Rapid multi-scale analysis of near-surface geophysical anomaly maps: Application to an archaeo-geophysical data set

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

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Petrophysically and geologically guided multi-physics inversion using a dynamic Gaussian mixture model

Geophysical Journal International ◽

10.1093/gji/ggaa378 ◽

2020 ◽

Vol 224 (1) ◽

pp. 40-68 ◽

Cited By ~ 1

Author(s):

Thibaut Astic ◽

Lindsey J Heagy ◽

Douglas W Oldenburg

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Quantitative Information ◽

Geophysical Data ◽

Magnetic Data ◽

Data Sets ◽

Data Set ◽

Geophysical Surveys ◽

Geological Information

SUMMARY In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.

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Cooperative inversion of 2D geophysical data sets: A zonal approach based on fuzzy c-means cluster analysis

Geophysics ◽

10.1190/1.2670341 ◽

2007 ◽

Vol 72 (3) ◽

pp. A35-A39 ◽

Cited By ~ 78

Author(s):

Hendrik Paasche ◽

Jens Tronicke

Keyword(s):

Cluster Analysis ◽

Geophysical Methods ◽

Geophysical Data ◽

Data Sets ◽

Data Set ◽

Near Surface ◽

Zonal Model ◽

Subsurface Structures ◽

Single Input ◽

Cooperative Inversion

In many near-surface geophysical applications, it is now common practice to use multiple geophysical methods to explore subsurface structures and parameters. Such multimethod-based exploration strategies can significantly reduce uncertainties and ambiguities in geophysical data analysis and interpretation. We propose a novel 2D approach based on fuzzy [Formula: see text]-means cluster analysis for the cooperative inversion of disparate data sets. We show that this approach results in a single zonal model of subsurface structures in which each zone is characterized by a set of different parameters. This finding implies that no further structural interpretation of geophysical parameter fields is needed, which is a major advantage compared with conventional inversions that rely on a single input data set and cooperative inversion approaches.

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A Review of Geophysical Modeling Based on Particle Swarm Optimization

Surveys in Geophysics ◽

10.1007/s10712-021-09638-4 ◽

2021 ◽

Author(s):

Francesca Pace ◽

Alessandro Santilano ◽

Alberto Godio

Keyword(s):

Particle Swarm Optimization ◽

Inverse Modeling ◽

Critical Evaluation ◽

Particle Swarm ◽

Geophysical Data ◽

Magnetic Data ◽

Data Sets ◽

Swarm Optimization ◽

Geophysical Modeling ◽

Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

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A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the 2-D fast Fourier transform

Geophysical Journal International ◽

10.1093/gji/ggaa372 ◽

2020 ◽

Vol 223 (2) ◽

pp. 1378-1397

Author(s):

Rosemary A Renaut ◽

Jarom D Hogue ◽

Saeed Vatankhah ◽

Shuang Liu

Keyword(s):

Fourier Transform ◽

Fast Fourier Transform ◽

Linear Systems ◽

Large Scale ◽

Surface Measurement ◽

Magnetic Data ◽

Uniform Grid ◽

Data Sets ◽

Inversion Algorithm ◽

Data Set

SUMMARY We discuss the focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid. For the uniform grid, the model sensitivity matrices have a block Toeplitz Toeplitz block structure for each block of columns related to a fixed depth layer of the subsurface. Then, all forward operations with the sensitivity matrix, or its transpose, are performed using the 2-D fast Fourier transform. Simulations are provided to show that the implementation of the focusing inversion algorithm using the fast Fourier transform is efficient, and that the algorithm can be realized on standard desktop computers with sufficient memory for storage of volumes up to size n ≈ 106. The linear systems of equations arising in the focusing inversion algorithm are solved using either Golub–Kahan bidiagonalization or randomized singular value decomposition algorithms. These two algorithms are contrasted for their efficiency when used to solve large-scale problems with respect to the sizes of the projected subspaces adopted for the solutions of the linear systems. The results confirm earlier studies that the randomized algorithms are to be preferred for the inversion of gravity data, and for data sets of size m it is sufficient to use projected spaces of size approximately m/8. For the inversion of magnetic data sets, we show that it is more efficient to use the Golub–Kahan bidiagonalization, and that it is again sufficient to use projected spaces of size approximately m/8. Simulations support the presented conclusions and are verified for the inversion of a magnetic data set obtained over the Wuskwatim Lake region in Manitoba, Canada.

