# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 1 Introduction  
Seismic data processing methods play crucial roles in the exploration of oil and gas resources. The results of seismic data processing provide a foundation for subsequent tasks, such as hydrocarbon reservoir identification and seismic inversion [1, 2]. Among these techniques, seismic imaging is particularly significant because it produces high-resolution images of the true reflectivity of subsurface structures. As such, it is regarded as one of the most effective methods for mapping underground structures and properties using seismic data [3, 4]. Traditional seismic migration methods, such as ray-based migration and one-way wave equation migration, typically depend on approximate solutions to the wave equation. These methods often perform inadequately when dealing with complex geological structures and may even exhibit instability [5]. By contrast, reverse-time migration (RTM) is a migration method based on the two-way wave equation. The advantages of the two-way wave equation in accuracy and reliability allow waves to propagate in all directions, making them less constrained by the velocity model and propagation direction [6]. Compared to traditional migration methods, RTM is capable of accurately handling complex geologicalstructures, including steeply dipping layers, and is theoretically the most precise among all seismic migration techniques [7; 8; 9]. However, the quality of an RTM image is heavily influenced by both the quality of the seismic data and the imaging method used. A good standard for evaluating seismic imaging methods is fidelity, which is the ability to map the target features to their correct locations [10]. Despite significant advancements in seismic data acquisition technology in recent years, the quality of seismic data remains susceptible to various types of noise. This challenge is particularly pronounced in academic research, where resources are more limited compared to industrial applications [11; 12]. Therefore, it is essential to preprocess raw seismic data to meet the requirements of subsequent migration imaging. [PERSON] et al. proposed using singular value decomposition (SVD) filtering to attenuate direct and refracted waves in seismic records, thereby highlighting the shallow reflection wavefield [13]. [PERSON] [14] utilized f-x domain predictive filtering to suppress random noise in seismic data. Another effective noise suppression technique is median filtering, which can efficiently remove salt-and-pepper noise present in seismic data [15]. Besides improving seismic data quality, there is also substantial research focused on optimizing imaging methods to enhance the quality of seismic images. For instance, separating the upgoing and downgoing waves in a wavefield can reduce the low-frequency noise in the imaging results [16; 17]. [PERSON] [18] separated P-waves and S-waves in elastic wavefields. [PERSON] [19] proposed a reconstructed wavefield RTM algorithm using a backward-reproduced wavefield instead of back-propagating the observed data to achieve higher-quality imaging results. [PERSON] and [PERSON] [6] effectively reduced artifacts in RTM by combining de-primary RTM algorithms and impedance matching, resulting in clearer imaging outcomes. [PERSON] et al. introduced a joint RTM (JRTM) method that improves the quality of the imaging results by simultaneously using both upgoing and downgoing waves [20]. In the RTM process, after obtaining the wavefield, different imaging conditions (ICs) are applied to the image space to generate a migrated image of the subsurface structures. The choice of the IC significantly influences the final quality and fidelity of the RTM image. The initial ICs were divided into excitation time and cross-correlation imaging conditions. The excitation time imaging condition does not require the storage of the entire wavefield but requires computation of the excitation time [21]. This excitation time is typically the first-arrival time or the time of maximum amplitude and can also be calculated using ray-tracing methods [22; 23].