A high accurate automated first‐break picking method for seismic records from high‐density acquisition in areas with a complex surface

ABSTRACT As the application of high‐density high‐efficiency acquisition technology becomes more and more wide, the areas with complex surface conditions gradually become target exploration areas, and the first‐break picking work of massive low signal‐to‐noise ratio data is a big challenge. The traditional method spends a lot of manpower and time to interactively pick first breaks, a large amount of interactive work affects the accuracy and efficiency of picking. In order to overcome the shortcoming that traditional methods have weak anti‐noise to low signal‐to‐noise ratio primary wave, this paper proposes a high accurate automated first‐break picking method for low signal‐to‐noise ratio primary wave from high‐density acquisition in areas with a complex surface. Firstly, this method determines first‐break time window using multi‐azimuth spatial interpolation technology; then it uses the improved clustering algorithm to initially pick first breaks and then perform multi‐angle comprehensive quality evaluation to first breaks according to the following sequence: ‘single trace → spread → single shot → multiple shots’ to identify the abnormal first breaks; finally it determines the optimal path through the constructed evaluation function and using the ant colony algorithm to correct abnormal first breaks. Multi‐azimuth time window spatial interpolation technology provides the base for accurately picking first‐break time; the clustering algorithm can effectively improve the picking accuracy rate of low signal‐to‐noise ratio primary waves; the multi‐angle comprehensive quality evaluation can accurately and effectively eliminate abnormal first breaks; the ant colony algorithm can effectively improve the correction quality of low signal‐to‐noise ratio abnormal first breaks. By example analysis and comparing with the commonly used Akaike Information Criterion method, the automated first‐break picking theory and technology studied in this paper has high picking accuracy and the ability to stably process low signal‐to‐noise ratio seismic data, has a significant effect on seismic records from high‐density acquisition in areas with a complex surface and can meet the requirements of accuracy and efficiency for massive data near‐surface modelling and statics calculation.

[1]  Nan Zhang,et al.  Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data , 2016, Memetic Computing.

[2]  Jihun Choi,et al.  Improved modified energy ratio method using a multi-window approach for accurate arrival picking , 2017 .

[3]  Vivienne Sze,et al.  Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.

[4]  Tianyue Hu,et al.  Automatic first-arrival picking based on extended super-virtual interferometry with quality control procedure , 2017 .

[5]  P. J. Hatherly.  A computer method for determining seismic first arrival times , 1982 .

[6]  Yangkang Chen,et al.  Automatic microseismic event picking via unsupervised machine learning , 2020, Geophysical Journal International.

[7]  Shifei Ding,et al.  Research on Point-wise Gated Deep Networks , 2017, Appl. Soft Comput..

[8]  Shuqian Dong,et al.  Theory and Practice of Refraction Interferometry , 2006 .

[9]  Wooil M. Moon,et al.  Detection of seismic refraction signals using a variance fractal dimension technique , 2000 .

[10]  G. Schuster,et al.  Iterative supervirtual refraction interferometry , 2014 .

[11]  Nathan Intrator,et al.  Classification of seismic signals by integrating ensembles of neural networks , 1998, IEEE Trans. Signal Process..

[12]  G. Schuster,et al.  Enhanced refractor imaging by supervirtual interferometry , 2011 .

[13]  Gerard T. Schuster,et al.  Theory of supervirtual refraction interferometry , 2012 .

[14]  Patrick Garda,et al.  A Neuro-fuzzy Coding for Processing Incomplete Data: Application to the Classification of Seismic Events , 2004, Neural Processing Letters.

[15]  Michael E. Murat,et al.  AUTOMATED FIRST ARRIVAL PICKING: A NEURAL NETWORK APPROACH1 , 1992 .

[16]  A. Clement,et al.  DIGITAL PROCESSING OF REFRACTION DATA STUDY OF FIRST ARRIVALS , 1972 .

[17]  Peter M. Shearer,et al.  Characterization of global seismograms using an automatic-picking algorithm , 1994, Bulletin of the Seismological Society of America.

[18]  Shifei Ding,et al.  An overview on Restricted Boltzmann Machines , 2018, Neurocomputing.

[19]  Alberto Signoroni,et al.  Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging , 2017, Pattern Recognit..

[20]  B. Gelchinsky,et al.  AUTOMATIC PICKING OF FIRST ARRIVALS AND PARAMETERIZATION OF TRAVELTIME CURVES , 1983 .

[21]  Chao Zhang,et al.  Automatic Time Picking for Microseismic Data Based on a Fuzzy C-Means Clustering Algorithm , 2016, IEEE Geoscience and Remote Sensing Letters.

[22]  Manfred Baer,et al.  An automatic phase picker for local and teleseismic events , 1987 .

[23]  Zhu Han,et al.  First-break automatic picking with deep semisupervised learning neural network , 2018, SEG Technical Program Expanded Abstracts 2018.

[24]  Salvatore Barba,et al.  Seismic signal detection by fractal dimension analysis , 1999 .

[25]  Danilo R. Velis,et al.  Automatic first-breaks picking: New strategies and algorithms , 2010 .

[26]  Min Bai,et al.  Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network , 2019, Earth and Space Science.

[27]  F. Coppens,et al.  First arrival picking on common-offset trace collections for automatic estimation of static corrections , 1985 .

[28]  Khalid Amin Khan.  Artificial Intelligence Based Automated First Break Picking And Quality Control , 2007 .

[29]  P. Stevenson,et al.  Microearthquakes at Flathead Lake, Montana: A study using automatic earthquake processing , 1976, Bulletin of the Seismological Society of America.

[30]  Michaël Aupetit,et al.  How to help seismic analysts to verify the French seismic bulletin? , 2006, Eng. Appl. Artif. Intell..

[31]  F. Boschetti,et al.  A fractal-based algorithm for detecting first arrivals on seismic traces , 1996 .

[32]  Danilo R. Velis,et al.  A robust method for microseismic event detection based on automatic phase pickers , 2013 .

[33]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Sen Liu,et al.  Integrating seismic first-break picking methods with a machine learning approach , 2018, SEG Technical Program Expanded Abstracts 2018.

[35]  Dave Marsden,et al.  Static corrections—a review, Part III , 1993 .

[36]  R. V. Allen,et al.  Automatic earthquake recognition and timing from single traces , 1978, Bulletin of the Seismological Society of America.