Reviewerly/exHarmony huggingface dataset:
Three-dimensional interpretation of DC resistivity/IP survey for Lithium exploration using high-precision topographic information from drone-mounted LIDAR.

Recently, due to the active spread of electric vehicles, the demand for batteries is increasing fast, and for this reason, the exploration for lithium that is an essential mineral for battery production, is increasing. In Korea, lithium exploration is also being conducted around deposits where lithium was identified in the past. However, most lithium mines are located in very rough terrain, so it is not easy to conduct a surface geological and geophysical exploration. Without considering complex topography, errors may occur in the inversion of surface geophysical exploration data, and in particular, it is necessary to use precise topographic information for the three-dimensional inversion. In this study, we would like to introduce a case study using high-resolution topographic data obtained from a drone-mounted LIDAR in the three-dimensional inversion of surface resistivity and IP data conducted for lithium exploration. The target area is the Boam Mine, located in the Middle East of Korea. Surface geophysical exploration was conducted along a road and ridge of the mountain, which are relatively easy to set up the survey line. Because existing topographic maps that are publically available did not include mining traces related to mining development and topographical changes formed by nearby roads, it is not adequate for the 3D inversion of surface resistivity and IP data. To acquire precise topographical information, aerial photography and LIDAR measurements using drones were performed. A numerical topographic model was constructed using the obtained high-precision DEM (digital elevation map). By applying this to the three-dimensional inversion, the distribution of the underground mineralization zone was estimated. The interpreted results were compared with the existing drilling results performed near the mine. Comparing the two results, drilling surveys using only surface geological information proceeded in the direction in which the mineralization zone did not develop. Drone LIDAR measurement is a costly exploration method and is difficult to use actively at all exploration sites. However, if three-dimensional inversion is required where the surface topography is very complex, as in this survey area, it could give more reliable inversion results.
Semi-automated fault extraction and quantitative structural analysis from DEM data, a comprehensive tool for fault network analysis

Mapping and characterisation of crustal faults represent one of the contemporary challenges for both tectonic understanding and seismic hazard assessment. Given the high resolution of satellite-derived digital elevation models and remote-sensing imagery, the development of an automatic method of fault extraction is a critical turning point. Here we present a Python-based, open-source workflow,&amp;#160; which is able to extract and characterize individual faults as well as entire fault networks from various datasets.&amp;#160;Our workflow consists of four main steps: (1) The DEM contains different types of noise, which we reduce using Gaussian smoothing. (2) Then we use the Canny edge detection to highlight topographic discontinuities, such as faults. (3) These edges are simplified in single pixel-wide lines through the skeletonization algorithm. (4) Finally, we create a network consisting of nodes and edges from this skeleton. After a few post-processing steps we obtain a fault network of the sample area.&amp;#160;We use the toolbox to study faulting in the East African Rift system, especially the Magadi Natron basin. The workflow was applied to a TanDEM-X digital elevation model with 12 m horizontal resolution and the Copernicus GLO-30 dataset with 30 m average horizontal resolution. The strike analysis shows four main directions from distinct fault populations. Moreover, we derive the fault displacement distribution throughout the basin, which allows us to calculate the total orthogonal extension of each geological unit and to compute the overall amount of extension of the region during geologically recent times.Our workflow is designed to evaluate topographic data of target sites in nature, it can, however, also be used to analyze analogue models and numerical simulations. To this aim, specific functions can be added in a modular way to suit the particularity of the area and of available data types. This workflow allows us to imagine a very wide range of applications and subjects of interest.
Towards monitoring volcanic hydrothermal alteration using geophysical approaches

