Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

TL;DR

This paper introduces a multi-information fusion deep learning network that transforms velocity picking into a semantic segmentation task, effectively estimating seismic velocities even in low SNR conditions by combining velocity spectra and stack gather segments.

Contribution

The paper presents a novel multi-information fusion network that integrates velocity spectra and stack gather segments for improved automatic velocity picking, especially in low SNR scenarios.

Findings

Accurately estimates velocities in medium and high SNR data.

Performs well in low SNR conditions, demonstrating robustness.

Provides stable and precise velocity picking results across datasets.

Abstract

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in the situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios.