A new automatic first arrival picking algorithm based on a mathematical approach with - PowerPoint Presentation

1. A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz Razi university of Kermanshah, Kermanshah, IranDepartment of Geology, Urmia University, Urmia, IranP3.5-114

2. A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com P3.5-114

3. P3.5-114ABSTRACT A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com This research demonstrates a new accurate automated method for seismic first arrival picking that is based on a mathematical approach with considering the fractal dimension of seismic traces. In this work, we introduce an adaptive mathematical triggering algorithm (based on comparing the length of consecutive data with a variable step size “r”) by considering the fractal dimension variations along the seismic records. The results show that our proposed algorithm is quite reliable and it is less susceptible to false-positive detection errors. Our proposed algorithm was verified using seismic records and synthesized seismic records with different noise levels. Also, we showed the performance and the results of the mathematical fractal dimension method on seismic records. The results emphasize that our proposed algorithm is quite practical and reliable for noisy and bad seismic records, and as well as, it is computationally efficient and easy to apply.

4. P3.5-114INTRODUCTION A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com The goal of any seismic detection method is to determine the accurate time of begin and end of phases. As we know, in seismology, first arrival picking is the first step in seismic data processing. So, the accuracy of the first arrival detection is very important. On the other hand, the manual picking method (first arrival determination was carried out by visual inspection of fluctuations in the waveform) can be useful. However, for the early warning approaches or when the numbers of seismic traces increased, to consuming the time and money, an automated and effective first arrival picking method is required.Several automated first arrival picking methods have been proposed and applied in the last few decades. However, used methods for detecting P-wave arrivals are based on the change of the intrinsic property characteristics of the measured signal, waveform similarity, including amplitude characteristic, frequency component characteristic, and dynamic characteristic. In this research, we introduced a novel method to automatic first arrival (P phase) picking based on a mathematical approach by considering the fractal dimension variations along the seismic records.

5. P3.5-114METHODS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com In the proposed algorithm, the P phase picking is based on comparing the length of consecutive data with a variable step size “r” of transformed amplitudes and considering the rapid changes in fractal dimension along the seismic traces.Indeed, the aim of our algorithm is to distinguish the first breaks in background noises.Initially, we calculate the length of consecutive data (), and the step size () as:Then we compare them till: 

6. P3.5-114METHODS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com Also, to enhance the performance of our first arrival picking method and overcome the problem of working with noisy seismic signals, we have considered the fractal dimension variation along the seismic waveforms. In this approach, the divider method (Mandelbrot and Pignoni, 1983) has been used to calculate the fractal dimension. The basic implementation of this method is for a vibration curve, a straight line segment “step size r” is selected to measure the curve from the start to the end. By completing this process, the number of the measurements is recorded with the step size N, and the length of the vibration curve equals the product of N and r approximately. By considering a series of step sizes , the corresponding can be obtained, and the lengths can be calculated approximately by:  A series of and is obtained and a Mandelbrot–Richardson (M-R) plot can be obtained. So, the fractal dimension (D) can be measured from the slope of M-R plot (S).  Figure : a shows four step sizes (r1, r2, r3, and r4) to calculate the length of the signal (L) for the fractal dimension measure in the first step

7. P3.5-114METHODS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com Therefore, to calculate the fractal dimension of a seismic trace, we used a moving time window to scan the trace from the start to the end along the time axis. While moving the time window, the fractal dimension of the waveform in each window can be calculated and recorded at the corresponding time t. As we can see the first arrival occurred in the jumping section. Figure: a) moving time window (A) on the seismic trace to calculate the fractal dimension. b) The calculated fractal dimension. The blue window shows the jumping section in fractal dimension.

8. P3.5-114RESULTS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com To investigate the performance of our algorithm for detecting first arrivals in seismic traces, a seismic traces with a sampling frequency of 100 Hz was chosen. This approach was selected to determine the first arrivals due to its simplicity, fast speed, and high accuracy. we performed our method on a seismic trace, to investigate the features and details of the results.

9. P3.5-114RESULTS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com Furthermore, to show the accuracy of our algorithm, we added different levels of noise to the seismic trace, and then first arrival detected. In this procedure, Gaussian white noise was added to real seismic traces and the synthesized traces were produced.

10. P3.5-114CONCLUSIONS A new automatic first arrival picking algorithm based on a mathematical approach with considering the fractal dimensionShamseddin Esmaeili, Hadi Esmaeili, Ramin Nikrouz, s.esmaeili@razi.ac.ir, h.esmaeili2003@gmail.com In this research, we introduced a new automatic first arrival picking method based on a mathematical approach with considering the fractal dimension variations along the seismic trace. This method is based on the analysis of certain trace attribute. In this approach, we compared the length of consecutive data with a variable step size “r” of transformed amplitudes, and then considered the rapid changes in fractal dimension along the seismic traces. The first arrival occurred in the rapid changing in the calculated lengths and a jumping section in fractal dimension of seismic traceWe performed our algorithm on a seismic trace (original and synthesized trace with adding white noise). The results showed that our proposed algorithm is quite reliable to determine the first arrivals in seismic traces. Our method is robust for seismic traces with low S/N and detects accurate picks even under the correlated noise, bad traces, and indistinct first breaks. Furthermore, this algorithm is computationally efficient and easy to apply. In general, we have observed that considering the variations of calculated lengths with fractal dimension along the seismic traces together is very effective for picking first arrivals.