Full Tensor Gravity Gradiometry Data Research Articles

Joint inversion of full-tensor gravity gradiometry data based on source growing

Joint inversion of full-tensor gravity gradiometry data based on source growing

Joint focusing inversion of marine controlled-source electromagnetic and full tensor gravity gradiometry data

We have developed an approach for integrating the interdependent information of different geophysical methods to obtain a self-consistent geophysical model in offshore exploration. Electromagnetic (EM) and gravity data complement seismic data by providing information about the physical properties of prospective reservoir rocks. We have combined marine EM and gravity gradiometry geophysical data in joint inversion using a joint focusing stabilizer. The method enforces coupling between different physical models and simultaneously promotes focused images of the targets. We also consider the importance of using robust norms to analyze data collected by marine controlled-source EM surveys. The practical effectiveness of the developed method is illustrated by a case study of integrating and imaging marine multiphysics data collected in the Nordkapp Basin, Barents Sea, Norway.

Joint Nonlinear Inversion of Full Tensor Gravity Gradiometry Data and Its Parallel Algorithm

Geophysical joint inversion is more frequently applied to deep crust probes. For the potential field data, it requires the introduction of large-scale observed data and sensitivity matrices. Massive matrix-vector multiplications occur during iterations, and the obtained data cannot be effectively interpreted by merely using desktop computers. To improve the resolution and the computing ability of inversion, we here propose the parallel joint nonlinear inversion of full tensor gravity gradiometry data. As the inversion is affected by linear searches, it is associated with certain classical computing performance issues. Hence, we addressed the memory and efficiency limitations, which are caused by very large calculation volumes. Also, we performed a quantitative and comprehensive feasibility analysis of parallel computing. Then, we identified the main factor influencing the inversion performance and clarified the correspondence between the cell number and the memory. A parallel inversion solution was proposed via graphics processing unit (GPU) based on the sensitivity matrix compression. The data tests demonstrated that inversion has antinoise property and that it can obtain accurate underground density distributions. Also, the parallel solution was found to be suitable for inverting cells at the million cell scale and greater because of its ability of acceleration and matrix compression. A design pattern was applied for gravity or magnetic anomaly inversion of 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 100\times20$ </tex-math></inline-formula> cells, and the run time was less than 1 min. Overall, we believe that the proposed solution can help implement massive potential field data inversions and promote the application of the parallel technique in other inversion research.

Shallow velocity modelling – the application of high-resolution Full Tensor Gravity Gradiometry data and seismic tomography: a Permian Basin case study

Subsurface imaging from surface seismic reflection data is often complex as a result of issues related to the surface and the near surface including noise (ambient or otherwise), differing coupling of the geophones/geophone arrays and different coupling between the sources and mixed sources and the ground. With the diversity in land seismic exploration and increasingly complex near-surface conditions advanced modelling capabilities are being used for velocity model generation in processing. In this article the use of a multi-physics approach that does not suffer from velocity-depth ambiguity and provides a robust shallow velocity model is presented. This case study is taken from a project in the Permian Basin, USA. It presents the integration of Airborne Full Tensor Gravity Gradiometry (FTG) with tomography and the unique approach applied by the authors for modelling the near surface velocity structure integrated with surface seismic data.

Structural interpretation of the Beetaloo Sub-basin, NT from nonseismic geophysical data

The Beetaloo Sub-basin is known for its vast unconventional hydrocarbon resources even though it is relatively underexplored. There is reasonably good coverage of 2D seismic within the sub-basin which is used as the basis for most structural interpretations. However, seismic quality varies, and it is occasionally deteriorated by the presence of basalts from the Kalkarindji suite and the karstic nature of the Gum Ridge formation. Aeromagnetic data, constrained by petrophysical logs are used, to map faults in the basalts of the Kalkarindji suite and their lateral extent to the South and the East of the sub-basin. The same structural elements are identified in the full tensor gravity gradiometry data. The top of this unit is observed in the electrical conductivity profiles, derived from Tempest data, in the NW part of the eastern sub-basin.

