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77 results for “Boreholes”
FIGURE 15 in Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Eocene/Oligocene boundary interval of three cored boreholes in southern coastal Tanzania and their response to the global cooling event
FIGURE 15. Range chart of pteropods, combined data of TDP 11, 12 and 17, calibrated to composite depths. Basic stratigraphical data mainly from Pearson et al. (2008, figure 16).
FIGURE 3 in Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Eocene/Oligocene boundary interval of three cored boreholes in southern coastal Tanzania and their response to the global cooling event
FIGURE 3. Heliconoides nikkieae sp. nov.; 1: Holotype, RGM 777415a, apertural view; 2: apertural view, specimen lost; 3: paratype 1, RGM 777415b, umbilical view; 4: paratype 3, RGM 777 381, umbilical view; 5: paratype 2, RGM 777415c, apical view. Figure 3-1, -2, -3 and -5 from the type locality, TDP 17.36.1, 10-25 cm; Figure 3-4 from TDP 17.21.1, 9-20 cm. Bar equals 100 μm.
Surrogate Modeling Benchmark - Borehole function
<p>This dataset is related to the Borehole function benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-borehole-function/">https://uqworld.org/t/benchmark-case-borehole-function/</a>.</p> <p>The experimental designs include datasets with 40, 80, 120, 160, and 200 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Text-fig. 3. Tumidopteris astra sp. nov., holotype GIN 4851/343h, morphology of sori. a: part of fertile pinnule; b: one practically complete sorus located on the terminal part of a lateral vein; c: sorus with four visible sporangia; d: partly damaged sorus with three visible sporangia; e: two neighbouring sori. Locality: the borehole IK-675, depth 961.7 m. Scale 1 mm (a, b, e), 500 Μm (c), 100 Μm (d). in A New Species Of The Genus Tumidopteris Naugolnykh From The Permian Of The Pechora Cis-Urals, Russia
Text-fig. 3. Tumidopteris astra sp. nov., holotype GIN 4851/343h, morphology of sori. a: part of fertile pinnule; b: one practically complete sorus located on the terminal part of a lateral vein; c: sorus with four visible sporangia; d: partly damaged sorus with three visible sporangia; e: two neighbouring sori. Locality: the borehole IK-675, depth 961.7 m. Scale 1 mm (a, b, e), 500 Μm (c), 100 Μm (d).
Text-fig. 2. Tumidopteris astra sp. nov., macromorphology. a, c: sterile pinna, spec. GIN 4851/340; b, d: holotype GIN 4851/343h; e: sterile pinna, spec. GIN 4851/344. Localities: the city of Vorkuta, Section 49, layer 254 (a, c), the borehole IK-675, depth 961.7 m (b, d), the borehole IK-677, depth 147.8 m (e). Scale 1 cm (a, b, c, e), 1 mm (d). in A New Species Of The Genus Tumidopteris Naugolnykh From The Permian Of The Pechora Cis-Urals, Russia
Text-fig. 2. Tumidopteris astra sp. nov., macromorphology. a, c: sterile pinna, spec. GIN 4851/340; b, d: holotype GIN 4851/343h; e: sterile pinna, spec. GIN 4851/344. Localities: the city of Vorkuta, Section 49, layer 254 (a, c), the borehole IK-675, depth 961.7 m (b, d), the borehole IK-677, depth 147.8 m (e). Scale 1 cm (a, b, c, e), 1 mm (d).
