Field Of Geosciences Research Articles

Exact Conditioning of Regression Random Forest for Spatial Prediction

Abstract Regression random forest is becoming a widely-used machine learning technique for spatial prediction that shows competitive prediction performance in various geoscience fields. Like other popular machine learning methods for spatial prediction, regression random forest does not exactly honor the response variable’s measured values at sampled locations. However, competitor methods such as regression-kriging perfectly fit the response variable’s observed values at sampled locations by construction. Exactly matching the response variable’s measured values at sampled locations is often desirable in many geoscience applications. This paper presents a new approach ensuring that regression random forest perfectly matches the response variable’s observed values at sampled locations. The main idea consists of using the principal component analysis to create an orthogonal representation of the ensemble of regression tree predictors resulting from the traditional regression random forest. Then, the exact conditioning problem is reformulated as a Bayes-linear-Gauss problem on principal component scores. This problem has an analytical solution making it easy to perform Monte Carlo sampling of new principal component scores and then reconstruct regression tree predictors that perfectly match the response variable’s observed values at sampled locations. The reconstructed regression tree predictors’ average also precisely matches the response variable’s measured values at sampled locations by construction. The proposed method’s effectiveness is illustrated on the one hand using a synthetic dataset where the ground-truth is available everywhere within the study region, and on the other hand, using a real dataset comprising southwest England’s geochemical concentration data. It is compared with the regression-kriging and the traditional regression random forest. It appears that the proposed method can perfectly fit the response variable’s measured values at sampled locations while achieving good out of sample predictive performance comparatively to regression-kriging and traditional regression random forest.

The Complicated Link Between Material Properties and Microfracture Density for an Underground Explosion in Granite

AbstractGeophysical techniques are often implemented as quick and inexpensive ways to locate and characterize fractures in the subsurface, which is important for a number of geoscience fields. Seismic velocities are the most widely used proxies for identification of fractures, but the correlation is not always well defined. Here we present material property data: unconfined compressive strength, bulk density (ρ), Young's modulus (E), Poisson's ratio (ν), P wave velocity (Vp), and S wave velocity (Vs), in conjunction with microfracture densities measured on samples of granite collected before and after underground chemical explosions. Results indicate the relationship between fractures and material properties is complex, even in this single‐lithology environment. We interpret that this complexity arises from varying fracture mechanisms (e.g., dilation‐inducing fractures vs. compression‐inducing fractures) in different parts of the core, due to differences in stress conditions. Additional complexity may result from chemical interactions between the fresh fractures and the fluids in the area. Water content appears to have a significant, if not dominant, role in the unconfined compressive strength of the samples. We suggest caution when using elastic property measurements as a proxy for fracturing in areas of explosion‐induced damage, or in other areas where a variety of mechanisms induce fracturing.

STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting

Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNN) or some hybrid approach mixing RNN and convolutional neural networks (CNN). In this work, we propose STConvS2S (Spatiotemporal Convolutional Sequence to Sequence Network), a deep learning architecture built for learning both spatial and temporal data dependencies using only convolutional layers. Our proposed architecture resolves two limitations of convolutional networks to predict sequences using historical data: (1) they violate the temporal order during the learning process and (2) they require the lengths of the input and output sequences to be equal. Computational experiments using air temperature and rainfall data from South America show that our architecture captures spatiotemporal context and that it outperforms or matches the results of state-of-the-art architectures for forecasting tasks. In particular, one of the variants of our proposed architecture is 23% better at predicting future sequences and five times faster at training than the RNN-based model used as a baseline.

Realization and application of geological cloud platform

ABSTRACT In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. This study aims at addressing the existing problems in geological applications, such as data sharing, data processing and computing performance. A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology. The application of the Geological Cloud Platform can be divided into two parts: 1) to nest the geological computing model in cloud platform and visualize the results and 2) to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system. Finally, we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.

