Identification and travel time detection method for complex surface first arrivals based on high-dimensional multi-attribute decision-making process

The wave theory first-arrival travel time tomographic velocity inversion and modeling has become a key step in onshore seismic wave imaging.With the gradual popularization of "wide-azimuth, high-density, and broadband" seismic data acquisition technology, it is no longer feasible to manually identify the first break and travel time in massive data.Oil and gas exploration in complex surface areas is becoming more and more routine.Together with the emergence of various machine learning algorithms, these factors promote the need to further explore high-precision, first arrival identification, and travel time detection methods and technologies.To resolve the problem of weak first arrivals buried in strong noise, the present study proposes a set of intelligent automatic processing procedures for first-arrival identification and travel time detection in complex surface areas.The main steps include preprocessing related to first-arrivals during shot collection, construction of high-dimensional feature space, division of first arrival distribution area by multi-attribute weighted K-means clustering, and detection of first-arrival travel time by the multi-attribute-constrained Markov decision process (MDP).Under the framework of MDP theory, first, a reasonable base plane is selected to eliminate the jumping time difference between traces.Then, considering the first arrival wave field in the three-dimensional (or two-dimensional) shot set as a whole, high-dimensional feature attributes are extracted, and multi-attribute weighted K-means clustering is used to divide the distribution area of the first arrivals and narrow the picking range.Finally, the multi-attribute constraints are used to construct the Markov optimal evolution algorithm, which fully considers the horizontal continuity in the information of the first arrivals.The actual data test results indicates that the method can automatically pick up the first arrival travel time with high accuracy and robustness.The proposed method has a good practical effect in the case of moderate complexity.