Adaptive multinary inversion of gravity and gravity gradiometry data

We have developed a novel approach for inversion of gravity and gravity gradiometry data based on multinary transformation of the model parameters. This concept is a generalization of binary density inversion to the models described by any number of discrete model parameters. The multinary inversion makes it possible to explicitly exploit the sharp contrasts of the density between the host media and anomalous targets in the inversion of gravity and gravity gradiometry data. In the framework of the multinary inversion method, we use the given values of density and error functions to transform the density distribution into the desired step-function distribution. To accommodate a possible deviation of the densities from the fixed discrete values, we develop an adaptive technique for selecting the corresponding standard deviations, guided by the inversion process. The novel adaptive multinary inversion algorithm is demonstrated to be effective in determining the shape, location, and densities of the anomalous targets. We find that this method can be effectively applied for the inversion of the full tensor gravity gradiometry (FTG) data computer simulated for the SEG salt density model and for the field FTG data collected in the Nordkapp Basin, Barents Sea.

Feasibility and Limitations of Void Detection Using Gravity Gradiometry

Detecting and characterizing near-surface voids is a long-standing problem in near-surface geophysics primarily because of the low signal-to-noise ratio due to the typically small sizes of these targets. Anticipated advances in gravity gradiometry instrumentation and data acquisition may provide the improved resolution required to address these targets. In this paper, we investigate void detection using simulated full tensor gravity gradiometry data. We show through numerical simulations that low-altitude gravity gradiometry can observe signals from voids well above the modern instrument noise level. We develop a practical detection algorithm to determine the tunnel orientation and its location in the subsurface. The algorithm first finds the tunnel orientation through a tensor rotation and then solves for the tunnel location in the subsurface based on the directional vectors obtained from the rotated tensor data. We use this algorithm to study the feasibility and limitations of the gravity gradiometry technique in void detection. We conclude that the method can be effective in many practical scenarios.

Full tensor gravity gradiometry data inversion: Performance analysis of parallel computing algorithms

We apply reweighted inversion focusing to full tensor gravity gradiometry data using message-passing interface (MPI) and compute unified device architecture (CUDA) parallel computing algorithms, and then combine MPI with CUDA to formulate a hybrid algorithm. Parallel computing performance metrics are introduced to analyze and compare the performance of the algorithms. We summarize the rules for the performance evaluation of parallel algorithms. We use model and real data from the Vinton salt dome to test the algorithms. We find good match between model and real density data, and verify the high efficiency and feasibility of parallel computing algorithms in the inversion of full tensor gravity gradiometry data.

Fast Density Inversion Solution for Full Tensor Gravity Gradiometry Data

We modify the classical preconditioned conjugate gradient method for full tensor gravity gradiometry data. The resulting parallelized algorithm is implemented on a cluster to achieve rapid density inversions for various scenarios, overcoming the problems of computation time and memory requirements caused by too many iterations. The proposed approach is mainly based on parallel programming using the Message Passing Interface, supplemented by Open Multi-Processing. Our implementation is efficient and scalable, enabling its use with large-scale data. We consider two synthetic models and real survey data from Vinton Dome, US, and demonstrate that our solutions are reliable and feasible.

Multinary Inversion for Tunnel Detection

We introduce multinary inversion to explicitly exploit the physical property contrasts between different objects and their host medium, e.g., between air-filled tunnels and their surrounding earth. Conceptually, multinary inversion is a generalization of binary inversion to multiple physical properties. However, unlike existing realizations of binary inversion which are solved using stochastic optimization methods, our realization of multinary inversion can be solved using deterministic optimization methods. This is significant as the method can be applied to both linear and nonlinear operators and easily extends to joint inversion of multimodal geophysical data. Using synthetic models of full-tensor gravity gradiometry data, multinary inversion is demonstrated to be robust for tunnel detection relative to the presence of significant geological noise.