Two-dimensional inversion of DC resistivity based on supervised descent method

To address the issues of inadequate prior information utilization and low computational efficiency for quasi-linear inversion with large amounts of data, this paper utilizes supervised descent method (SDM) for two-dimensional inversion of DC resistivity. SDM involves two stages: offline training and online prediction, with the training set consisting of models generated from prior information and simulation data. In the training process, the direction of descent (from the initial model to the trained model) was learned, and in the prediction process, both the learned direction of descent and the calculated residuals were taken into account. Through synthesis data tests, we present a discussion on the inversion accuracy, convergence speed, anti-noise robustness, and generalization performance of the proposed SDM. Online prediction results show that, with an inversion data error of 0. 0037, using the modular training set of mixed models (i. e., block and hierarchical structure) can significantly improve the generalization performance of SDM. For field-data inversion cases, the training set is constructed using the resistivity results of the measured data, and the quality and integrity of the model data in the training set can be optimized. Comparing with the Gaussian Newton method, we also discussed the inversion accuracy and efficiency of SDM for batch measured data. The results demonstrate that the inversion time for a single data is longer than the Newton method, yet the prediction process takes less than 0. 5 s after the training process is completed, indicating that the proposed SDM has higher inversion efficiency for large batches of data with the same type, dimension, and size.