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  • 2020-2024  (36,733)
  • 1940-1944  (2)
  • 2023  (36,733)
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Years
Year
  • 1
    facet.materialart.
    Unknown
    Conductivity, temperature, and depth measurements at the RMS Titanic wreck and Nargeolet-Fanning Ridge, Northwest Atlantic, in summer 2022 (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-04
    Description: To gain information on the physical parameters of deep water in the Northwest Atlantic, CTD measurements were taken during seven dives to the RMS Titanic wreck (front of bow approx. 41.7330181, -49.9460561; 3816 m water depth) and one dive to the Nargeolet-Fanning Ridge (approx. 41.5980514, -49.4386889; 2896 m water depth) during the OceanGate expedition aboard the AHTS Horizon Arctic, 15 June - 25 July 2022. The CTD measurements of the water column down to a maximum water depth of 3853 m were conducted using a Valeport MIDAS SVX2 6000 unit attached to the submersible Titan for the duration of each dive and provided standard data for conductivity, temperature, and pressure. Conductivity and temperature data were used to compute salinity.
    Keywords: Conductivity; CTD; CTD, Valeport, MIDAS SVX2 6000, mounted on submersible; CTD-MIDAS_SVX2-SUB; CTD profile; DATE/TIME; Deep sea; Density, sigma-theta (0); Depth; DEPTH, water; Doppler velocity log (DVL), Sonardyne, mounted on submersible; DVL_Sonardyne_SUB; Event label; Horizon Arctic (AHTS); iAtlantic; Integrated Assessment of Atlantic Marine Ecosystems in Space and Time; LATITUDE; LONGITUDE; Newfoundland; Northwest Atlantic; Number of observations; NW Atlantic; OceanGate; Pressure, water; Salinity; Sigma theta (calculated, using CTD salinity); Temperature; Temperature, water; Titan-2022-C2_0073; Titan-2022-C2_0075; Titan-2022-C2_0076; Titan-2022-C2_0079; Titan-2022-C2_0080; Titan-2022-C2_0081; Titan-2022-C2_0082; Titan-2022-C2_0083
    Type: Dataset
    Format: text/tab-separated-values, 1242327 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 2
    facet.materialart.
    Unknown
    Depth profiles of trace gases, biomass and phytoplankton group composition during RV POLARSTERN cruise PS92 (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-03
    Description: This data set contains the concentrations of chlorohyll a (chla) and the phytoplankton fuctional types from the CTD stations during PS 92, which were calculated from marker pigment ratios using the CHEMTAX program (Mackey et al, 1996).Pigment ratios were constrained as suggested by Higgins et al. (2011) based on microscopic examination of representative samples during the cruise, and the input matrix published by Fragoso et al. (2016) was applied. The resulting phytoplankotn group composition is represented in chl a concentrations. From the same bottles various trace gases were measured as carbon monoxide (CO) and the Volatile Organic Compounds (VOCs) as Dimethyl sulphide (DMS), methanethiol (MeSH) and isoprene.
    Keywords: Arctic Ocean; ARK-XXIX/1, TRANSSIZ; Artic; AWI_BioOce; Biological Oceanography @ AWI; Carbon monoxide; Cast number; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata + Cryptophyta; Chlorophyll a, Haptophyta + Chrysophyta + Cyanobacteria; Chlorophyll a, Phaeocystis; Chlorophyll a, Prasinophyta + Chlorophyta; Cruise/expedition; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Dimethyl sulfide; DPA; ELEVATION; Event label; GASC; Gas chromatograph; High Performance Liquid Chromatography (HPLC); Isoprene; LATITUDE; LONGITUDE; Methanethiol; phytoplankton functional types; Polarstern; Pressure, water; Proton Transfer Mass Spectrometer; PS92; PS92/019-5; PS92/027-3; PS92/031-3; PS92/032-5; PS92/039-8; PS92/043-5; PS92/046-2; PS92/047-4; PTRMS; Sea ice; Station label; Time in seconds; trace gases; Type; vertical profile; volatile organic compounds
    Type: Dataset
    Format: text/tab-separated-values, 888 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 3
    facet.materialart.
    Unknown
    A regional pCO2 climatology of the Baltic Sea (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-03
    Description: Based on direct surface pCO2 observation and a model-based extrapolation technique, we established a regional pCO2 climatology of the Baltic Sea. Observations from June 2003 to Dec. 2021 are obtained from the SOCAT version 2022 data collection and largely based on ICOS DE-SOOP Finnmaid data. The extrapolation technique uses model-based patters of variability to create observational data-constrained, gap- and discontinuity-free mapped fields including local error estimates without the need for or dependence on ancillary data (like, e.g., satellite sea surface temperature maps). Details on the pCO2 climatology and the model-based extrapolation technique are found in Bittig et al. (2023). Here we make the corresponding dataset available with monthly climatological pCO2 value as well as a linear pCO2 time trend for the Baltic Sea domain. Both value and trend are provided with their error estimate and are centered on the 15th of each month. Besides, the long-term trend 2003-2021 in pCO2 as well as its error estimate is given.
