Joint inversion of ERT and ambient noise surface wave data with DPC-guided fuzzy c-means clustering for near-surface imaging

Abstract

SUMMARY We present a novel strategy for performing joint inversion with guided fuzzy c-means (GFCM) clustering coupling and apply it to electrical resistivity tomography (ERT) and ambient noise surface wave (ANSW) data. To accurately extract a priori clustering information, we use density peak clustering (DPC) rather than fuzzy c-means (FCM). The number and centres of resistivity and shear-wave velocity a priori clusters are extracted by DPC and then used to guide the joint inversion with the GFCM clustering coupling of ERT and ANSW data. Synthetic and field data are used to evaluate the flow and algorithm of DPC-GFCM clustering joint inversion. The results of synthetic examples show that the models recovered by the DPC-GFCM clustering joint inversion are nearly the same as the true models and are more accurate than those inverted using individual inversion and FCM-GFCM clustering joint inversion. In the field case, the depths of the stratigraphic interfaces shown in the resistivity and shear-wave velocity models inverted by DPC-GFCM clustering joint inversion are nearly consistent with those from the drilling data. In contrast, the strata recovered by the individual inversion and FCM-GFCM clustering joint inversion significantly differ from the drilling results. Both the synthetic and field examples verify the effectiveness of the DPC-GFCM clustering coupling method used for the joint inversion of ERT and ANSW data acquired from the near surface with strong heterogeneity. This novel approach can also be applied to other types of geophysical data.