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

Huailiang Li,Xianguo Tuo,Ruili Wang,Tong Shen,Jérémie Courtois,Minhao Yan

doi:10.3390/s17092150

Abstract

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.

Highlights

First break picking is a fundamental, but important step, in three-component (3C) vertical seismic profiling (VSP) data processing, such as velocity estimation, wave-field separation, and anisotropy estimates [1]
These approaches have played a critical role in their different periods, respectively, especially the short-term average to long-term average (STA/LTA) and Akaike information criterion (AIC) algorithms are, nowadays, still the most widely used [6,7,8,9]
It is probable that these approaches will not work properly when being directly applied to 3C VSP, due to the fact that the geophone’s layout is arbitrary when the 3C seismic explorations are applied in a borehole, which will cause the horizontal and vertical components of all geophones to change randomly [11,12]

Summary

Introduction

First break picking is a fundamental, but important step, in three-component (3C) vertical seismic profiling (VSP) data processing, such as velocity estimation, wave-field separation, and anisotropy estimates [1]. During the last few decades, various techniques have been developed for determining first breaks automatically or semi-automatically [4], such as automatic methods available that are based on the correlation properties, on some statistical criteria, or on artificial neural networks for both individual and groups of traces [5] These approaches have played a critical role in their different periods, respectively, especially the short-term average to long-term average (STA/LTA) and Akaike information criterion (AIC) algorithms are, nowadays, still the most widely used [6,7,8,9]. The data quality plays a significant role on the effectiveness of any picking algorithm [10] They mainly focus on the condition that all geophones are in a regular state; in other words, their data can be directly used without rotation calibration processing, taking shallow seismic, microseismic, and three-dimension three-component (3D3C) seismic exploration, for instance. The authenticity and accuracy of data calibration is questionable

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Discussion

Conclusion