Deep Learning Inversion of Electrical Resistivity Data

Inversion of Electrical Resistivity Data: A Review

High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used f...

A direct inversion scheme for deep resistivity sounding data using artificial neural networks

Initialization of model parameters is crucial in the conventional 1D inversion of DC electrical data, since a poor guess may result in undesired parameter estimations. In the present work, we investigate the performance of neural networks in the direct inversion of DC sounding data, without the need of a priori information. We introduce a two-step network approach where the first network identi...

Piecewise 1D laterally constrained inversion of resistivity data

In a sedimentary environment, layered models are often capable of representing the actual geology more accurately than smooth minimum structure models. Furthermore, interval thicknesses and resistivities are often the parameters to which nongeophysicist experts can relate and base decisions on when using them in waste site remediation, groundwater modelling and physical planning. We present a l...

3D Inversion of Automated Resistivity Profiling (ARP) Data

Three-dimensional numerical modelling and inversion of magnetometric resistivity data

We develop an algorithm to model magnetometric resistivity (MMR) response over an arbitrary 3-D conductivity structure and a method for inverting surface MMR data to recover a 3-D distribution of conductivity contrast. In the forward modeling algorithm, the second-order partial differential equations for the scalar and vector potentials are discretized on a staggered gird using the finitevolume...