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Data Sets

Hurricane Climatology ◽

10.1093/oso/9780199827633.003.0009 ◽

2013 ◽

Author(s):

James B. Elsner ◽

Thomas H. Jagger

Keyword(s):

Model Building ◽

Careful Analysis ◽

Logical Structure ◽

Fortran Program ◽

Data Sets ◽

Data Set ◽

Near Surface ◽

Wind Speeds ◽

The North ◽

The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

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Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data

BMC Genomics ◽

10.1186/s12864-020-07038-3 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Da Xu ◽

Jialin Zhang ◽

Hanxiao Xu ◽

Yusen Zhang ◽

Wei Chen ◽

...

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Therapeutic Targets ◽

Genomic Data ◽

Data Sets ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Model Learning ◽

Data Set ◽

Multi Scale

Abstract Background The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. Results In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. Conclusions The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

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Fennec dust forecast intercomparison over the Sahara in June 2011

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-6977-2016 ◽

2016 ◽

Vol 16 (11) ◽

pp. 6977-6995 ◽

Cited By ~ 16

Author(s):

Jean-Pierre Chaboureau ◽

Cyrille Flamant ◽

Thibaut Dauhut ◽

Cécile Kocha ◽

Jean-Philippe Lafore ◽

...

Keyword(s):

High Resolution ◽

Surface Wind ◽

Dust Emission ◽

Airborne Lidar ◽

Data Sets ◽

Density Currents ◽

Data Set ◽

Near Surface ◽

Western Sahara ◽

Deep Well

Abstract. In the framework of the Fennec international programme, a field campaign was conducted in June 2011 over the western Sahara. It led to the first observational data set ever obtained that documents the dynamics, thermodynamics and composition of the Saharan atmospheric boundary layer (SABL) under the influence of the heat low. In support to the aircraft operation, four dust forecasts were run daily at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara. At monthly scale, large aerosol optical depths (AODs) were forecast over the Sahara, a feature observed by satellite retrievals but with different magnitudes. The AOD intensity was correctly predicted by the high-resolution models, while it was underestimated by the low-resolution models. This was partly because of the generation of strong near-surface wind associated with thunderstorm-related density currents that could only be reproduced by models representing convection explicitly. Such models yield emissions mainly in the afternoon that dominate the total emission over the western fringes of the Adrar des Iforas and the Aïr Mountains in the high-resolution forecasts. Over the western Sahara, where the harmattan contributes up to 80 % of dust emission, all the models were successful in forecasting the deep well-mixed SABL. Some of them, however, missed the large near-surface dust concentration generated by density currents and low-level winds. This feature, observed repeatedly by the airborne lidar, was partly forecast by one high-resolution model only.

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High-resolution numerical modeling of mesoscale island wakes and sensitivity to static topographic relief data

Geoscientific Model Development ◽

10.5194/gmd-8-2645-2015 ◽

2015 ◽

Vol 8 (8) ◽

pp. 2645-2653 ◽

Cited By ~ 10

Author(s):

C. G. Nunalee ◽

Á. Horváth ◽

S. Basu

Keyword(s):

High Resolution ◽

Vortex Shedding ◽

Weather Prediction ◽

Surface Wind ◽

Wrf Model ◽

Data Sets ◽

Data Set ◽

Near Surface ◽

Nwp Model ◽

Height Data

Abstract. Recent decades have witnessed a drastic increase in the fidelity of numerical weather prediction (NWP) modeling. Currently, both research-grade and operational NWP models regularly perform simulations with horizontal grid spacings as fine as 1 km. This migration towards higher resolution potentially improves NWP model solutions by increasing the resolvability of mesoscale processes and reducing dependency on empirical physics parameterizations. However, at the same time, the accuracy of high-resolution simulations, particularly in the atmospheric boundary layer (ABL), is also sensitive to orographic forcing which can have significant variability on the same spatial scale as, or smaller than, NWP model grids. Despite this sensitivity, many high-resolution atmospheric simulations do not consider uncertainty with respect to selection of static terrain height data set. In this paper, we use the Weather Research and Forecasting (WRF) model to simulate realistic cases of lower tropospheric flow over and downstream of mountainous islands using the default global 30 s United States Geographic Survey terrain height data set (GTOPO30), the Shuttle Radar Topography Mission (SRTM), and the Global Multi-resolution Terrain Elevation Data set (GMTED2010) terrain height data sets. While the differences between the SRTM-based and GMTED2010-based simulations are extremely small, the GTOPO30-based simulations differ significantly. Our results demonstrate cases where the differences between the source terrain data sets are significant enough to produce entirely different orographic wake mechanics, such as vortex shedding vs. no vortex shedding. These results are also compared to MODIS visible satellite imagery and ASCAT near-surface wind retrievals. Collectively, these results highlight the importance of utilizing accurate static orographic boundary conditions when running high-resolution mesoscale models.

