Study of ensemble Kalman filter joint inversion for near-surface resistivity tomography and seismic refraction data

Electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) are two important near-surface geophysical imaging methods. The resistivity is highly sensitive to factors such as water content and porosity, whereas the seismic velocity provides high-resolution imaging of layer interfaces and velocity structures. Due to the strong heterogeneity and multi-scale structural characteristics of near-surface media, as well as environmental noise and human activity interference, a single method is prone to generating multiple solutions and resulting in low imaging accuracy, making it difficult to accurately characterize complex near-surface structures. In this study, the authors propose an ERT and SRT joint inversion method based on the ensemble Kalman filter (EnKF), which enables the jointy constrained inversion of resistivity and velocity and provides uncertainty quantification results. By introducing structural consistency constraints during the inversion update process, the inversion accuracy of the resistivity and velocity models at layer interfaces and anomalous structures is improved, enhancing interface continuity. Model testing and field data from the Northeast Black Soil Test Field demonstrate that the proposed EnKF joint inversion strategy can effectively improve imaging resolution, providing a reliable framework for geophysical imaging and interpretation in complex near-surface applications.