Fast approximate EM induction modeling of metallic and UXO targets using a permeable prism

The time-domain EM induction response of non-magnetic and magnetic targets can be approximated using a conductive permeable prism composed of six faces of conductive plates, each face being composed of a set of conductive ribbons. The effect of magnetic permeability is included by the use of two “apparent flux gathering” coefficients, and two “effective magnetic permeability” coefficients, in the axial and transverse directions. These four magnetic property coefficients are a function of physical properties and geometry of the target, but are independent of prism orientation relative to a transmitter. The approximation algorithm is computationally fast, allowing inversions for target parameters to be achieved in seconds. The model is tested on profiles acquired with a Geonics EM63 time-domain EM metal detector over a non-magnetic copper pipe target, and a steel artillery shell in horizontal and vertical orientations. Results show that this approximation to a permeable prism has a capability of fitting geometric, conductivity and magnetic parameters at both early and late sample times. The magnetic parameters show strong change from early to late times on the EMI decay curve, indicating that the magnetic properties of the target have non-linear characteristics. It is proposed that these magnetic parameters and the nature of their non-linearity may carry additional discrimination information for distinguishing between intact munitions and scrap in UXO studies.     

Independent component analysis for UXO detection in highly cluttered environments

Statistical signal processing techniques have shown progress in discriminating UXO from clutter when the objects occur in isolation. Under this condition, only a single object contributes to the measured sensor data. For multiple closely spaced subsurface objects, however, the unprocessed sensor measurement is a mixture of the responses from several objects. Consequently, the unprocessed measurements cannot be used directly to discriminate UXO from clutter. In this paper, we implement independent component analysis (ICA), a well-established blind source separation (BSS) technique, to recover the unobserved object signatures from the mixed measurement data obtained by simulating electromagnetic induction (EMI) sensor data, and then use the recovered signatures for UXO/clutter discrimination. Discrimination performance depends on multiple factors, including the number of clutter objects in proximity to the UXO, the separation distance between the UXO and clutter, and the number of mixed measurements available. Simulation results are presented illustrating the impact of these factors on discrimination performance.     

A hybrid neural network model for typhoon-rainfall forecasting

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.     

Pitfalls of 1D inversion of small-loop electromagnetic data for detecting man-made objects

In order to interpret field data from small-loop electromagnetic (EM) instruments with fixed source–receiver separation, 1D inversion method is commonly used due to its efficiency with regard to computation costs. This application of 1D inversion is based on the assumption that small-offset broadband EM signals are insensitive to lateral resistivity variation. However, this assumption can be false when isolated conductive bodies such as man-made objects are embedded in the earth. Thus, we need to clarify the applicability of the 1D inversion method for small-loop EM data. In order to systematically analyze this conventional inversion approach, we developed a 2D EM inversion algorithm and verified this algorithm with a synthetic EM data set. 1D and 2D inversions were applied to synthetic and field EM data sets. The comparison of these inversion results shows that the resistivity distribution of the subsurface constructed by the 1D inversion approach can be distorted when the earth contains man-made objects, because they induce drastic variation of the resistivity distribution. By analyzing the integrated sensitivity of the small-loop EM method, we found that this pitfall of 1D inversion may be caused by the considerable sensitivity of the small-loop EM responses to lateral resistivity variation. However, the application of our 2D inversion algorithm to synthetic and field EM data sets demonstrate that the pitfall of 1D inversion due to man-made objects can be successfully alleviated. Thus, 2D EM inversion is strongly recommended for detecting conductive isolated bodies, such as man-made objects, whereas this approach may not always be essential for interpreting the EM field data.     

Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network

A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial–temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of clusters based on the Haar wavelet, which captures the occurrence of extreme hydrological events, appear in blocks while those classified by the Morlet wavelet, which indicates periodicity changes and describes fine structures, appear in strips that cross the island of Taiwan. Principal component analysis is applied to the precipitation data of each cluster. The first principal components explain 62–90% of the total variation of data. Characteristics of precipitation data for each cluster are explored using scalogram analysis. The results show that both extreme hydrological events and periodicity changes appear in the spatial and temporal precipitation data but with different characteristics for each cluster. Recognizing homogeneous hydrologic regions and identifying the associated precipitation characteristics improves the efficiency of water resources management in adapting to climate change, preventing the degradation of the water environment, and reducing the impact of climate-induced disasters. Measures for countering the stress of precipitation variation for water resources management are provided.     

