Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

Vertical electrical sounding (VES) data are usually interpreted in terms of a 1D resistivity model using linearized inversion. The local approach of a non-linear inverse problem has fundamental limitations, i.e. the necessity of a starting model close to the solution and possible convergence to a local rather than a global minimum solution. We studied the application of a global search approach for non-linear inversion using the guided random search method to model VES data. A quasi-2D resistivity model can be created by stitching 1D models obtained from VES data along a profile. Both vertical and lateral resistivity variations are minimized to incorporate a 2D smoothness constraint. The proposed method was applied to invert synthetic VES data as well as field data from a sedimentary environment. Both synthetic and field data inversions resulted in models that correlated well with the known synthetic model and with the geology of the study area, respectively

Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

Vertical electrical sounding (VES) data are usually interpreted in terms of a 1D resistivity model using linearized inversion. The local approach of a non-linear inverse problem has fundamental limitations, i.e. the necessity of a starting model close to the solution and possible convergence to a local rather than a global minimum solution. We studied the application of a global search approach for non-linear inversion using the guided random search method to model VES data. A quasi-2D resistivity model can be created by stitching 1D models obtained from VES data along a profile. Both vertical and lateral resistivity variations are minimized to incorporate a 2D smoothness constraint. The proposed method was applied to invert synthetic VES data as well as field data from a sedimentary environment. Both synthetic and field data inversions resulted in models that correlated well with the known synthetic model and with the geology of the study area, respectively

Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

Vertical electrical sounding (VES) data are usually interpreted in terms of a 1D resistivity model using linearized inversion. The local approach of a non-linear inverse problem has fundamental limitations, i.e. the necessity of a starting model close to the solution and possible convergence to a local rather than a global minimum solution. We studied the application of a global search approach for non-linear inversion using the guided random search method to model VES data. A quasi-2D resistivity model can be created by stitching 1D models obtained from VES data along a profile. Both vertical and lateral resistivity variations are minimized to incorporate a 2D smoothness constraint. The proposed method was applied to invert synthetic VES data as well as field data from a sedimentary environment. Both synthetic and field data inversions resulted in models that correlated well with the known synthetic model and with the geology of the study area, respectively

Interpretation of 1D Vector Controlled-Source Audio-Magnetotelluric (CSAMT) Data Using Full Solution Modeling

In conventional controlled-source audio-magnetotelluric (CSAMT) prospecting, scalar CSAMT measurement is usually performed because of its simplicity and low operational cost. Since the structure of earth's conductivity is complex, the scalar CSAMT method can lead to a less accurate interpretation. The complex conditions need more sophisticated measurements, such as vector or tensor CSAMT, to interpret the data. This paper presents 1D vector CSAMT interpretation. A full solution 1D CSAMT forward modeling has been developed and used to interpret both vector and scalar CSAMT data. Occam's smoothness constrained inversion was used to test the vector and scalar CSAMT interpretations. The results indicate the importance of vector CSAMT to interpret CSAMT data in complex geological system