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Limitations on digital filtering of the DNAG magnetic data set for the conterminous U.S.

Geophysics ◽

10.1190/1.1443511 ◽

1993 ◽

Vol 58 (9) ◽

pp. 1281-1296 ◽

Cited By ~ 12

Author(s):

V. J. S. Grauch

Keyword(s):

Spatial Frequency ◽

North American ◽

Digital Filtering ◽

Magnetic Data ◽

Data Sets ◽

Frequency Content ◽

Long Distance ◽

High Quality ◽

Data Set ◽

The U.S

The magnetic data set compiled for the Decade of North American Geology (DNAG) project presents an important digital data base that can be used to examine the North American crust. The data represent a patchwork from many individual airborne and marine magnetic surveys. However, the portion of data for the conterminous U.S. has problems that limit the resolution and use of the data. Now that the data are available in digital form, it is important to describe the data limitations more specifically than before. The primary problem is caused by datum shifts between individual survey boundaries. In the western U.S., the DNAG data are generally shifted less than 100 nT. In the eastern U.S., the DNAG data may be shifted by as much as 300 nT and contain regionally shifted areas with wavelengths on the order of 800 to 1400 km. The worst case is the artificial low centered over Kentucky and Tennessee produced by a series of datum shifts. A second significant problem is lack of anomaly resolution that arises primarily from using survey data that is too widely spaced compared to the flight heights above magnetic sources. Unfortunately, these are the only data available for much of the U.S. Another problem is produced by the lack of common observation surface between individual pieces of the U.S. DNAG data. The height disparities introduce variations in spatial frequency content that are unrelated to the magnetization of rocks. The spectral effects of datum shifts and the variation of spatial frequency content due to height disparities were estimated for the DNAG data for the conterminous U.S. As a general guideline for digital filtering, the most reliable features in the U.S. DNAG data have wavelengths roughly between 170 and 500 km, or anomaly half‐widths between 85 and 250 km. High‐quality, large‐region magnetic data sets have become increasingly important to meet exploration and scientific objectives. The acquisition of a new national magnetic data set with higher quality at a greater range of wavelengths is clearly in order. The best approach is to refly much of the U.S. with common specifications and reduction procedures. At the very least, magnetic data sets should be remerged digitally using available or newly flown long‐distance flight‐line data to adjust survey levels. In any case, national coordination is required to produce a consistent, high‐quality national magnetic map.

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Connecting onshore-offshore Campos Basin structures: Interpretation of high-resolution airborne magnetic data

Interpretation ◽

10.1190/int-2014-0007.1 ◽

2014 ◽

Vol 2 (4) ◽

pp. SJ35-SJ45 ◽

Cited By ~ 5

Author(s):

Juarez Lourenço ◽

Paulo T. L. Menezes ◽

Valeria C. F. Barbosa

Keyword(s):

Source Rocks ◽

Magnetic Data ◽

Data Sets ◽

Hydrocarbon Migration ◽

Data Set ◽

Campos Basin ◽

Turbidite Sedimentation ◽

Edge Detection Method ◽

Transfer Faults ◽

Airborne Magnetic

We interpreted northwest-trending transfer faults whose extensions are not entirely mapped in the Precambrian basement of the onshore and offshore Campos Basin. To enhance the subtle northwest–southeast lineaments not clearly seen in the total-field data, we reprocessed and merged two airborne magnetic data sets aiming at producing a single merged magnetic data set. Next, we applied a directional filter to these integrated magnetic data. Finally, we applied a multiscale edge detection method to these filtered data. This combination allowed the detection of edges and ridges that are used to produce several northwest–southeast lineations. We interpreted these northwest-trending lineations as magnetic expressions of transfer faults that cut across the onshore adjacent basement of the Campos Basin to the shallow and deep Campos Basin waters. These interpreted northwest-trending faults suggested the continuity of the known northwest-striking transfer faults in the deep Campos Basin waters toward the shallow Campos Basin waters and the adjacent continent. Moreover, our interpreted northwest-trending faults revealed the control of several known oilfields in the Campos Basin. This result supported the hypothesis of the influence of the northwest–southeast-trending transfer faults on the petroleum system of Campos Basin, which were reactivated in the Tertiary providing a pathway for the turbidite sedimentation, reworking, and redistribution of several deepwater reservoirs. In addition, it was hypothesized that this faulting system controlled the hydrocarbon migration paths from the presalt source rocks through salt windows into basal suprasalt layers.

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