Full waveform inversion by proximal Newton method using adaptive regularization

SUMMARY Regularization is necessary for solving non-linear ill-posed inverse problems arising in different fields of geosciences. The base of a suitable regularization is the prior expressed by the regularizer, which can be non-adaptive or adaptive (data-driven), smooth or non-smooth, variational-based or not. Nevertheless, tailoring a suitable and easy-to-implement prior for describing geophysical models is a non-trivial task. In this paper, we propose two generic optimization algorithms to implement arbitrary regularization in non-linear inverse problems such as full-waveform inversion (FWI), where the regularization task is recast as a denoising problem. We assess these optimization algorithms with the plug-and-play block matching (BM3D) regularization algorithm, which determines empirical priors adaptively without any optimization formulation. The non-linear inverse problem is solved with a proximal Newton method, which generalizes the traditional Newton step in such a way to involve the gradients/subgradients of a (possibly non-differentiable) regularization function through operator splitting and proximal mappings. Furthermore, it requires to account for the Hessian matrix in the regularized least-squares optimization problem. We propose two different splitting algorithms for this task. In the first, we compute the Newton search direction with an iterative method based upon the first-order generalized iterative shrinkage-thresholding algorithm (ISTA), and hence Newton-ISTA (NISTA). The iterations require only Hessian-vector products to compute the gradient step of the quadratic approximation of the non-linear objective function. The second relies on the alternating direction method of multipliers (ADMM), and hence Newton-ADMM (NADMM), where the least-squares optimization subproblem and the regularization subproblem in the composite objective function are decoupled through auxiliary variable and solved in an alternating mode. The least-squares subproblem can be solved with exact, inexact, or quasi-Newton methods. We compare NISTA and NADMM numerically by solving FWI with BM3D regularization. The tests show promising results obtained by both algorithms. However, NADMM shows a faster convergence rate than NISTA when using L-BFGS to solve the Newton system.

Preface

Zhenhao Xu 1, Xi Peng 2, 3 1Editor, Proceedings of the Third International Workshop on Environment and Geoscience (IWEG 2020); Urban Underground Space Engineering Department, Shandong University,China 2Co-Editor, Proceedings of the Third International Workshop on Environment and Geoscience (IWEG 2020); Wuhan University, China 3Email: xipeng@whu.edu.cnThe Third International Workshop on Environment and Geoscience (IWEG 2020) was planned to be held on July 18-20, 2020 in Chengdu, China. For the COVID-19 epidemic, to avoid the health risk of crowd gathering while ensuring timely academic exchange among scholars, the organizing committee decided to hold IWEG 2020 virtually/online on July 18-20, 2020 in China through the VooV Meeting platform ( 腾讯会议 in Chinese). Although held online, this online conference attracted nearly 60 scholars and experts from 3 different countries and regions in the field of Environment and Geoscience to share new research results, novel policy developments, and practical implementation experiences regarding. IWEG2020 was a successful, fruitful online academic event.We are very grateful that Professor Jie Ji from University of Science and Technology of China presided over the conference. Thanks for all participants for attending and sharing!List of Technical Program Committee is available in this PDF.

Multi-scale fracture network characterisation on carbonate platforms

Characterisation of fracture networks at different scales is challenging and important to many fields of geoscience, especially when access to multiple resolution datasets is limited. Here, we develop an integrated analysis of fracture networks on carbonate platforms using three scales of observation: small (outcrop), intermediate (airborne LiDAR) and large (3D seismic). Statistical analyses and ternary diagrams of geometrical and topological data from Cariatiz (South East Spain) and Pernambuco (East Brazil) are used to understand the relationships and distribution of fracture networks between multi-scale datasets. A variety of fracture types at each scale of observation reveal how complex fracture networks are on carbonate platforms. Our results demonstrate that fracture network properties behave differently depending on the fracture size, and that transitional scale gaps between datasets constrain fracture characterisation. Airborne LiDAR maps show that intermediate-sized fractures appear to have a better controlled orientation and a lower connectivity than smaller fractures from the same area in Cariatiz. Fracture branch length distributions fit a negative exponential or log-normal distribution for massive non-stratabound units. This work is important as it demonstrates that the use of outcrop data is a good approach to understand fracture complexity of carbonate platforms. Understanding sub-seismic fracture networks is therefore critical in quantifying fluid flow and permeability in carbonate reservoirs.

