2D data-space cross-gradient joint inversion of MT, gravity and magnetic data

•We presented data-space cross-gradient joint inversion.•Object function is generalized for any sensitivity relationship of data and models.•Iterative solver is effective in determining Lagrange multipliers.•The field example of MT, gravity and magnetic data was presented.•Joint inversion in data space remarkably saves computer memory.

We have developed a data-space multiple cross-gradient joint inversion algorithm, and validated it through synthetic tests and applied it to magnetotelluric (MT), gravity and magnetic datasets acquired along a 95 km profile in Benxi-Ji'an area of northeastern China. To begin, we discuss a generalized cross-gradient joint inversion for multiple datasets and model parameters sets, and formulate it in data space. The Lagrange multiplier required for the structural coupling in the data-space method is determined using an iterative solver to avoid calculation of the inverse matrix in solving the large system of equations. Next, using model-space and data-space methods, we inverted the synthetic data and field data. Based on our result, the joint inversion in data-space not only delineates geological bodies more clearly than the separate inversion, but also yields nearly equal results with the one in model-space while consuming much less memory.