Seismic horizon extraction with dynamic programming

Abstract

Horizon picking is a fundamental and crucial step for seismic interpretation, but it remains a time-consuming task. Although various automatic methods have been developed to extract horizons in seismic images, most of them may fail to pick horizons across discontinuities such as faults and noise. To obtain more accurate horizons, we propose a dynamic programming algorithm to efficiently refine manually or automatically extracted horizons so that they can more accurately track reflectors across discontinuities, follow consistent phases, and reveal more geologic details. In this method, we first compute an initial horizon using an automatic method, manual picking, or interpolation with several control points. The initial horizon may not be accurate and only needs to follow the general trend of the target horizon. Then, we extract a sub-volume of amplitudes centered at the initial horizon and meanwhile flatten the sub-volume according to the initial horizon. We finally use the dynamic programming to efficiently pick the globally optimal path that passes through global maximum or minimum amplitudes in the sub-volume. As a result, we are able to refine the initial horizon to a more accurate horizon that follows consistent amplitude peaks, troughs, or zero-crossings. As our method does not strictly depend on the initial horizon, we prefer to directly interpolate an initial horizon from a limited number of control points, which is computationally more efficient than automatically or manually picking an initial horizon. In addition, our method is convenient to be interactively implemented to update the horizon while editing or moving the control points. More importantly, these control points are not required to be exactly placed on the target horizon, which makes the human interaction highly convenient and efficient. We demonstrate our method with multiple 2D and 3D field examples that are complicated by noise, faults, and salt bodies.

A novel automatic source point deviating method based on dynamic programming

“…Along with the complexity of modelling a seismic system, such as ray tracing, dynamic programming has been applied widely in seismic prospecting. Because of the discontinuities of horizons in seismic images, S. Yan and Wu (2021) proposed to apply dynamic programming to extract horizons more accurately, and S. Yan et al (2022) infilled gaps and refined the vertical positions of horizons to model subsurface by applying dynamic programming. Besides, this method has been applied to develop an alternative three-dimensional traveltime computation method in orthorhombic media (J.…”

“…In order to reduce the workload of manpower, economic expenses and subjective bias, it is urgent to develop an automatic approach to offset the source points originally arranged in the obstacle area to other positions. With the great success of various optimization technologies achieved in many application fields (Mattia, 2019; S. Yan & Wu, 2021), related technologies have great potential to efficiently solve the problem of source point offset. In this paper, we will present an automatic source point offset method based on dynamic programming.…”

Deep Learning for Efficient Microseismic Location Using Source Migration‐Based Imaging

“…In this study, we use deep learning to efficiently compute a better-focused source from migration image for microseismic location during hydraulic fracturing. The U-Net is firstly proposed for cell image segmentation in life science by Ronneberger et al (2015) and has been widely used in many aspects of geophysics (Li et al, 2021;Wu et al, 2020;Yan & Wu, 2021). Due to the good performance of the U-Net on feature extraction, we use a 3D U-Net architecture modified from the widely used 2D U-Net to deal with the unfocused problem of migration imaging, and the network can also be regarded as a kind of imaging condition.…”