Most volcanoes on Earth host a volcano-hydrothermal system. Lying between the surface and magma reservoirs, they exert a dramatic influence on volcano dynamics. Understanding their behavior is however challenging because of the complex interplay between gas, liquid, and rocks. Volcanic gas can, for example, be completely scrubbed through interactions with groundwater whereas the kinetics of these reactions are controlled by thermodynamic conditions that are poorly constrained. While fluid circulation gives rise to a range of geophysical signals such as ground vibrations, self-potential, or variable resistivity observable at the surface, their complex dynamics complicate the isolation of pre-eruptive signals and the interpretation of the observed volcanic activity. Indeed, some volcanoes remain on alert for months or years without experiencing any eruption. Such situations severely affect the credibility of the agencies in charge of monitoring volcano activities.In this contribution, we will focus on multi-disciplinary efforts to better characterise the time evolution of these complicated systems. Our ultimate goal is to deconvolve the contribution of dynamic processes occurring in such systems (temperature, gas saturation, alteration, precipitation) to possibly facilitate real-time monitoring efforts. Our examples come from different parts of the world, in both hemispheres.
Mapping mining environmental impacts using PRISMA hyperspectral imagery: Acid mine drainage (AMD) example

Acid mine drainage (AMD) is considered as one of the main factors causing water pollution in regions with historic or current mining activities. Its generation, release, mobility, and attenuation involves complex processes governed by a combination of physical, chemical, and biological factors. Clearly this phenomenon is highly dynamic depending on other&amp;#160;external&amp;#160;factors such as precipitations and ground water table fluctuations&amp;#160;and conventional monitoring is time and resource-demanding. Recent research studies proved that imaging spectroscopy represents an alternative to conventional methods and an efficient way to characterize mines and assess the potential for AMD discharge while focusing on&amp;#160;mapping those&amp;#160;minerals serving&amp;#160;as indicators of sub-aerial oxidation of pyrite (&amp;#8216;hot spots&amp;#8217;) and the subsequent formation of AMD. In this study&amp;#160;a&amp;#160;potential of new PRISMA hyperspectral satellite sensor for multi-temporal AMD mappng was evaluated. The PRISMA&amp;#160;AMD mineral mapping&amp;#160;results were compared with existing ground truth&amp;#160;data&amp;#160;and&amp;#160;other&amp;#160;validated AMD maps derived using aerial high-resolution hyperspectral imaging data (e.g.,&amp;#160;CASI/SASI). To conclude, a&amp;#160;spectral and spatial resolution&amp;#160;of the PRISMA satellite data&amp;#160;is sufficient to map this phenomenon at multi-temporal scale and PRISMA data has a potential to be operationally used in remediation projects and other environmental applications.
The use of multivariate Gaussian process emulation in making projections of land ice contributions to sea level rise

Better understanding changes in the cryosphere is key to predicting future global sea level rise, as is being done in the PROTECT project (https://protect-slr.eu). There are large uncertainties around how these changes will present over the next few centuries, with the Antarctic ice sheet being the component with the most varied predictions of potential mass change; statistical methods are required in order to quantify this uncertainty and estimate more robust projections. We present here results from a multivariate Gaussian process emulator (Rougier, 2008; Rougier et al., 2009) of an ensemble of ice sheet and glacier models. We build projec- tions of contributions to global sea level rise over several centuries from the Antarctic and Greenland ice sheets, and the world&amp;#8217;s glaciers, emulating them individually in order to better understand the biases and internal variability each model contains. Our use of an outer-product emulator allows us to model multi-variate output, resulting in projections over several centuries rather than a single year at a time. We predict changes for differ- ent Shared Socioeconomic Pathways (SSPs) to show how different emissions scenarios will affect land ice contributions to sea level rise, and demonstrate the differing sensitivity to parameters and forcings of the ensemble of models used. References Rougier, J. (2008). Efficient emulators for multivariate deterministic functions. Journal of Computational and Graphical Statistics, 17(4):827&amp;#8211;843. Rougier, J., Guillas, S., Maute, A., and Richmond, A. D. (2009). Expert knowledge and multivariate emulation: The thermosphere&amp;#8211;ionosphere electrodynamics general circula- tion model (tie-gcm). Technometrics, 51(4):414&amp;#8211;424.
The Petrinja earthquake series located and visualised using machine learning

