  With the anticipated launch of space-based gravitational wave detectors,
including LISA, TaiJi, TianQin, and DECIGO, expected around 2030, the detection
of gravitational waves generated by intermediate-mass black hole binaries
(IMBBHs) becomes a tangible prospect. However, due to the detector's reception
of a substantial amount of non-Gaussian, non-stationary data, employing
traditional Bayesian inference methods for parameter estimation would result in
significant resource demands and limitations in the waveform template library.
Therefore, in this paper, we simulated foreground noise induced by
stellar-origin binary black holes (SOBBHs), which is non-Gaussian and
non-stationary, and we explore the use of Gaussian process regression (GPR) and
deep learning for parameter estimation of Intermediate Mass Binary Black Holes
(IMBBHs) in the presence of such non-Gaussian, non-stationary background noise.
By comparing these results from deep learning and GPR, we demonstrate that deep
learning can offer improved precision in parameter estimation compared to
traditional GPR. Furthermore, compared to GPR, deep learning can provide
posterior distributions of the sample parameters faster.
