Simulating 2-D magnetotelluric responses using vector-quantized temporal associative memory artificial neural network-based approaches

Abstract

Abstract In this research, we explore the application of artificial neural networks, specifically the vector-quantized temporal associative memory (VQTAM) and VQTAM coupled with locally linear embedding (VQTAM-LLE) techniques, for simulating 2-D magnetotelluric forward modeling. The study introduces the concepts of VQTAM and VQTAM-LLE in the context of simulating 2-D magnetotelluric responses, outlining their underlying principles. We rigorously evaluate the accuracy and efficiency of both VQTAM variants through extensive numerical experiments conducted on diverse benchmark resistivity and real-terrain models. The results demonstrate the remarkable capability of VQTAM and VQTAM-LLE in accurately and efficiently predicting apparent resistivity and impedance phases, surpassing the performance of traditional numerical methods. This study underscores the potential of VQTAM and VQTAM-LLE as valuable computational alternatives for simulating magnetotelluric responses, offering a viable choice alongside conventional methods.

Keywords

• Magnetotelluric modeling
• Neural networks
• VQTAM
• VQTAM-LLE