Bagoue dataset-Cote d'Ivoire: Electrical profiling, electrical sounding and boreholes data
<p>Bagoue region lies between longitudes 6° and 7° W and latitudes 9° and 11° N in the north of Cote d’Ivoire. The geophysical and boreholes data were collected from National Office of Drinking Water (ONEP) and West-Africa International Drilling Company (FORACO-CI) during the Presidential Emergency Program (PPU) in 2012-2013 and the National Drinking Water Supply Program (PNAEP) in 2014. During the progress of both projects, the electrical methods is the most used especially the resistivity profiling and the electrical sounding methods. Originally, data were used for Groundwater Flow Rate (GFR) prediction using a Support Vector Machines(SVMs).</p>
Borehole logs of the projects STIMTEC and STIMTEC-X
<p>Logs available for each borehole in the Research mine Reiche Zeche (Freiberg, Germany) during the projects STIMTEC and STIMTEC-X. Logging operations were carried out by LIAG (Hannover, Germany). The types of data available include optical televiewer (OTV) and acoustic televiewer (BHTV) logs, as well as sonic and caliper logs.</p>
Fig. 9 in Strophomenide and orthotetide Silurian brachiopods from the Baltic region, with particular reference to Lithuanian boreholes
Fig. 9. Diagram showing the relative dispositions of E. (Eoplectodonta) and E. (Ygerodiscus) from west to east in the East Baltic platform.
Fig. 7 in Strophomenide and orthotetide Silurian brachiopods from the Baltic region, with particular reference to Lithuanian boreholes
Fig. 7. Eoplectodonta (E.) penkillensis (Reed, 1917), B20497, Riga Formation (Sheinwoodian, M. riccartonensis Zone), eroded bedding plane with the Clorinda sp., Vilkaviškis−129, 837.1 m, × 3.4.
Eight Mile Lake Research Watershed, Thaw Gradient: Geochemical data from perched waters collected from boreholes on Gradient site during August-September 2019
In this larger study, we are asking the question: Is old carbon that comprises the bulk of the soil organic matter pool released in response to thawing of permafrost? We are answering this question by using a combination of field and laboratory experiments to measure radiocarbon isotope ratios in soil organic matter, soil respiration, and dissolved organic carbon, in tundra ecosystems. The objective of these proposed measurements is to develop a mechanistic understanding of the SOM sources contributing to C losses following permafrost thawing. We are making these measurements at an established tundra field site near Healy, Alaska in the foothills of the Alaska Range. Field measurements center on a natural experiment where permafrost has been observed to warm and thaw over the past several decades. This area represents a gradient of sites each with a different degree of change due to permafrost thawing. As such, this area is unique for addressing questions at the time and spatial scales relevant for change in arctic ecosystems. Geochemical analysis of water perched on the permafrost table complements the overarching aim of this study by targeting the organic component transported laterally from the soils. The association between DOC and mineral elements in the waters perched on the permafrost table was determined. These mineral element-organic carbon associations must be accounted for when considering the stability of organic carbon transported laterally from the catchment.
Data Sets and Code for Simple formulas for pseudoposition for electrical resistivity and IP in vertical boreholes based on mean positions of the sensistivity
<p>Data and Matlab code in figures.</p>
Borehole Lithological Facies Dataset for Interval Kriging
<p>Four sets of well log data and predefined grids for performing the interval kriging estimation. </p>
Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04