Improvement of HVSR technique by self-organizing map (SOM) analysis

Data interpretation is one of the most important and thorny tasks in geosciences. Difficulties occur especially in non-invasive geophysical techniques and/or when the data that have to be analyzed are multidimensional, non-linear and highly noisy. Another important task is to ensure an efficient automatic data analysis, in order to allow a data interpretation as independent as possible from any a priori knowledge. This paper describes the post-processing application of a kind of neural network (self-organizing map, SOM) to the identification of the fundamental HVSR frequency of a given site. SOM results can be represented as two-dimensional maps, with a non-parametric mapping that projects the high dimensional original dataset in a fashion that provides both an unsupervised clustering and a highly visual representation of the data relationships. This innovative application of the SOM algorithm is presented with a case study related to the characterization of a mineral deposit.     

Deep exploration using long-offset transient electromagnetics: interpretation of field data in time and frequency domain

In the framework of the Deep Electromagnetic Soundings for Mineral Exploration project, we conducted ground-based long-offset transient-electromagnetic measurements in a former mining area in eastern Thuringia, Germany. The large-scale survey resulted in an extensive dataset acquired with multiple high-power transmitters and a high number of electric and magnetic field receivers. The recorded data exhibit a high data quality over several decades of time and orders of magnitude. Although the obtained subsurface models indicate a strong multi-dimensional subsurface with variations in resistivity over three orders of magnitude, the electrical field step-on transients are well fitted using a conventional one-dimensional inversion. Due to superimposed induced polarization effects, the transient step-off data are not interpretable with conventional electromagnetic inversion. For further interpretation in one and two dimensions, a new approach to evaluate the long-offset transient-electromagnetic data in frequency domain is realized. We present a detailed workflow for data processing in both domains and give an overview of technical obstructions that can occur in one domain or the other. The derived one-dimensional inversion models of frequency-domain data show strong multi-dimensional effects and are well comparable with the conventional time domain inversion results. To adequately interpret the data, a 2.5D frequency-domain inversion using the open source algorithm MARE2DEM (Modeling with Adaptively Refined Elements for 2-D EM) is carried out. The inversion leads to a consistent subsurface model with shallow and deep conductive structures, which are confirmed by geology and additional geophysical surveys.     

Identification and classification of multiple reflections with self-organizing maps

Artificial neural networks can be used effectively to identify and classify multiple events in a seismic data set. We use a specialized neural network, a self-organizing map (SOM), that tries to establish rules for the characterization of the physical problem. Selected seismic data attributes from CMP gathers are used as input patterns, such that the SOM arranges the data to form clusters in an abstract space. We show with synthetic and real data how the SOM can identify and classify primaries and multiples, and how it can classify the various types of multiple corresponding to a certain generating mechanism in the subsurface.     

3D parallel inversion of time-domain airborne EM data

To improve the inversion accuracy of time-domain airborne electromagnetic data, we propose a parallel 3D inversion algorithm for airborne EM data based on the direct Gauss–Newton optimization. Forward modeling is performed in the frequency domain based on the scattered secondary electrical field. Then, the inverse Fourier transform and convolution of the transmitting waveform are used to calculate the EM responses and the sensitivity matrix in the time domain for arbitrary transmitting waves. To optimize the computational time and memory requirements, we use the EM “footprint” concept to reduce the model size and obtain the sparse sensitivity matrix. To improve the 3D inversion, we use the OpenMP library and parallel computing. We test the proposed 3D parallel inversion code using two synthetic datasets and a field dataset. The time-domain airborne EM inversion results suggest that the proposed algorithm is effective, efficient, and practical.     

