Joint inversion of MT data with GTF and HMT data: Main advantages from a synthetic case study and real data

Abstract:

In this work we assess the main advantages of complementing the standard magnetotelluric (MT) impedance tensor (Z) response with inter-station horizontal magnetic tensor (HMT) and geomagnetic transfer function (GTF) responses. Results were obtained from constraining the subsurface beneath a MT profile in a 3-D environment using distorted synthetic responses and real data.

From the synthetic case study four main improvements were observed when performing joint inversion of Z, HMT and GTF data simultaneously, instead of only inverting the commonly used Zdata: (1) superior precision in the characterization of the electrical resistivity values of the anomalies below and outside the profile; (2) the potential to recover high electrical resistivity anomalies that are poorly recovered by using MT responses alone; (3) improvement in the characterization of the bottom and lateral boundaries of the anomalies with low electrical resistivity; and (4) superior imaging of the continuity of structures with low electrical resistivity.

A corresponding inversion comparison was performed using real responses from Clare Basin (Co. Clare, Ireland) acquired during summer of 2014 within the IRECCSEM project (www.ireccsem.ie). A total of 140 sites were acquired, including audiomagnetotellurics (AMT) and broadband magnetotelluric (BBMT) time series data. The results obtained undertaking a joint inversion of Z, GTF and HMT data corroborate the results observed in the synthetic study. In addition, when confronted the results of the Clare Basin area with independent geological and geophysical data, the geoelectrical models show higher resolution when HMT and GTF data are included in the inversion process.

The observed advantages offer new opportunities for the MT method by making the results from a MT profile in a 3-D environment more believable, supporting the possibility of high-resolution studies without necessarily expending a large amount of economical and computational resources.