    Keywords: Baltic Sea; BONUS_INTEGRAL; climatology; CO2; CSV text file; CSV text file (File Size); CSV text file (MD5 Hash); Description; Integrated carbon and trace gas monitoring for the Baltic Sea
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 4
    facet.materialart.
    Unknown
    Continuous VM-ADCP (vessel-mounted Acoustic Doppler Current Profiler) profile during Maria S. Merian cruise MSM76 (2023)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Details
    Publication Date: 2024-07-03
    Keywords: -; Acoustic Doppler Current Profiler; Acoustic Doppler Current Profiling, vessel-mounted (VM-ADCP); ADCP; AWI_PhyOce; Bin number; Current velocity, east-west; Current velocity, error; Current velocity, north-south; Current velocity, standard deviation; Current velocity, vertical; DATE/TIME; Depth, relative; DEPTH, water; LATITUDE; LONGITUDE; Maria S. Merian; MSM76; MSM76_0_underway-2; North Atlantic; Number; Percentage; Physical Oceanography @ AWI; Profile ID; Quality; SADCP; Ship speed; Ship speed, standard deviation; Ship velocity, East; Ship velocity, North; Ship velocity, vertical; VM-ADCP
    Type: Dataset
    Format: text/tab-separated-values, 10766304 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 5
    facet.materialart.
    Unknown
    Living organic carbon stocks of the North Sea: data and calculations (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-03
    Description: We collected, formatted and standardized publicly available data from publications and databases for 24 living carbon pools in the North Sea and calculated the carbon stock of these pools per m². The living groups include: Phytoplankton, Protozooplankton, Bacteria, Mesozooplankton, Zooplankton, Phytobenthos, Zoobenthos, Cod, Haddock, Saithe, Whiting, Norway pout, Herring, Sandeel, Sprat, Other fish, Grey Seal, Harbour Seal, Minke Whale, White beaked dolphin, Harbour porpoise, Bottlenose dolphin and White-sided dolphin. Estimates are based on the standing stock biomass of the studied organism groups and represent spatial and temporal averages. Data, data sources, assumptions and calculations are described in detail to ensure reproducibility. The data collection was carried out in the course of the project Anthropogenic impacts on particulate organic carbon cycling in the North Sea (APOC) funded by the German Federal Ministry of Education and Research (BMBF).
    Keywords: BalticSea_biomass_C; Benthos; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); Binary Object (Media Type); biomass standing stock; Carbon pool; File content; Mammals; MULT; Multiple investigations; North Sea; Organic carbon stock; plankton
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 6
    facet.materialart.
    Unknown
    Seawater carbonate chemistry and developmental, physiological and behavioral responses of larval Atlantic surfclams Spisula solidissima to projected climate change scenarios (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-03
    Description: This study used laboratory experiments to assess developmental, physiological and behavioral responses to projected climate change scenarios using larval Atlantic surfclams Spisula solidissima solidissima, found in northwest Atlantic Ocean continental shelf waters.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Animalia; Aragonite saturation state; Aragonite saturation state, standard deviation; Behaviour; Bicarbonate ion; Bicarbonate ion, standard deviation; Biomineralization index; Bottles or small containers/Aquaria (〈20 L); Calcification/Dissolution; Calcite saturation state; Calcite saturation state, standard deviation; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Calculated using seacarb after Orr et al. (2018); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Carbon dioxide, standard deviation; Clearance rate, algae cell per larvae biovolume; Coast and continental shelf; Day of experiment; Deer_Isle; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Fugacity of carbon dioxide in seawater, standard deviation; Growth/Morphology; Growth rate; Identification; Laboratory experiment; Larvae, swimming; Mollusca; Mortality; Mortality/Survival; North Atlantic; OA-ICC; Ocean Acidification International Coordination Centre; Other studied parameter or process; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; pH, standard deviation; Potentiometric; Potentiometric titration; Reproduction; Respiration; Respiration rate, oxygen, per larvae biovolume; Salinity; Salinity, standard deviation; Scope for growth; Settlement; Single species; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI); Speed, swimming; Spisula solidissima; Temperate; Temperature; Temperature, water; Temperature, water, standard deviation; Treatment: pH; Treatment: temperature; Type of study; Zooplankton
    Type: Dataset
    Format: text/tab-separated-values, 14552 data points
    Location Call Number Expected Availability
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    DATA PANGAEA
  • 7
    facet.materialart.