Earthquake detection and phase picking are crucial steps in the analysis of earthquakes. With the increasing number of seismic instruments available, large amounts of seismic data are generated, requiring the use of automatic algorithms to process earthquake series and to include events that would not be discovered with manual approaches. The Petrinja earthquake series started with local magnitude ML5.0 earthquake on December 28, 2020, followed by ML6.4 earthquake one day later. In the two years of this earthquake series, human analysts picked a total of 16,000 earthquakes smaller than M2.0, 1528 with magnitudes M2.0-2.9, 156 with magnitudes M3.0-3.9, 17 with magnitudes M4.0-4.9, 2 with magnitudes M5.0-5.9 and one earthquake with magnitude greater than M6.0. While the seismic network at the onset of this sequence counted only a few instruments in the epicentral area, the rapid aftershock deployment of 5 stations in the near vicinity of the fault zone, and the further gradual yet still remarkable growth of the seismic network to more than 50 instruments, produced an extraordinary amount of data, which are perfectly suited for employing machine learning (ML) methods for seismic phase picking and earthquake detection.In this study we present application of various ML methods to the Petrinja earthquake series. We also compare how the results change when we train a model using a subset of data from this earthquake series. Our results show that these machine learning methods are promising approaches for accurately detecting and picking phases in such earthquake series, and also delineate tectonic features responsible for generating them.
Soil geochemical association of PTE and associated health risk in Kotayk marz (region), Armenia

Conditioned by the nowadays level of anthropogenic impact the study and characterization of the chemical composition of soil environment is of great concern. In 2021 Kotayk marz (administrative region of Armenia) regional scale soil geochemical surveys (1 sample per 25 km2) was performed. In total, 86 soil samples were collected and the contents of Cr, V, Ti, As, Zn, Cu, Co, Fe, Mn, Ba, Pb, and Mo were determined by the XRF spectroscopy (USEPA, 2007). To reveal factors controlling potentially toxic elements (PTE) spatial patterns and their geochemical associations the combined application of modern compositional data analysis methods and geospatial mapping was performed. In addition, health risk assessment was performed. The spatial distribution of the identified multivariate outliers showed that a spatial cluster occupying the northern part of the region is conditioned by natural mineralization and ore mining. After the elimination of univariate and multivariate outliers k-means clustering combined with the clr-biplot allow to reveal two spatially distinct groups of cases. Moreover, it was identified that the group of cases located in the eastern, southern and western part of the region is represented by the Fe, Co, Ti, As, Cu, and Cr. In these areas the geological base is characterized by the volcanic rocks (basalts, andesites, dacites, rhyolite, and esibasalts etc.) (Vardanyan, 2007). The second group of cases are mainly located in the northern part of the region and is represented by Pb, Zn, V, and Ba where the geological base is composed of granodiorites, granites, granosienites, monzonites, gabbro, dolomite, marble etc. [3]. Therefore, it was found that the chemical features and geochemical associations of elements in Kotayq marz soils are mainly conditioned by the peculiarities of local geological base and ore mining activities. Excesses vs MAC&amp;#8217;s stated in Armenia are observed for As (in all samples), Cr, Cu, Pb and Mn in 81.4 %, 1.2 %, 1.2 % and 8 % of samples. However, the health risk assessment showed that children&amp;#8217;s non-carcinogenic risks (RAIS, 2021) of As were observed only in one sampling site, respectively. The multi-elemental non-carcinogenic risk to children was detected in whole territory of the region and was mainly conditioned by the contents of Fe, Co, Mn, Pb, and As.Acknowledgment:Supported by the MESCS Science Committee of RA: grant &amp;#8470;1-12/20TB.References[1] US EPA. (2007). Field portable x-ray fluorescence spectrometry for the determination of elemental concentrations in soil and sediment. Method 6200.[2] RAIS. (2021). Risk Exposure Models for Chemicals User&amp;#8217;s Guide. The Risk Assessment Information System. https://rais.ornl.gov/tools/rais_chemical_risk_guide.html. Accessed 1 January 2020[3] Vardanyan M, Valesyan L (2007) National Atlas of Armenia, p.2
Forecasting surface solar irradiance in Germany using Meteosat Rapid Scanning Service satellite images

