pymtinv: 2D Magnetotelluric Inversion Library

pymtinv is a Python-based scientific computing library designed for 2D Magnetotelluric (MT) inversion. It serves as a comprehensive research framework to benchmark classical deterministic optimization methods against novel Probabilistic Computing (P-bit) architectures tailored for FPGA acceleration.

This project bridges the gap between Geophysical Inverse Problems and Emerging Hardware Architectures, demonstrating how stochastic logic can revolutionize high-dimensional optimization.

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Key Features

• ** High-Performance Forward Solver:** Solves the Helmholtz equation for TE-mode using the Finite Difference Method (FDM) on a staggered grid.
• ∇ Adjoint State Gradient: Implements efficient gradient calculation independent of the number of model parameters ($O(1)$ complexity), enabling large-scale inversion.
• ** Auto-Tuning Framework:**
  • Automatic Regularization: Determines the optimal Tikhonov parameter ($\beta$) using a fast, robust L-Curve scan.
  • Hyperparameter Search: Automatically tunes Learning Rate and Temperature for stochastic p-bit optimization via grid search.
• ** Probabilistic Inversion (P-bits):** Simulates Langevin Dynamics to emulate p-bit networks (invertible logic), enabling global search capabilities and escaping local minima.
• ** Advanced Visualization:** Plotting tools with support for non-uniform meshes (padding), logarithmic conductivity maps, and convergence graphs.
• ** FPGA Projection:** Benchmarking tools to compare CPU execution times with theoretical FPGA performance (massively parallel updates).