<p>Dataset of relative seismic velocity variations (dv/v) of R66E3 Raspberry Shake and groundwater level variations of BSS002NNZL borehole from 2022/04/28 to 2025/02/04.</p> <p>A Raspberry Shake RS3D, a three-components geophone (station R66E3 and network code AM) with a natural frequency of 4.5 Hz (electronically extended to 0.5 Hz), is installed in the technical room located 8 m from the BSS002NNZL borehole (a groundwater monitoring borehole), in the town of La Trinité, Martinique. </p> <p>Seismic recordings for station R66E3 (network AM: <a href="https://urldefense.com/v3/__https:/doi.org/10.7914/SN/AM__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjstW1a3C0w$">https://doi.org/10.7914/SN/AM</a>) are collected from the Raspberry Shake data center (<a href="https://urldefense.com/v3/__https:/data.raspberryshake.org/fdsnws/__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjsumhGvdYA$">https://data.raspberryshake.org/fdsnws/</a>). The raw continuous seismic recordings and relative velocity variations are processed by using MATLAB (<a href="https://urldefense.com/v3/__https:/mathworks.com__;!!KbSiYrE!iv8kuFqjRdPI_Ko8AW4XCYuOXVn1VnpNP__Nya_VHhUPUgY40Hpa99szk_9JYls8gw61SbHCjss0LIo2Pw$">https://mathworks.com</a>).</p> <p>This dataset contains the resulting 2-3 Hz frequency range relative velocity variations data. </p> <p>The dataset also contains daily mean groundwater level data (in altitude and pressure) of BSS002NNZL borehole. The borehole is equipped with a PARATRONIC SNP pressure sensor with data recording every minutes. The probe resolution is ±1 mm.</p> <p>Rainfall and air temperature are available close to the site thanks to the French climatic network operated by Météo-France (<a href="https://meteo.data.gouv.fr/">meteo.data.gouv.fr</a>). Daily rainfall (from Morne des Esses station) and daily air temperature data (from Spoutourne station) are also provided in the dataset.</p>
Stress-State Differences between Sedimentary Cover and Basement of the Songliao Basin, NE China: In-situ Stress Measurements at 6–7 km depth of an ICDP Scientific Drilling Borehole (SK-II)
<p>The excel file is the original ASR test data (Nine samples, 6293 - 6846 m vertical depth).</p>
Text-fig. 2. Lithologic profiles from "Kazimierz" openpit and BK-110 borehole. in Micropalaeontological Taphocoenoses Of The Miocene Poznań Formation (Konin Area, Central Poland)
Text-fig. 2. Lithologic profiles from "Kazimierz" openpit and BK-110 borehole.
FIGURE 5 in Pteropoda (Mollusca, Gastropoda, Thecosomata) from the Eocene/Oligocene boundary interval of three cored boreholes in southern coastal Tanzania and their response to the global cooling event
FIGURE 5. Limacina robusta (Eames, 195); RGM 777423b, apertural view. Bar equals 100 μm.
Borehole observation, in-situ stress and breakout simulation datasets for BS34 in the Xinchang site, Beishan region
<p>Two datasets are available here as supplementary materials for the study of borehole breakout development.</p> <p>For the dataset 'Borehole Data.zip', it contains the natural fractures, breakouts, and drilling-induced tensile fractues, and mini-frac test results obtained from borehole BS34 in the Xinchang site, Beishan region. For the dataset 'Simulation Data.zip', it includes the simulated stress evolution associated with breakout development in the vicinity of a pre-existing frature, and simulated breakout geometry using a finite element model.</p>
Six years of high-resolution monitoring data of 40 borehole heat exchangers
<p>This dataset provides six years of monitoring data of the ground heat exchanger that supplies heat and cold to the E.ON Energy Research Center located in Aachen, Germany. The data belong to the following publication:</p> <blockquote> <p>Heim, E., Stoffel, P., Müller, D., & Klitzsch, N. (2024). <em>Six years of high-resolution monitoring data of 40 borehole heat exchangers</em>. Scientific Data, 11(1), 1334. <a href="https://doi.org/10.1038/s41597-024-04241-9" rel="nofollow">https://doi.org/10.1038/s41597-024-04241-9</a></p> </blockquote> <p>The ground heat exchanger consists of 40 double-U-loop borehole heat exchangers (BHE), that are arranged in three subfields. Each subfield is connected to an underground vault, in which sensors for the inlet fluid temperature, the outlet fluid temperature and the volume flow of each BHE are placed. The sensors record data in 30-second intervals. Coherent data is provided from July 1, 2018 to June 30, 2024 in two time resolutions and processing levels:</p> <ul> <li><strong>Raw data</strong>: The raw data in its initial resolution, aligned to coherent 30-second timestamps.