Photovoltaic (PV) production is strongly dependent on cloud cover behaviour. It can induce a very high variability of the production which is problematic for a safe and gainful injection into the power grid. Advanced forecasting solutions represent a major key to reliable PV systems. Satellite data are used to provide forecasts from 15 minutes until 6 hours ahead.To achieve cloud cover forecast, the first step consists in converting two successive satellite images into a cloud index map. Then, the movement of the clouds between these two images is obtained by analysing the optical flow, transformed into a Cloud Motion Vector (CMV) which is then applied on the image taken at T0 to extrapolate it and forecast the various cloud index maps up to T0 + 6h. Finally, the cloud index is combined with a clear sky model in order to compute the effective Surface Solar Irradiance.Over Europe, raw images are taken by EUMETSAT&amp;#8217;s (European Organisation for the Exploitation of Meteorological Satellites) geostationary satellite. The satellite scans the Earth&amp;#8217;s full disk in 15 minutes with the PRIME satellite positioned at 0&amp;#176;. However, the Rapid Scanning Service (RSS) scans the northern third of the Meteosat disk every five minutes, enabling more frequent data acquisition and lower delivery time. One satellite is dedicated to this operating mode and is positioned at 9.5&amp;#176;E.TRUSTPV is a European Union&amp;#8217;s Horizon 2020 Research project whose purpose is to investigate and demonstrate the development of O&amp;M-friendly and grid-friendly solar solutions in large portfolios of distributed and utility scale photovoltaics. Within TRUSTPV, we demonstrate the performance improvement provided by using the geostationary meteorological satellite's RSS to obtain images more frequently and therefore improve intraday forecasts. In this work, we forecast cloud cover every 5 minutes with a 5-minute time step. Then, we simulate PRIME operation with forecasts generated every 15 minutes with a 15-minute time step by using the same optical flow and extrapolation algorithms. Moreover, we take into account the latency in the access to the data in real time. The model outputs are compared to 10-minute solar radiation measurements from Deutscher Wetterdienst (DWD) stations located in Germany over the period ranging from 2021-09-01 to 2022-08-31. We determine the quarterly performance in order to study the seasonal effects. The results are also expressed in terms of relative Root Mean Scare Error (RMSE), RMSE Skill Score, Mean Absolute Error (MAE), MAE Skill Score, and mean bias error.Comparisons between forecasted surface solar irradiance at 30 minutes of time horizon and co-located pyranometric measurements show an improvement for all sites with a decrease of MAE around 4%. This gain brought by the RSS will improve the quality of power production forecasts of PV plants.The research leading to these results has received funding from the Horizon 2020 Research and invention Programme, under Grant Agreement No 952957, Trust-PV project.
Description Of A Framework And Associated Sensitivity Analysis For Recovery Of Rheological Models And Their Key Parameters Using Multi-Cycle Fault Slip Models

Constraining the effective rheology of major faults is crucial to improve our understanding of the physics of plate boundary deformation. Laboratory studies have used analog experiments to propose rheological models based on viscoelasticity or friction that match laboratory-observed behavior under stress-controlled conditions. Such models have since been used to fit real-world observations of deformation near plate interfaces (both for co- and postseismic displacement timeseries), yielding a variety of estimates of key rheological parameters.However, confidently differentiating between models using purely observations of a single earthquake (coseismic and postseismic deformation) is difficult &amp;#8212; especially in the presence of coarse spatiotemporal sampling, inherent observational noise, and the simplifications of our forward models. In this study, we present a framework built on numerical probabilistic simulations aimed at using displacement timeseries across multiple earthquake cycles in a subduction zone, which successfully distinguishes between endmember constitutive models and recovers key rheological properties. Using synthetic Global Navigation Satellite System network datasets, we furthermore investigate the sensitivity of (hyper-)parameters to the recovery of the true underlying rheological models, and present progress made towards using real 3D observations of a megathrust.
Random Forest classifier for lithological mapping of the Mundiyawas-Khera mineralized belt of the Alwar basin, India, from remote sensing and potential field data

