SUMMARY Magnetic susceptibility and natural remanent magnetization of rocks are useful parameters to study geological structures and geodynamic processes. Traditional widely used algorithms for the inversion of magnetic data can recover the distribution of the apparent susceptibility or total magnetization intensity, but do not provide information on the remanent magnetization. In this paper, we propose a framework to directly invert for the magnetic susceptibility and the natural remanent magnetization vector using surface or airborne magnetic data, assuming that the Köenigsberger ratio of the rock is known or approximately deducible. The susceptibility and remanence are computed using two different approaches: (1) the susceptibility, intensity, and direction of the remanent magnetization are continuously recovered for each discretized cell and (2) the remanence direction is assumed to be uniform in each subzone and is iteratively computed as discrete values. Both processes are implemented using the preconditioned conjugate gradient algorithm. The method is tested on three synthetic models and one field data set from the Zaohuohexi iron-ore deposit, Qinghai Province, northwest China. The results of the continuous inversion show the trend of the remanent magnetization directions, while the discrete inversion yields more specific values. This inversion framework can determine the source bodies’ geometry and position, and also provide superposed and comprehensive information on the natural remanent magnetization, which may be useful to investigate geological bodies bearing stable primary remanent magnetization.
SUMMARY Joint inversion of multiple geophysical data sets with complementary information content can significantly reduce the non-uniqueness inherent to each individual data set and, therefore, can improve subsurface characterization. Gradient-based joint inversion methods depend on the choice of model regularization and usually produce one single optimal model, and rely on linearization to estimate model parameter uncertainty. However, a quantitative evaluation of the parameter uncertainty of the derived model parameters is crucial for reliable data interpretation. In this study, we present a transdimensional Markov Chain Monte Carlo (MCMC) method for the joint inversion of direct current resistivity and transient electromagnetic data, which provides a rigorous assessment of the uncertainty associated with the derived model. The transdimensional property of the algorithm allows the number of unknown model parameters to be determined adaptively by the data. This usually favours models with fewer parameters through the parsimony criterion of the Bayesian method by choosing suitable prior distributions. In this paper, we demonstrate that the transdimensional MCMC method combines complementary information contained in each data set and reduces the overall uncertainty using synthetic examples. Furthermore, we successfully applied the new joint inversion scheme to field data from Azraq, Jordan. The transdimensional MCMC inversion results are in good agreement with the results obtained by deterministic inversion techniques. From the MCMC inversion results we identified the thickness of a basalt formation and a conductive zone, which were uncertain and not interpreted in prior studies, adding to the geological interpretation.
Abstract The location and origin of Neoproterozoic‐Cambrian sutures provide keys to understand the formation and evolution of the supercontinent Gondwana. The Larsemann Hills is located near a major Neoproterozoic‐Cambrian suture zone in the Prydz Belt, but has not been examined locally by comprehensive geophysical studies. In this study, we analyzed data collected from a one‐dimensional (1D) joint seismic‐MT array deployed during the 36th Chinese National Antarctic Research Expedition. We found that a sharp Moho discontinuity offset of 6–8 km shows up in the stacked image of teleseismic P‐wave receiver function analysis; coinciding with the abrupt Moho offset, a near‐vertical channel with (a) low resistivity extending to the uppermost mantle depths, and (b) high crustal Poisson's ratio in the crust is identified. These findings provide evidence for the determination of the location and collisional nature of the Prydz belt or a portion of it.
Owing to its sensitivity to hydrothermal alteration and geothermal fluids, the distribution of subsurface electrical conductivity provides critical information for characterizing geothermal systems. For geothermal exploration, the controlled-source electromagnetic (CSEM) technique provides a crucial tool to image the subsurface resistivity structures, especially in areas with strong cultural noise. Usually, land-based CSEM surveys are carried out with dozens of operating frequencies to enhance spatial resolution. However, the 3D inversion of multifrequency CSEM data using the commonly used direct solver is challenging with limited computational resources. In this study, we implement a practical inversion strategy for interpreting 3D multifrequency CSEM data. By combining a hybrid direct-iterative solver with the inexact Gauss-Newton optimization, 3D inversion of CSEM data involving multiple frequencies can be performed effectively on typical workstations. First, we test the effectiveness of the developed approach using synthetic multifrequency 3D CSEM data generated for a simplified geothermal model. The inversion strategy is then applied to the multifrequency CSEM field data collected for the geothermal exploration at Tianzhen region in Shanxi Province, China. The comparison between the inversion results using the sparse data set (six frequencies) and the dense data set (12 frequencies) highlights the necessity of using data from more frequencies in the inversion to improve the resolution. The resulting 3D resistivity model clearly delineates the clay alteration layers and shallow thermal reservoirs of the potential high-temperature geothermal system within the study area, while its deep heat source is not revealed owing to the limited investigation depth of the survey.
Ground filtering (GF) is a fundamental step for airborne laser scanning (ALS) data processing. The advent of deep learning techniques provides new solutions to this problem. Existing deep-learning-based methods utilize a segmentation or classification framework to extract ground/non-ground points, which suffers from a dilemma in keeping high spatial resolution while acquiring rich contextual information when dealing with large-scale ALS data due to the computing resource limits. To this end, we propose SeqGP, a novel deep-learning-based GF pipeline that explicitly converts the GF task into an iterative sequential ground prediction (SeqGP) problem using points-profiles. The proposed SeqGP utilizes deep reinforcement learning (DRL) to optimize the prediction sequence and retrieve the bare terrain gradually. The 3D sparse convolution is integrated with the SeqGP strategy to generate high-precision classification results with memory efficiency. Extensive experiments on two challenging test sets demonstrate the state-of-the-art filtering performance and universality of the proposed method in dealing with large-scale ALS data.
Natural remanent magnetization (NRM) is the part of rock magnetism, which leads to more complex magnetic data inversion and interpretation. The conventional inversion methods focus on reducing the influence of the remanent magnetization and recovering the depth and shape of magnetic sources, while the remanent magnetization is an important parameter to understand the mineralization /geological process. The remanent magnetization can not only reflect the geological structure and mineral composition, but also contain the geomagnetic field record of each geological period. We study to obtain the geological information involved with the remanent magnetization. In the case of Daye iron-ore deposit in Hubei province, we calculate the direction and intensity of remanent magnetization based on the prior information of Königbergs ratio, the direction and intensity of the total magnetization. In the Yeshan region, we use the IDQ curve to estimate the direction of the remanent magnetization. The results show that the remanent magnetization direction, extracted from magnetic anomaly, can not only indicate the information of local geological activities, but also be used to classify the lithologies of the sub-anomalies.
Summary We developed a parallelized 3D inversion algorithm for large-scale controlled controlled-source electromagnetic (CSEM) data in data space. We solve the forward modelling process with the finite element (FEM) method based on the unstructured tetrahedral elements, which can efficiently deal with complex geometry CSEM problems. For the inversion process, we transformed the conventional model space inversion to data space to decrease the RAM memory requirement. A synthetic CSEM land model with three anomalous bodies is applied to demonstrate the effectiveness and stableness of the developed CSEM inversion scheme. By comparing the inversion results of the conventional model space inversion method and the data space inversion method, we validated the newly proposed method is more sensitive to the low conductor. Further, the new inversion scheme requires much less memory compared to the model space inversion method, which validates the applicability of the developed inversion scheme in data space to the large-scale field CSEM data with complex geometry.
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