With the development of computational power,there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image,such as full-waveform inversion and least squares migration.However,though more advanced than conventional methods,these data fitting methods can be very expensive in terms of computational cost.Recently,various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency.In this study,we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems.We first prescribe the basic theory of our method and then give synthetic examples.Our numerical experiments illustrate the potential of this method for large-size seismic inversion application. With the development of computational power, there has been been increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least squares migration. Despite, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost.Recently, various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency. in this study, we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems. We first prescribe the basic theory of our method and then give synthetic examples. Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.