It can be challenging to pick high-quality first arrivals on noisy seismic data sets. The stability and smoothness criteria of the picked first arrival are not satisfied for data sets with shingles and interference from unexpected and backscattered events. To improve first-arrival picking, we have adopted an automatic first-arrival picking workflow using global path tracing (GPT) to find a global solution for first-arrival picking with the condition of smoothness of the traced path. Our methodology is composed of data preconditioning, GPT, and a final addition of traced and piloted traveltimes to compute the total picked traveltime. We propose several ways to precondition the data set, including the use of the amplitude and amplitude ratio with and without a pilot. A 2D GPT is composed of two steps, namely, accumulation of energy on the potential path and backtracking of the optimal path with a strain factor for smoothness. For higher dimensional data sets, two strategies were adopted. One was to split the higher dimension data into subdomains of two dimensions to which 2D GPT was applied. The alternative method was to smooth the preconditioned data set in directions except for the one used to trace the path before applying 2D GPT. We discuss the importance of choosing the proper parameters for data preconditioning and for constraining GPT. We demonstrate the robustness and stability of our automatic first-arrival picking via GPT using synthetic and field data examples.

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