EVALUATING THE QUALITY OF PHOTOGRAMMETRIC POINT-CLOUDS IN CHALLENGING GEO-ENVIRONMENTS – A CASE STUDY IN AN ALPINE VALLEY

Abstract. Precise and accurate three-dimensional geospatial data has become increasingly available thanks to advances in both Terrestrial Laser Scanning (TLS) and Structure-from-Motion Photogrammetry (SfM). These tools provide valuable information for mapping geomorphological features and detect surface changes in mountainous environments. The exploitation of 3D point-clouds has been proven tremendously useful in the field of geosciences. It remains, however, controversial whether cost efficient photogrammetry can provide as accurate and reliable geospatial information as the significantly more expensive laser scanning or not. In this study, a rockfall case site in the territory of Obergurgl, Austria, is investigated in order to provide answers to the above question in a complex environment. The analysis includes different terrestrial photogrammetry configurations aiming to comprehensively define the strengths and limitations of terrestrial photogrammetry over TLS. The latter constitutes an optimized methodology that provides guidelines for costly future assessments as part of the site investigation phase in geohazard management. There are no doubts that compared to traditional and conventional surveying methods TLS and Photogrammetry both offer products much faster and with a much higher data density. In the current study, we show that when photogrammetry is applied following a well-defined optimized strategy, it can be potentially an adequate alternative to more costly TLS datasets for mass movement assessment and monitoring purposes.

Access Anglesey 2018: Lessons from an inclusive field course

Abstract. Traditional methods of fieldwork delivery can present learners with a range of physical, cognitive and social challenges which may subsequently hinder their ability to engage effectively with learning. We developed a residential geoscience field course designed to be physically accessible to, and socially inclusive of, a diverse range of learners including those with limited physical mobility and neurodiverse conditions. This paper presents the logistical and pedagogical challenges involved in delivering such a field course. In terms of pedagogic design scheduling, pace and timing, and the ability to access content in multiple ways were critical to ensuring that all students were included in the learning. The most effective mitigations were the simplest and benefitted the whole group. Practical interventions found to support access and inclusion for the benefit of all participants included using an audio tour-guide system to communicate with students at field locations, using a four-wheel drive vehicle to improve access to specific locations, providing alternative exercises such as prepared photomicrographs and rock specimens, providing electronic tablets with suitable apps, and selecting accommodation with accessible common-room spaces, and a dedicated quiet room.

HUMAN IMPACTS ON LANDSCAPES: LESSONS OF CITIES FROM THE PAST

The first cities emerged in the Middle East at the end of the 4th millennium BC. Studies in the field of archaeology, geomorphology, geoscience and history allow us to understand which types of hazards were affecting the cities, and how they had an impact on landscapes in the past, in the Middle East, but also in other parts of the world. There is much to be gained: these studies are fundamental to a better understanding of present-day hazards, to urban development, but also to remembering our heritage. Cities have always been susceptible to nature’s risks and natural disasters but have also – through urban development and through the proximity of great numbers of human beings –, generated their own specific hazards.

Inspiration, inoculation, and introductions are all critical to successful mentorship for undergraduate women pursuing geoscience careers

Diversity in the geosciences is low despite efforts to improve the representation of different groups in society, for example in terms of gender. Specifically, women are underrepresented in recruitment and retention at every stage of the academic to professional pipeline. Mentoring programs can improve women’s motivation and persistence in science, technology, engineering and mathematics (STEM) career pathways. However, mentorship programs consist of multiple components that vary in complexity and cost, which can limit scalability. Here we present results from a randomized experiment with 158 undergraduate women majoring in a geoscience field to identify the critical elements of a successful mentorship program. The combination of three factors was necessary to increase mentoring, motivation, and persistence: inspiration through exposure to geoscience careers via female role models, inoculation through training on how to grow their mentor network and overcome obstacles, and an introduction to a local female geoscientist mentor.