STATISTICAL MULTIVARIATE ANALYSIS OF AIRBORNE GEOPHYSICAL DATA ON THE SE BORDER OF THE CENTRAL LAPLAND GREENSTONE COMPLEX*

Statistical multivariate methods for the integrated processing of airborne geophysical data were tested. The data consisted of magnetic, electromagnetic and gamma radiation measurements, to which cluster analysis, principal components analysis and discriminant analysis were applied. Also, auxiliary variables were derived from the original ones and their value was tested. Although the frequency distributions of the data do not favour statistical analysis, the practical results are acceptable. Principal component analyses show geological and technical aspects that are difficult to obtain from the original observations. In cluster analyses, the sources of measured fields control the grouping of variables. Discriminant analysis was applied to the automatic identification of rocks by geophysical data. The rocks investigated are metasediments and metavolcanics, some magnetic and others conductive. When all available geophysical data were included, correct identifications were made in more than 60% of cases. In particular, gamma ray observations were found to improve the discrimination of non-magnetic and non-conductive rocks. The geophysical similarity of rocks studied by cluster analysis depends on electrical and magnetic properties as well as on their origin; the content of radioactive elements in turn is related to the origin.     

Sequential analysis of hydrochemical data for watershed characterization

A methodology for characterizing the hydrogeology of watersheds using hydrochemical data that combine statistical, geochemical, and spatial techniques is presented. Surface water and ground water base flow and spring runoff samples (180 total) from a single watershed are first classified using hierarchical cluster analysis. The statistical clusters are analyzed for spatial coherence confirming that the clusters have a geological basis corresponding to topographic flowpaths and showing that the fractured rock aquifer behaves as an equivalent porous medium on the watershed scale. Then principal component analysis (PCA) is used to determine the sources of variation between parameters. PCA analysis shows that the variations within the dataset are related to variations in calcium, magnesium, SO4, and HCO3, which are derived from natural weathering reactions, and pH, NO3, and chlorine, which indicate anthropogenic impact. PHREEQC modeling is used to quantitatively describe the natural hydrochemical evolution for the watershed and aid in discrimination of samples that have an anthropogenic component. Finally, the seasonal changes in the water chemistry of individual sites were analyzed to better characterize the spatial variability of vertical hydraulic conductivity. The integrated result provides a method to characterize the hydrogeology of the watershed that fully utilizes traditional data.     

A New Approach for Border Detection of the Dumluca (Turkey) Iron Ore Area: Wavelet Cellular Neural Networks

Anomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks (Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics. At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then, Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling results.     

Performing cluster analysis and discrimination analysis of hydrological factors in one step

Based on self-organizing map, a method that can perform cluster analysis and discrimination analysis in one step is proposed in this paper. Using the proposed method, one can view the relative topological relationships of input patterns, determine the proper number of clusters, and assign unknown patterns to known clusters without losing any information of input patterns. Regarding the capability of determining the proper number of clusters, the proposed method is superior to conventional cluster analysis. The discrimination results also show that the assignments of unknown patterns to known clusters are reasonable using the proposed method. The advantages of the proposed method are also demonstrated by an application to the hydrological factors affecting low-flow duration curves in southern Taiwan.     

航空电磁拟三维模型空间约束反演

为了克服时间域航空电磁数据单点反演结果中常见的电阻率或层厚度横向突变造成数据难以解释的问题,通过引入双向约束实现航空电磁拟三维空间约束反演.除考虑沿测线方向相邻测点之间的横向约束外,同时还考虑了垂直测线方向测点在空间上的相互约束.为此,首先设计拟三维模型中固定层厚和可变层厚两种空间约束反演方案,然后通过在目标函数中引入沿测线和垂直测线方向上的模型参数约束矩阵,并使用L-BFGS算法使目标函数最小化,获得最优拟三维模型空间反演解.基于理论模型和实测数据反演,对单点反演与两种空间约束反演方案的有效性进行比较,证明本文空间约束反演算法对于噪声的压制效果好,反演的界面连续光滑,同时内存需求和反演时间少,是一种快速有效的反演策略.     

Characterisation of glacial sediments using geophysical methods for groundwater source protection

A sequence of glacial and alluvial deposits overlying the Cretaceous Chalk in Eastern England was characterised using two geophysical techniques: electrical resistivity imaging and electromagnetic (EM) induction. Extensive geological data were available from trenching and boreholes. Synthetic modelling of the electrical resistivity imaging technique was undertaken to identify its limitations and to optimise survey design. The EM induction method provided a quick and cost-effective reconnaissance technique for identifying large-scale lateral variation in lithology, and for siting resistivity profiles and further boreholes. The resistivity imaging technique provided detailed information on the vertical continuity of permeable units, and was able to identify permeable pathways through the sequence. Certain limitations in detecting thin sand or gravel layers underlying electrically conductive clay were seen in both the synthetic and field data. Nevertheless, the study shows that knowledge of these limitations allows interpretation for the purpose of groundwater vulnerability assessment, given that an appropriate amount of invasive investigation has been conducted.     

