arXiv subjects

Jochen Kamm

Publications and source records attributed to Jochen Kamm.

4 recordsLinked to original sources

arXiv2026

Backend-agnostic Julia framework for 3D modeling and inversion of gravity data

This paper presents a high-performance framework for three-dimensional gravity modeling and inversion implemented in Julia, addressing key computational challenges associated with gravity inversion, including large scale problem, ill-posedness, and non-uniqueness. The framework employs a data-space inversion formulation that reduces the dimensionality of the inverse problem, resulting in lower memory requirements and improved computational efficiency. Forward modeling and inversion operators are implemented using a backend-agnostic kernel abstraction, allowing the same computational code to run on multicore CPUs and GPU accelerators. Performance evaluations using NVIDIA CUDA GPUs show significant reductions in computational time relative to CPU execution, particularly for large-scale problems involving up to approximately 3.3 million rectangular prisms and 178,797 gravity observations. The inversion incorporates implicit model constraints through the data-space formulation and depth-weighted sensitivity, which reduces the effect of depth-dependent amplitude decay and promotes geologically coherent density models. Synthetic experiments demonstrate the capability of the framework to recover complex subsurface structures, including vertical and dipping dykes. Application to field gravity data further demonstrates the practical applicability of the proposed approach, with the recovered density distributions showing good agreement with independent geological constraints and previous interpretations. The results demonstrate that GPU-accelerated Julia provides an efficient and extensible platform for large-scale three-dimensional gravity modeling and inversion, enabling high-resolution geophysical investigations with reduced computational and memory requirements.

physics.geo-ph↗

arXiv2025

The Complex-Step Integral Transform

Building on the well-established connection between the Hilbert transform and derivative operators, and motivated by recent developments in complex-step differentiation, we introduce the Complex-Step Integral Transform (CSIT): a generalized integral transform that combines analytic continuation, derivative approximation, and multi-scale smoothing within a unified framework. A spectral analysis shows that the CSIT preserves phase while suppressing high-wavenumber noise, offering advantages over conventional Fourier derivatives. We discuss the roles of the real and imaginary step parameters, compare FFT-based and interpolation-based implementations, and demonstrate the method on the advection equation and instantaneous-frequency computation. Results show that the CSIT yields smoother, more robust attributes than Hilbert-based methods and provides built-in stabilization for PDE solvers. The CSIT thus represents a flexible alternative for numerical differentiation, spectral analysis, and seismic signal processing. The method opens several avenues for future work, including non-periodic implementations, adaptive parameter selection, and integration with local interpolation frameworks such as high-order Finite-Element methods.

math.NA↗

arXiv2025

Three-dimensional inversion of gravity data using implicit neural representations and scientific machine learning

Inversion of gravity data is an important method for investigating subsurface density variations relevant to mineral exploration, geothermal assessment, carbon storage, natural hydrogen, groundwater resources, and tectonic evolution. Here we present a scientific machine-learning approach for three-dimensional gravity inversion that represents subsurface density as a continuous field using an implicit neural representation (INR). The method trains a deep neural network directly through a physics-based forward-model loss, mapping spatial coordinates to a continuous density field without predefined meshes or discretisation. Spatial encoding enhances the network's capacity to capture sharp contrasts and short-wavelength features that conventional coordinate-based networks tend to oversmooth due to spectral bias. We demonstrate the approach on synthetic examples including smooth models, representing realistic geological complexity, and a dipping block model to assess recovery of structures at different depths. The INR framework reconstructs detailed structure and geologically plausible boundaries without explicit regularisation or depth weighting, while reducing the number of inversion parameters as the problem size grows bigger. These results highlight the potential of implicit representations to enable scalable, flexible, and interpretable large-scale geophysical inversion. This framework could generalise to other geophysical methods and for joint/multiphysics inversion.

physics.geo-ph↗

arXiv2024

Ground electrical and electromagnetic methods for deep mineral exploration -- results from the SEEMS DEEP project

The transition towards carbon neutral transportation and energy sources increases the global demand for mineral raw materials while easy-to-find near-surface (\< 200 m) ore deposits are unlikely discovered in well-explored areas such as Europe. In order to increase the mineral exploration success rate, the project SEEMS DEEP (SEismic and ElectroMagnetic methodS for DEEP mineral exploration) develops geophysical deep exploration workflow capable of imaging the bedrock from the surface down to several kilometres depth. In this paper, we present first results from ground electrical and electromagnetic surveys conducted at the SEEM DEEP geological test site, namely the Koillismaa Layered Intrusion Complex in north-eastern Finland. Here, a 1.7 km long hole drilled by GTK intersected mafic-ultramafic rocks with anomalous electrical and chargeability properties at ~1400 m depth, making it an interesting case study to test the ability of such technologies for imaging resistivity and chargeability contrasts at several kilometre depth.

physics.geo-ph↗