Recognition of geochemical anomalies using a deep variational autoencoder network

Deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level, and non-linear geospatial data and for extracting previously unknown patterns related to geological processes. In this study, a deep variational autoencoder (VAE) network was used to extract features related to mineralization; and these features were then integrated as a anomaly map in support of mineral exploration based on geochemical exploration data, which consist of Cu, Pb, Mn, Zn and Fe2O3. Various experiments were conducted to determine the optimal parameters of the VAE. The structure of the VAE, in which the network depth and number of hidden units were 24–12-3-12-24, was built to recognize geochemical anomalies related to Fe polymetallic mineralization in the southwest Fujian Province, China. The geochemical anomalies recognized by the VAE show a close spatial correlation with known Fe polymetallic deposits. Meanwhile, the areas with high probability are located in or around the Yanshanian intrusions and the contact zones of the Carboniferous–Permian formation and Yanshanian intrusions. These results suggest that the anomalous areas identified by the VAE are meaningful for mineral exploration.

First Authorship Gender Gap in the Geosciences

AbstractAlthough gender parity has been reached at the graduate level in the geosciences, women remain a minority in faculty positions. First authorship of peer‐reviewed scholarship is a measure of academic success and is often used to project potential in the hiring process. Given the importance of first author publications for hiring and advancement, we sought to quantify whether women are underrepresented as first authors relative to their representation in the field of geoscience. We compiled first author names across 13 leading geoscience journals from January 2013 to April 2019 (n = 35,183). Using a database of 216,286 names from 79 countries, across 89 languages, we classified the likely gender associated with each author's given (first) name. We also estimated the gender distribution of authors who publish using only initials, which may itself be a strategy employed by some women to preempt perceived (and actual) gender bias in the publication process. Female names represent 13–30% of all first authors in our database and are substantially underrepresented relative to the proportion of women in early career positions (30–50%). The proportion of female‐name first authors varies substantially by subfield, reflecting variation in representation of women across geoscience subdisciplines. In geoscience, the quantification of this first authorship gender gap supports the hypothesis that the publication process—namely, achievement or allocation of first authorship—is biased by social factors, which may modulate career success of women in the sciences.

Probing Microbial Extracellular Respiration Ability Using Riboflavin.

Electrochemically active bacteria (EAB) are capable of extracellular electron transfer (EET) to insoluble metal oxides, and thus play a great role in the fields of environment, energy, and geosciences. However, rapid and accurate quantification of the EET ability of EAB is still challenging. In this work, we develop a riboflavin-based fluorescence method for facile, accurate, and in situ measurement of the EET ability of EAB. This method is successfully used to quantify the single-cellular EET ability of Geobacter sulfurreducens DL-1 (60.29 ± 13.02 fA) and Shewanella oneidensis MR-1 (2.11 ± 0.47 fA), the two widely present EAB in the environment. It also enables quantitative identification of EET-related c-type cytochromes in the outer membrane of S. oneidensis MR-1. This method provides a useful tool to rapidly identify EAB in diverse environments and elucidate their electron transfer mechanisms.