Lithological interpretation of remote sensing and geophysical data plays a vital role in mineral resource mapping, especially in areas of the limited outcrop. This study applied a Random Forest (RF) classifier to obtain the refined lithological map of the Mundiyawas-Khera mineralized belt of the Alwar basin, India, from remote sensing and potential field data. A total of 540 samples covering the major lithologies were fed to RF for training (80%) and testing (20%), and its performance was evaluated using precision, recall, and accuracy. The degree of uncertainty associated with RF was also computed using the information entropy technique to pinpoint the regions where the refined lithology map is incorrectly classified. The results indicate that RF yields an overall accuracy of 73.15% in classifying all the major lithological units in the region, such as felsic volcanic, carbon phyllite, mica schist, quartzite, and tremolite-bearing dolomite. Among all the five lithologies, RF showed the best precision (84.62%) and recall (90.91%.) for quartzite and M-mica schist respectively and comparable precision/recall values for the felsic volcanic rocks that host Cu mineralization. Whereas other lithologies, dolomite and carbon phyllite, were not accurately predicted by RF, which might be due to the limited number of samples. The results of the class membership probabilities indicate that not all the litho-units predicted by the model are absolute. The study illustrates that RF can be used as a viable alternative in regions with limited outcrops and geochemical information to prepare the new lithology map or refine the existing geological maps.&amp;#160;Keywords: Machine Learning, Lithology Classification, Gravity and Magnetic Data
Comparison of Methods for Learning Differential Equations from Data

Some results from the DEEB (Differential Equation Estimation Benchmark) are presented. In DEEB, we compare different machine learning approaches and statistical methods for estimating nonlinear dynamics from data. Such methods constitute an important building block for purely data-driven earth system models as well as hybrid models which combine physical knowledge with past observations.Specifically, we examine approaches for solving the following problem: Given time-state-observations of a deterministic ordinary differential equation (ODE) with measurement noise in the state, predict the future evolution of the system. Of particular interest are systems with chaotic behavior - like Lorenz 63 - and nonparametric settings, in which the functional form of the ODE is completely unknown (in particular, not restricted to a polynomial of low order). To create a fair comparison of methods, a benchmark database was created which includes datasets of simulated observations from different dynamical systems with different complexity and varying noise levels. The list of methods we compare includes: echo state networks, Gaussian processes, Neural ODEs, SINDy, thin plate splines, and more.Although some methods consistently perform better than others throughout different datasets, there seems to be no silver bullet.
Data journals induce culture change in earth sciences

Science changes direction, practice and impact when researchers discover tangible rewards. Policy organizations, funding agencies and educational institutions might wish otherwise but long experience suggests that new incentive structures, better recognition of engagement, and cultural change emerge bottom-up, not top-down. For these reasons, recent emergence of data journals issuing valid credit for data providers accompanied by guidance and assurance for data users promote rapid positive change in data sharing and impact. In Earth System Science Data (ESSD), a &amp;#8216;new&amp;#8217; (since 2009) Copernicus journal (and, likewise, in Scientific Data by Springer/Nature since 2014 and in a few other recent journals), authors experience widespread, often unanticipated, impact of their data through use, and re-use and citation. Meanwhile, journalists discover, and appreciate, reliability and utility of data publications, particularly for climate, biodiversity or public health data products that update on regular (e.g. annual) bases. With care and cooperation, ESSD publications on topics such as agricultural or woodfire emissions, population, global carbon, methane or energy budgets, or regional pipeline capabilities: a) feed and support &amp;#8216;front-page&amp;#8217; articles in BBC, Washington Post and other nationally- and internationally-prominent news sources; b) develop useful options for essential planetary monitoring (e.g. as components incorporated into UNFCCC&amp;#8217;s proposed Global Stocktake); and c) demonstrate science - via normal steps of scrutiny and revision - engaged with urgent social issues. Through familiar but innovative mechanisms researchers gain validation and certification of data (for citation credit!), ensure wide re-use throughout broad research communities, and often achieve substantial public impact. These new mechanisms signal a positive change in the culture of our science.&amp;#160;