Characterisation of glacial sediments using geophysical methods for groundwater source protection

A sequence of glacial and alluvial deposits overlying the Cretaceous Chalk in Eastern England was characterised using two geophysical techniques: electrical resistivity imaging and electromagnetic (EM) induction. Extensive geological data were available from trenching and boreholes. Synthetic modelling of the electrical resistivity imaging technique was undertaken to identify its limitations and to optimise survey design. The EM induction method provided a quick and cost-effective reconnaissance technique for identifying large-scale lateral variation in lithology, and for siting resistivity profiles and further boreholes. The resistivity imaging technique provided detailed information on the vertical continuity of permeable units, and was able to identify permeable pathways through the sequence. Certain limitations in detecting thin sand or gravel layers underlying electrically conductive clay were seen in both the synthetic and field data. Nevertheless, the study shows that knowledge of these limitations allows interpretation for the purpose of groundwater vulnerability assessment, given that an appropriate amount of invasive investigation has been conducted.     

Application of a library based method to time domain electromagnetic data for the identification of unexploded ordnance

A simple library based algorithm for the identification of unexploded ordnance is tested on time domain electromagnetic data. A library of polarization tensors is generated from data acquired on a test stand over a collection of different UXO. The objective of the algorithm is to determine which target of the library is most likely to have produced an observed data anomaly. For each target in our library a non-linear inverse problem is solved for the position and orientation that minimizes the least-squares difference between the observed data anomaly and the data predicted from each target. This technique avoids direct inversion for polarization tensor, making it feasible for cases where sensor data quality may not be sufficient to support confident estimation of model parameters. For cases where the background soil response is significant, we also invert for the t⁻¹ decay characteristic of viscous remnant magnetic soil. We present results of preliminary tests of the library technique to Geonics EM63 time domain electromagnetic data collected on a test plot seeded with UXO. These tests demonstrate an excellent ability to accurately identify isolated targets. Misidentification of single targets occur when data anomalies have low signal to noise ratios or when targets within the library have similar polarization tensors.     

Detection of point scatterers using diffraction imaging and deep learning

Diffracted waves carry high-resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high-accuracy and high-resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.     

Mining,Environmental, Petroleum,and Engineering Industry Applications of Electromagnetic Techniques in Geophysics

Electromagnetic (EM) techniques are extremely important as a direct detection geophysical tool utilized in the base metal industry. They were developed in countries such as Canada, whose thin conductive weathering overburden did not hamper the penetration of EM signals and enabled exploration to depths on the order of 300 m. As a result, EM techniques were used widely in North America and Scandinavia for many years before they became common in countries with a thick conductive overburden, such as Australia. The 1980s and 1990s have seen the use of EM methods move from anomaly finding to mapping, as well as the development of better, faster and more accurate computer modelling algorithms. A review of EM papers, for the years 1998 to 2002, showed that most dealt with EM techniques as mapping tools. Airborne, ground and marine EM techniques are still being developed, as are data processing and interpretation software. The advent of robust 2-D and 3-D computer modelling and inversion algorithms has led to the acceptance of EM methods as a mapping tool for many environmental and petroleum industry applications, a trend which is expected to increase.     

自适应Chirplet变换及其在探地雷达目标探测中的应用(英文)

探地雷达不仅能够探测金属目标体,而且能够探测非金属目标体,而成为UX0和地雷探测的一种重要的浅部地球物理方法。但是在地雷和UX0探测中,目标体埋藏深度浅,在探地雷达数据信噪比较低情况下,地表和土壤层的反射严重干扰对目标体的拾取。本文采用自适用Chirplet变换来消除地表层和土壤层变化的干扰,并在Radon—Wigner分布的基础上,采用自适用Chirplet变换来拾取目标体的信号。通过对实际探测实验数据应用证明,本方法处理结果比传统的偏移方法具有较高的信噪比,并能清晰地提取目标体信号。