Björn Heincke

High-resolution geophysical techniques for improving hazard assessments of unstable rock slopes

Unstable slopes are an increasing concern in mountainous regions worldwide. Significant expansion of human habitats and transport routes in mountain valleys, melting of alpine permafrost as a consequence of global warming, and exceptional climatic events are amplifying the risks of catastrophic mountain-slope failures. To minimize the effects of such failures, short-term predictions are required for the timely evacuation of vulnerable populations, and medium-term forecasts are needed for the optimum design and construction of barriers that protect lifelines (e.g., roads, railways, and pipelines) and other expensive infrastructure. Moreover, long-term hazard assessments are necessary for prudent land-use planning. These tasks require detailed information on the extent and probable behavior of unstable rock. In this context, the locations and geometries of major fractures and faults are particularly important.

Adaptive coupling strategy for simultaneous joint inversions that use petrophysical information as constraints

Joint inversion strategies for geophysical data have become increasingly popular since they allow to combine complementary information from different data sets in an efficient way. Here, we present a non-linear joint inversion scheme, in which data from different methods are inverted separately and are joined through constrains accounting for parameter relationships. To avoid that the convergence behavior of the inversions is not profoundly disturbed by this coupling, the strengths of the constraints are re-adjusted at each iteration. In contrast to a joint inversion with a fixed parameter relationship, where data is inverted to one common model, this scheme requires no relative weighting of the data sets from different methods. Moreover, we observe that the adaption of the coupling strengths makes the convergence of the inversions much more robust. When we test our scheme with and without adaption on a synthetic 2-D model with seismic tomography, gravity and MT data, the final results with adaption were significantly closer to the true model. Finally, we observe that the adaptive scheme can to some extent handle models with structures for which the assumed parameter relationships are invalid.

GPU parallelization of a three dimensional marine CSEM code

One of the main problem of 3D time domain controlled source electromagnetic (CSEM) inversion is the high runtimes of forward modeling codes. We reduced the runtime of the 3D time domain finite difference CSEM code TEMDDD by the GPU-parallelization of expensive algorithms. The code solves the electromagnetic diffusion equation by discretization of spatial operators and subsequent calculation of eigenpairs. These eigenpairs are found by approximation of the eigenspace in a Krylov subspace using the spectral Lanczos decomposition Method. This algorithm was in its original form not parallelizable due to implementation of the upper boundary condition at the air-water interface. We show for the marine case that replacing the original boundary condition at the air-water surface by a discretized air layer allows GPU parallelization of every time consuming algorithm of the code in the marine case. Speedups between 20 and 60 have been achieved compared to the original code for a larger 3-D model. In this model the bathymetry from a survey area offshore Egypt is used as an example demonstrating that the parallelized version of the code is applicable to real survey scenarios.

3D georadar surveying in areas of moderate topographic relief

A three-dimensional (3-D) georadar survey has been conducted across a 41.2 x 34.5m area with moderate topographic relief (dips: 4 - 16°) near Randa in southwestern Switzerland. For this survey, we employed a semiautomated acquisition system that combined a standard georadar unit with a self-tracking theodolite. This system recorded georadar data and coordinates simultaneously. Subsequently, an accurate topographic model of the acquisition surface was determined from the measured coordinates. With the aid of this topographic model, a provisional static correction for each georadar trace was determined. Application of the static corrections removed the most significant distortions of the major reflections and diffractions, which likely originated from the soil-rock interface and/or major fractures.

Emulation: A Bayesian tool for joint inversion

Where several different kinds of geophysical datasets have been acquired from a particular region, each of these can contain valuable information about the Earth, which may not be present in the other datasets. Jointly determining a common model, therefore, often gives a more thorough and more constrained description of the Earth structure than considering each dataset individually. For example, a seismic velocity inversion is only weakly constrained by first arrival seismic refraction data, but considering it alongside Magneto-Telluric (MT) and gravity data can greatly assist in the constraint (Jegen-Kulcsar et al., 2009). Strategies for joint inversion are therefore an active area of research. To date, most schemes for accomplishing this have been deterministic in nature. Using a deterministic technique often means that it is conceptually difficult to include prior beliefs about the system under determination, uncertainties both in measurement and the relationship between the different physical quantities (velocity, resistivity, density), and the discrepancy between the model and the real Earth. Statistical strategies such as MCMC (Markov Chain Monte Carlo) model searches exist for assessing this kind of problem, but the number of potentially computationally expensive forward model runs required to effectively sample the whole model space and thus achieve a meaningful result is normally prohibitively high (> 105), even for simple 1D models, so such schemes are not generally implemented. However, a technique known as emulation is used in various scientific fields eg. cosmology (Vernon and Goldstein, 2009), whereby computationally expensive forward modelling code (a simulator) is approximated by an uncertainty-calibrated computationally cheap function. Here we apply emulation to the problem of stochastic joint model determination. We show that emulation can be used to quickly exclude large areas of implausible model space, allowing fast updating of beliefs about an Earth structure. It thus provides a means by which the input model space for a deterministic inversion or MCMC scheme can be greatly reduced. We also show how an emulator can, by itself, effectively constrain a region of the Earth. We demonstrate the concept using a 1D model.

2-D and 3-D Joint Inversion of Seismic, MT and Gravity Data from the Faroe-Shetland Basin

We present both 2-D and 3-D joint inversions of seismic tomography, MT and gravity data from Faroe-Shetland Basin. The objective is to evaluate how far petrophysically-linked joint inversions can improve both the imaging and the interpretation of the basalt flows and the underlying structures. The investigation area is License006, where the presence of a borehole (BRUGDAN) and a 3-D reflection seismic survey allow a direct comparison and, hence, an evaluation of the quality of the joint inversion models. For the 2-D and 3-D investigations we use different joint-inversion codes that have other strengths and weaknesses. We observe that results from both 2-D and 3-D joint inversions are more consistent than the ones from the corresponding individual inversions and the shape of the basaltic sequence and structures underneath are better resolved. The 2-D joint inversion in particular gives precise basalt thicknesses, whereas the 3-D joint inversion determines a two-dimensional high-resistivity anomaly that is oriented in the same direction as general regional trends of basement.