Undergraduate learning in the field: Designing experiences, assessing outcomes, and exploring future opportunities

Field learning experiences reach tens of thousands of undergraduate students annually, constituting their importance as components of undergraduate education and potential pathways for STEM education. Reports and planning efforts by national entities have highlighted the need to ground undergraduate field learning experiences in evidence-based practices, and to better understand the impacts of these experiences on students. In this study we describe the results of a national survey aimed at better understanding how instructors and directors at biological field stations, marine laboratories, and geoscience field camps are thinking about and designing programs, including learning strategies, student support, desired student outcomes, student assessment and program evaluation. This study is based on an online survey distributed in 2018 to a sample of directors and educators representing 163 undergraduate field learning experiences. The study achieved a satisfactory response rate of 31% (n = 563). The results of the study provide guidance on where support for improvement and research efforts should focus, including more intentional program design that considers student-centered and inclusive approaches and basic research on the impact of undergraduate field learning experiences on student learning more broadly (not just investigated in one program or course), both in terms of what the students learn (broadly defined) as well as how they learn, taking into account affective and cognitive gains. Such research can make productive use of the diversity of program types to investigate the link between student outcomes and student experiences.

Characteristics, influencing factors, and prediction of fractures in weathered crust karst reservoirs: A case study of the Ordovician Majiagou Formation in the Daniudi Gas Field, Ordos Basin, China

The study of fractures in karst reservoirs is a hot spot in the field of geosciences. In this article, we conducted a systematic study on the characteristics, influencing factors, and prediction methods of karst reservoir fractures of the Ordovician Majiagou Formation in the Daniudi Gas Field, China. Both weathering and tectonic fractures are mainly developed in the Ma 5 member of the Majiagou Formation. The former is categorized into surface weathering, steep wall tensile and karst collapse fractures, and the latter is made of tensile and shear fractures. The palaeo‐temperatures of the fracture filling inclusions range from 90 to 140°C, peaking at 111–120°C, which are derived from oxygen isotopes of −7.9 to −17.5‰, with average −14.1‰ from the same filling inclusions. Integrations of regional tectonic activities, inclusion tests, and stable isotope analysis recognizes four formation periods of fractures since the Caledonian. The factors controlling the development degree of the karst reservoir fractures include lithology, petrophysical properties, rock layer thickness, structural deformation strength, and karst palaeomorphology. The buckling thin plate simulation results show a good positive correlation between the maximum principal stress and tensile fracture density. The same positive correlation is also observed between the shear stress and fracture density. The ancient karst landforms have strongly affected the distribution of weathering fractures. Surficial weathering fractures occur typically in the weathered residual monadnocks, while steep wall tension ruptures in areas near geomorphic steep ridges, and karst collapse fractures in karst residual monadnocks and karst pit areas.

Conditioning Multi-Gaussian Groundwater Flow Parameters to Transient Hydraulic Head and Flowrate Data With Iterative Ensemble Smoothers: A Synthetic Case Study

Over the last decade, data assimilation methods based on the ensemble Kalman filter (EnKF) have been particularly explored in various geoscience fields to solve inverse problems. Although this type of ensemble methods can handle high-dimensional systems, they assume that the errors coming from whether the observations or the numerical model are multivariate Gaussian. To handle existing nonlinearities between the observations and the variables to estimate, iterative methods have been proposed. In this paper, we investigate the feasibility of using the ensemble smoother and two iterative variants for the calibration of a synthetic 2D groundwater model inspired by a real nuclear storage problem in France. Using the same set of sparse and transient flow data, we compare the results of each method when employing them to condition an ensemble of multi-Gaussian groundwater flow parameter fields. In particular, we explore the benefit of transforming the state observations to improve the parameter identification performed by one of the two tested algorithms. Despite the favorable case of a multi-Gaussian parameter distribution considered, we show the importance of defining an ensemble size of at least 200 to obtain sufficiently accurate parameter and uncertainty estimates for the groundwater flow inverse problem considered.

From Grand Canyon to Yosemite: Lessons learned from the development and assessment of digital geoscience field trips for mobile smart devices

Author(s): Bursztyn, Natalie | Abstract: How geoscience apps are enhancing students' understanding of the geology of places such as Grand Canyon and Yosemite national parks.

Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers

Outliers are encountered in all practical situations of data analysis, regardless of the discipline of application. However, the term outlier is not uniformly defined across all these fields since the differentiation between regular and irregular behaviour is naturally embedded in the subject area under consideration. Generalized approaches for outlier identification have to be modified to allow the diligent search for potential outliers. Therefore, an overview of different techniques for multivariate outlier detection is presented within the scope of selected kinds of data frequently found in the field of geosciences. In particular, three common types of data in geological studies are explored: spatial, compositional and flat data. All of these formats motivate new outlier concepts, such as local outlyingness, where the spatial information of the data is used to define a neighbourhood structure. Another type are compositional data, which nicely illustrate the fact that some kinds of data require not only adaptations to standard outlier approaches, but also transformations of the data itself before conducting the outlier search. Finally, the very recently developed concept of cellwise outlyingness, typically used for high-dimensional data, allows one to identify atypical cells in a data matrix. In practice, the different data formats can be mixed, and it is demonstrated in various examples how to proceed in such situations.

Electron probe microanalysis of the key metal beryllium

As beryllium (Be) has been widely used in advanced technology, national defense, security systems, and other strategic emerging industries, it has become one of the key metals in the competition between countries. However, Be is an ultra-light metal element and its accurate in-situ analysis is a technical bottleneck in the geoscience field. Although secondary ion mass spectrometry (SIMS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) can be used to analyze Be, they are mainly used for the in-situ analysis of trace Be. Moreover, the analysis of Be is affected by the substrate effect, large analysis error, and lack of standard samples. Thus, the in-situ analysis of Be has a great impact on the research of the occurrence and metallogenic mechanism of Be. Electron probe microanalyzers (EPMA) have an obvious advantage in the analysis of the quantity of minerals. The in-situ analysis of Be may be performed in the configuration of a spectroscopic crystal with large mesh spacing. In this study, an electron probe microanalyzer (Shimadzu) equipped with a spectroscopic crystal (LAS300), was used to explore the analytical conditions of Be. Based on the analysis of a standard sample of metal Be, its analytical conditions of voltage and current were obtained. The peak interference and intensity of Be are discussed by the analysis of metal Be, beryl, bertrandite, hambergite, mengxianminte, bromellite, and other Be-bearing minerals, as well as pulse height analyzer (PHA) filtration. Using the above analysis conditions, the chemical compositions of the main Be-bearing minerals (bertrandite, phenakite, and beryl) were systematically and quantitatively analyzed. The results show that: (1) The proposed analysis conditions of Be are a voltage of 12 kV, considering the peak intensity and stability; the analytical current of the anhydrous Be minerals is between 100–200 nA; and the analytical current of hydrous Be minerals is between 50–100 nA. The PHA is selected to filter the higher-ordered X-rays of other heavy elements during the measurement, and the measured time is usually set between 50 and 100 s. (2) Before the analysis, the characteristic X-ray peak position of Be in Be-bearing minerals must be determined to choose the appropriate BG (+) and BG (−) and the same or similar Be-bearing minerals should be selected as standard samples. Based on the above conditions, the quantitative analysis of Be-bearing minerals can be performed well by EPMA. (3) The amount of Be-bearing minerals were analyzed to confirm the above conditions, which resulted in suitable contents of BeO in main Be-bearing minerals from Be resources. Bertrandite and phenakite from the volcanic rocks of Qingtian, Zhejiang Province contain 39.95wt%–43.15wt% and 45.33wt%–47.08wt% BeO, respectively. Beryl from the tungsten Be ore of Damingshan, Lin’an, Zhejiang Province, consists of 12.74wt%–13.75wt% BeO. (4) The technical difficulties of quantitatively analyzing Be arise from the fact that the 2S electrons from the outer layer of Be cannot transition to 1S to produce X-rays, which results in the flat-peak pattern phenomenon of characteristic X-ray, a peak shift, and weak peak intensity. In conclusion, it is feasible to use the above analytic conditions to quantitatively analyze Be-bearing minerals, which are not only of great practical significance to understanding the occurrence, metallogenic mechanism, and resource distribution of Be but also helpful in revealing the mineralogical and geochemical properties of Be.