# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 1 Introduction  
Ocean Bottom Node (OBN) acquisition is one of the technologies used in marine seismic surveys. OBN acquisition offers several advantages in contrast to the Towed Streamer (TS), Ocean Bottom Seismometer (OBS) and Ocean Bottom Cable (OBC) acquisition, such as the deployment flexibility of the system and the ability to record a wider azimuth and higher quality of seismic data. In view of the escalating challenges associated with a marine seismic survey and the continuous demand for high-quality seismic data, OBN acquisition has been recently used as an appealing alternative [1, 2]. The OBN acquisition system acquires data from dual sensors (hydrophone and geophone) placed on the seafloor, which record both the pressure and particle motion of the wavefield. The data from the hydrophone are denoted as the P-component, while the three-component geophone records data comprise a vertical component (\(Z\)) and two horizontal components (\(X\) and \(Y\)). A major advantage of multi-component data acquisition is the capability to achieve up- and down-going wavefield separation through combining the hydrophone and vertical geophone, which is one of the effective ways to implement the ghost reflection suppression [3, 4, 5]. These processing steps rely on the assumption that vertical component geophone records the pure pressure wave mode only (while the horizontal component geophone records shear wave) and that the recorded data are noise free. However, in field acquisition, due to the complex seafloor structure and poor coupling of the OBN, the geophone data may be contaminated by a various noises [4, 6, 7]. In particular, the shear wave noise leaked into vertical component degrades the effectiveness of the ghost reflection suppression and is detrimental to further processing steps. As a result, the vertical component shear wave noise attenuation is an essential step in OBN data processing. The aforementioned process of shear wave attenuation is inherently a subfield of the denoising problem. The removal of noise (including random and coherent) is a common and crucial task in seismic data processing, with varying definitions of noise and signal in different scenarios. In OBN data, the pressure wave recorded by the Z-component geophone is regarded as a valid signal and the leaked shear wave (X- and Y-component) in the Z-component as noise. Shear wave exhibits nonrepeatable, incoherent, and random behavior in common shot gathers (CSGs), while they are characterized by high amplitudes and successive events like coherent noise in common receiver gathers (CRGs) [8; 9]. Treating it as coherent noise attenuation, most of the research on OBN data Z-component shear wave removal has been conducted in CRG, as is the case in this paper. Common coherent noises in seismic data include linear noise, surface wave, ground-roll, and multiple wave, etc. [10]. Previous attempts to suppress coherent noise can be roughly classified into filtering- or prediction-based methods. Filtering-based methods utilize the differences in velocity, dip, and frequency of the signal and noise to design filters [11; 12]. These methods are effective when the signal and noise are separable in the transform domain, but they do not work well when dealing with complex field data due to the overlap in the signal and noise [13]. The prediction-based method is to predict the noise and subtract it from the noisy data, also known as matching subtraction. The least-squares adaptive subtraction method introduced by [PERSON] et al. [14] is widely used for multiple removal [15; 16; 17; 18]. Similarly, [PERSON] and [PERSON] [13] used two horizontal component data as the reference signal and applied the adaptive subtraction method on OBC data to suppress the shear noise in the Z-component. [PERSON] and [PERSON] [19] combined the filtering-based method with adaptive subtraction to remove shear wave noise of OBN data. However, when the noise energy is stronger than the signal, the effective signal may be attenuated during the matching subtraction process [20].