of seismic refraction data. Furthermore, the effect of noise on the architecture of two known neural networks (multilayer perceptron and radial basis function) in the first break picking is also investigated.

Tau-p velocity imaging of regolith structure

The tau-p velocity imaging method, first developed for obtaining the velocity field from marine multichannel seismic data, has been applied to refracted waves from the regolith in regional seismic reflection surveys on land. The technique converts travel time picks from the refracted wavefield into two-dimensional velocity models, by transforming from time-offset into the tau-p domain. Each arrival is mapped individually, and the ?true? velocity and position of the ray turning point is obtained by considering reversed raypaths. Thus the data are transformed directly into a depth or two-way time image of the subsurface displayed in seismic velocity. The method is extremely fast and involves no interpretive steps or iteration. Ideal datasets contain the refracted wavefield sampled densely and equally in the shot and receiver domains. It was therefore decided to test the application of the method for mapping the velocity structure of a portion of regolith and to compare the results with those obtained using more conventional methods. The area chosen for study was part of a regional seismic reflection line across the Lachlan River palaeo-valley in central NSW. The data set consisted of the first break picks for 240 channels with receivers spaced every 40 m and vibration points every 40 m. The velocity images were produced as both time and depth sections and compared with one and two layer refractor models from a refraction tomographic approach. A low velocity region on the image corresponds to the deepest part of the refractor model, interpreted as the thickest part of the palaeo-valley. Bedrock velocity variations are also mapped and agree with the changes along the (lowest) refractor velocity profile. While further tuning may be required for land work, the technique has the advantage that velocities can be directly imaged and potentially related to regolith physical properties.

ON TIMING THE VSP FIRST ARRIVAL*

AbstractThe advent of signal energy on a VSP or check‐shot trace may be defined as the first break. An accurate pick of this first break would be possible in the absence of noise. However, real data traces are inevitably corrupted by noise and this leads to difficulty in identifying a break because the signal‐to‐noise ratio is low in its neighbourhood.Under such conditions, an obvious alternative is to pick “troughs” where the local signal‐to‐noise ratio is likely to be much higher. Although trough picking is an effective way to minimize the noise problem, it is sensitive to signal properties (such as absorption and multiple reflections) which have no effect upon the accuracy of break picks. Thus, trough picking is signal‐sensitive and break picking is noise‐sensitive.Clearly, an ideal first‐arrival picking scheme would combine the noise‐tolerant features of trough picking with the signal‐tolerant features of break picking. This ideal may be approached by exploiting known properties of the VSP trace using conventional signal processing techniques. The result of such processing is to reduce the problem to that of picking a trough correctly centered about the true break time.