2019, Lin, Tingting, Zhang, Yang, Muller-Petke, Mike

Magnetic resonance sounding (MRS) signals are always corrupted by random noise. Although time-frequency peak filtering (TFPF) has been proven to be an effective method to suppress the random noise, it shows shortcomings when processing the oscillating high-frequency MRS signal at about 2 kHz. In this study, a new method combining empirical mode decomposition (EMD) and TFPF is proposed to overcome the TFPF limitation when processing the MRS oscillating signal. With the help of EMD decomposition characteristics, the random-noise-corrupted MRS oscillating signal is first decomposed into several different components which contain frequencies ranging from the highest to the lowest ones. Then, the components which do not have signal frequency are discarded to bring down the level of random noise. The residual components are further processed by TFPF, respectively, based on the theory of instantaneous frequency estimation and the property of noise accumulation. Finally, the de-noised result is obtained by reconstructing the processed components. The numerical simulations on synthetic signals embedded in both artificial noise and real noise show the combined method can improve the signal-to-noise ratios and reduce the uncertainties of signal parameters. In addition, the combined method is applied following a standard processing scheme in field data, and better results are also obtained.

2017, Rücker, Carsten, Günther, Thomas, Wagner, Florian M.

Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple, and modern software architectures aiming at a uniform interface for interdisciplinary and flexible modelling and inversion approaches. We present pyGIMLi (Python Library for Inversion and Modelling in Geophysics), an open-source framework that provides tools for modelling and inversion of various geophysical but also hydrological methods. The modelling component supplies discretization management and the numerical basis for finite-element and finite-volume solvers in 1D, 2D and 3D on arbitrarily structured meshes. The generalized inversion framework solves the minimization problem with a Gauss-Newton algorithm for any physical forward operator and provides opportunities for uncertainty and resolution analyses. More general requirements, such as flexible regularization strategies, time-lapse processing and different sorts of coupling individual methods are provided independently of the actual methods used. The usage of pyGIMLi is first demonstrated by solving the steady-state heat equation, followed by a demonstration of more complex capabilities for the combination of different geophysical data sets. A fully coupled hydrogeophysical inversion of electrical resistivity tomography (ERT) data of a simulated tracer experiment is presented that allows to directly reconstruct the underlying hydraulic conductivity distribution of the aquifer. Another example demonstrates the improvement of jointly inverting ERT and ultrasonic data with respect to saturation by a new approach that incorporates petrophysical relations in the inversion. Potential applications of the presented framework are manifold and include time-lapse, constrained, joint, and coupled inversions of various geophysical and hydrological data sets.