###### Abstract
In 1998, [PERSON] introduced a data-driven method for internal multiple suppression [6]. This approach directly employs surface observed seismic data to construct internal multiples, enhancing computational efficiency. However, the method necessitates the accurate selection of primary reflection events from seismic data, which is difficult to implement in complex field data. In 2005, [PERSON] and [PERSON] proposed an internal multiple prediction method based on common focus technology, which is grounded in wavefield extrapolation [7; 8]. This method is better suited to complex geological conditions but relies on a velocity model, limiting its application in field data. In 2006, [PERSON] et al. presented the inverse scattering series method based on the point scattering model [9]. As a data-driven, wave equation-based method, it does not require prior information such as velocity models. However, the method involves substantial computation and is only effective for near offsets, making it insufficient for current data processing demands. In 2013, [PERSON] and [PERSON] introduced an internal multiple suppression method based on sparse inversion [10]. This approach minimizes the damage caused by adaptive matched subtraction to primary signals and effectively protects effective waves. Nevertheless, the method still entails considerable computation and yields unstable wavelets, complicating large-scale field data processing. In 2015, inspired by [PERSON]'s imaging method, [PERSON] et al. proposed a self-focusing-based [PERSON] internal multiple suppression method [11]. This technique does not require accurate velocity models, only an inaccurate macro velocity model to forward direct waves. Subsequently, direct waves and original data are used to construct the up- and down-going Green functions for relevant virtual source points, generating the associated internal multiples. However, this method demands significant computation due to the need for virtual source points at various depths, making it difficult to apply widely to large scale, complex structured seismic data. In 2018, [PERSON] and [PERSON] proposed a one-step internal multiple suppression method based on [PERSON] approach, also known as the data domain internal multiple suppression method [12]. This technique does not require macro velocity models for direct wave estimation and directly outputs primaries without internal multiples. However, this method imposes high requirements on the observation system, and its large-scale application in field data remains limited. In 2020, [PERSON] et al. applied the [PERSON] internal multiple suppression method to field seismic data processing, achieving favorable results [13]. Nonetheless, in low signal-to-noise ratio scenarios, energy may not be sufficiently focused, rendering the [PERSON] internal multiple suppression method ineffective. In 2022, [PERSON] et al. compared the self-focusing-based [PERSON] method for internal multiple suppression with the data domain method [14]. In the same year, [PERSON] et al. introduced a multiple suppression method based on self-attention convolutional auto encoders [15]. This method can reduce artificial cost, reduce dependence on unknown prior information, and improve data processing efficiency. In 2006 and 2009, [PERSON] proposed a virtual seismic event construction method to predict internal multiples [16; 17]. This approach does not require prior information, such as velocity models or subsurface structures, enabling accurate internal multiple predictions. However, this method requires sequential extraction of primaries to construct internal multiples generated by relevant layers, which increase the computational complexity. In 2013, [PERSON] et al. utilized a multi-channel L\({}_{1}\) norm adaptive matching algorithm to suppress multiples, considering the differences in amplitude and phase between virtual event method-predicted internal multiples and actual internal multiples, achieving favorable results in model data [18]. In 2018, [PERSON] et al.