</li> <li><strong>Prepared data</strong>: The raw data was resampled to 5 minute intervals using the weighted mean, with the volume flow as weight. Moreover, data periods that are not representative of the thermal exchange process in the BHE were masked (e.g., no-flow periods).</li> </ul> <p>Both raw and prepared data are provided as .csv files for each month individually. Temperature measurements are given in °C, the volume flow rate in l/min. </p> <p>In addition, a <strong>supporting file</strong> indicating the BHE locations is provided. It also contains the length of the horizontal connecting pipes going from the underground vaults (where the sensors are placed) to the BHE heads. </p> <p><strong>Code </strong>explaining how to open and work with the data is provided in a separate github repository:<strong> </strong><a href="https://github.com/elimh/ERC_BHEfield_Data_Code">https://github.com/elimh/ERC_BHEfield_Data_Code</a></p>
Borehole pressure and temperature data, barometric pressure and temperature data, solid Earth tidal strain, for OmanDP site BA1, 4/18 to 1/19
<p>Borehole (relative) pressure and temperature data for OmanDP site BA1: holes BA1B and BA1D from 4/18 to 1/19.</p> <p>Barometric pressure and temperature data for OmanDP site BA1 from 4/18 to 1/19.</p> <p>Earth tidal strain for the OmanDP site BA1: estimates from PyGTide software from 4/18 to 9/20.</p>
Local ground-based geophysical observation data (Borehole tilt, broadband seismic, and infrasound) accompanying the 2018 phreatic eruption at Kusatsu-Shirane volcano (Motoshirane)
<p>A geophysical observation dataset (borehole tilt, broadband seismic, infrasound, and GNSS displacement) accompanying the 2018 phreatic eruption at the Motoshirane cone of the Kusatsu-Shirane volcano. The data was obtained by the local geophysical observation network operated by Kusatsu-Shirane Volcano Observatory, Tokyo Institute of Technology. All time is JST (UTC+9). Terada et al. (2021), Yamada et al. (2021), and Yamada et al. (submitted) describe station locations and instrumentations.</p> <p> </p> <p>Tilt (text files: 20190123_stn_1_Hz.txt)</p> <p>format: yy/mm/dd hour:min sec NS tilt EW tilt</p> <p> </p> <p>Broadband seismic and infrasound waveforms (sac files)</p> <p>(ex: 1801230950_stn_cmp.s)</p> <p>*stn: station name</p> <p>*cmp: component</p> <p> </p> <p>GNSS displacement (pos file)</p> <p>(ex: stn1030_0591.pos)</p> <p>*stn: station name</p> <p>Displacements are calculated by kinematic analysis using 960591 (a GNSS station operated by Geospatial Information Authority of Japan) as a base station. The kinematic analysis was performed on RTKLIB (ver. 2.4.2, Takasu, 2013). See the header for each file for detail.</p> <p> </p> <p> </p> <p>References</p> <p> </p> <p>Terada, A., Kanda, W., Ogawa, Y., Yamada, T., Yamamoto, M., Ohkura, T., et al. (2021). The 2018 phreatic eruption at Mt . Motoshirane of Kusatsu – Shirane volcano, Japan: Eruption and intrusion of hydrothermal fluid observed by a borehole tiltmeter network. Earth, Planets and Space, 73. <a href="https://doi.org/10.1186/s40623-021-01475-4">https://doi.org/10.1186/s40623-021-01475-4</a></p> <p> </p> <p>Takasu, T. (2013), RTKLIB: An Open Source Program Package for GNSS Positioning.</p> <p> </p> <p>Yamada, T., Kurokawa, A. K., Terada, A., Kanda, W., Ueda, H., Aoyama, H., et al. (2021). Locating hydrothermal fluid injection of the 2018 phreatic eruption at Kusatsu-Shirane volcano with volcanic tremor amplitude. Earth, Planets and Space, 73(1), 1–15. https://doi.org/10.1186/s40623-020-01349-1</p>
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