    Unknown
    Phytoplankton pigment concentrations and phytoplankton groups measured on water samples collected from various expeditions in the Atlantic Ocean from 71°S to 84°N (2023)
    PANGAEA
    Details
    Publication Date: 2024-07-03
    Description: This data set composes a large amount of quality controlled in situ measurements of major pigments based on HPLC collected from various expeditions across the Atlantic Ocean spanning from 71°S to 84°N, including 11 expeditions with RV Polarstern from the North Atlantic to the Arctic Fram Strait: PS74, PSS76, PS78, PS80, PS85, PS93.2 (https://doi.org/10.1594/PANGAEA.894872), PS99.1 (https://doi.org/10.1594/PANGAEA.905502), PS99.2 ( https://doi.org/10.1594/PANGAEA.894874), PS106 (https://doi.org/10.1594/PANGAEA.899284), PS107 (https://doi.org/10.1594/PANGAEA.894860), PS121 (https://doi.org/10.1594/PANGAEA.941011), four expeditions (two with RV Polarstern and two Atlantic Meridional Transect expeditions with RRS James Clark Ross and RRS Discovery) in the trans-Atlantic Ocean: PS113 ( https://doi.org/10.1594/PANGAEA.911061), PS120, AMT28 and AMT29, and one expedition with RV Polarstern in the Southern Ocean: PS103 (https://doi.org/10.1594/PANGAEA.898941). Chlorophyll a concentration (Chl-a) of six phytoplankton functions groups (PFTs) derived from these pigments have been also included. This published data set has contributed to validate satellite PFT products available on the EU funded Copernicus Marine Service (CMEMS, https://marine.copernicus.eu/), which are derived from multi-sensor ocean colour reflectance data and sea surface temperature using an empirical orthogonal function based approach (Xi et al. 2020; 2021). Description on in situ PFT Chl-a determination from pigment data: PFT Chl-a in this data set were derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017) with retuned coefficients by Alvarado et al (2021), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). The values of retuned DPA weighting coefficients for PFT Chl-a determination are: 1.56 for fucoxanthin, 1.53 for peridinin, 0.89 for 19'-hexanoyloxyfucoxanthin, 0.44 for 19'-butanoyloxyfucoxanthin, 1.94 for alloxanthin, 2.63 for total chlorophyll b, and 0.99 for zeaxanthin. The coefficient retuning was based on an updated global HPLC pigment data base for the open ocean (water depth 〉200 m), which was compiled based on the previously published data sets spanning from 1988 to 2012 described in Losa et al. (2017), with updates in Xi et al. (2021) and Álvarez et al. (2022), by adding other newly available HPLC pigment data collected between 2012 and 2018 mainly from SeaBASS (https://seabass.gsfc.nasa.gov/), PANGAEA, British Oceanographic Data Centre (BODC, https://www.bodc.ac.uk/), and Australian Open Access to Ocean Data (AODN, https://portal.aodn.org.au/) (as of February 2020, see Table 1 attached in the 'Additional metadata' for more details on the data sources).
    Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; AC3; Alloxanthin; AMT28; AMT28_10-33; AMT28_1-1; AMT28_11-36; AMT28_12-41; AMT28_13-44; AMT28_14-48; AMT28_15-50; AMT28_16-57; AMT28_17-58; AMT28_18-64; AMT28_19-66; AMT28_20-71; AMT28_21-73; AMT28_22-78; AMT28_23-80; AMT28_2-4; AMT28_24-85; AMT28_25-87; AMT28_27-93; AMT28_28-95; AMT28_29-100; AMT28_30-101; AMT28_31-105; AMT28_32-111; AMT28_33-112; AMT28_34-117; AMT28_35-120; AMT28_36-124; AMT28_37-126; AMT28_3-8; AMT28_38-133; AMT28_40-137; AMT28_4-11; AMT28_41-142; AMT28_43-147; AMT28_44-150; AMT28_45-155; AMT28_46-158; AMT28_47-164; AMT28_48-166; AMT28_49-174; AMT28_50-176; AMT28_51-181; AMT28_5-13; AMT28_52-183; AMT28_53-188; AMT28_54-190; AMT28_55-198; AMT28_56-199; AMT28_57-204; AMT28_58-206; AMT28_59-210; AMT28_59-212; AMT28_61-218; AMT28_6-17; AMT28_62-220; AMT28_63-226; AMT28_64-227; AMT28_65-232; AMT28_66-234; AMT28_7-21; AMT28_8-24; AMT28_9-28; AMT29; AMT29_AA; AMT29_AB; AMT29_AC; AMT29_AD; AMT29_AE; AMT29_AF; AMT29_AG; AMT29_AH; AMT29_AI; AMT29_AJ; AMT29_AK; AMT29_AL; AMT29_AM; AMT29_AN; AMT29_AO; AMT29_AP; AMT29_AQ; AMT29_AR; AMT29_AS; AMT29_AV; AMT29_AX; AMT29_BC; AMT29_BD; AMT29_BE; AMT29_BF; AMT29_BG; AMT29_BH; AMT29_BI; AMT29_BJ; AMT29_BK; AMT29_BL; AMT29_BM; AMT29_BN; AMT29_BO; AMT29_BP; AMT29_BQ; AMT29_BR; AMT29_BS; AMT29_BT; AMT29_BU; AMT29_BV; AMT29_BW; AMT29_BX; AMT29_BY; AMT29_BZ; AMT29_CA; AMT29_CB; AMT29_CC; AMT29_CD; AMT29_CE; AMT29_CF; AMT29_CG; AMT29_CH; AMT29_CJ; AMT29_CK; AMT29_CL; AMT29_CM; AMT29_CN; AMT29_CO; AMT29_CP; AMT29_CQ; AMT29_CR; AMT29_CS; AMT29_CT; AMT29_CTD_001; AMT29_CTD_002; AMT29_CTD_003; AMT29_CTD_004; AMT29_CTD_005; AMT29_CTD_006; AMT29_CTD_007; AMT29_CTD_008; AMT29_CTD_009; AMT29_CTD_010; AMT29_CTD_011; AMT29_CTD_013; AMT29_CTD_015; AMT29_CTD_016; AMT29_CTD_017; AMT29_CTD_018; AMT29_CTD_019; AMT29_CTD_020; AMT29_CTD_021; AMT29_CTD_022; AMT29_CTD_024; AMT29_CTD_025; AMT29_CTD_026; AMT29_CTD_027; AMT29_CTD_028; AMT29_CTD_029; AMT29_CTD_030; AMT29_CTD_031; AMT29_CTD_032; AMT29_CTD_034; AMT29_CTD_035; AMT29_CTD_036; AMT29_CTD_037; AMT29_CTD_038; AMT29_CTD_039; AMT29_CTD_041; AMT29_CTD_042; AMT29_CTD_043; AMT29_CTD_044; AMT29_CTD_045; AMT29_CTD_046; AMT29_CTD_047; AMT29_CTD_048; AMT29_CTD_049; AMT29_CTD_050; AMT29_CTD_051; AMT29_CTD_052; AMT29_CTD_053; AMT29_CTD_054; AMT29_CTD_055; AMT29_CU; AMT29_CV; AMT29_CW; AMT29_CX; AMT29_CY; AMT29_CZ; AMT29_DA; AMT29_DB; AMT29_DC; AMT29_DD; AMT29_DE; AMT29_DF; AMT29_DG; AMT29_DH; AMT29_DI; AMT29_DJ; AMT29_DK; AMT29_DL; AMT29_DM; AMT29_DN; AMT29_DO; AMT29_DP; AMT29_DQ; AMT29_DR; AMT29_DS; AMT29_DT; AMT29_DU; AMT29_DV; AMT29_DZ; AMT29_EB; AMT29_EC; AMT29_EE; AMT29_EF; AMT29_EG; AMT29_EI; AMT29_EK; AMT29_EL; AMT29_EM; AMT29_EO; AMT29_EQ; AMT29_ER; AMT29_ES; AMT29_ET; AMT29_EV; ANT-XXXII/2; ANT-XXXIII/4; Arctic Amplification; Arctic Ocean; ARK-XXIV/1; ARK-XXIV/2; ARK-XXIX/2.2; ARK-XXV/1; ARK-XXV/2; ARK-XXVI/1; ARK-XXVII/1; ARK-XXVII/2; ARK-XXVIII/2; ARK-XXX/1.1; ARK-XXX/1.2; ARK-XXXI/1.1,PASCAL; ARK-XXXI/1.2; ARK-XXXI/2; AWI_BioOce; Barents Sea; Biological Oceanography @ AWI; Campaign; Canarias Sea; chlorophyll; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a + Divinyl chlorophyll a + Chlorophyllide a; Chlorophyll b + Divinyl chlorophyll b + Chlorophyllide b; Chlorophyllide a; CT; CTD, towed system; CTD/Rosette; CTD/Rosette with Underwater Vision Profiler; CTD001; CTD002; CTD003; CTD004; CTD005; CTD006; CTD007; CTD008; CTD009; CTD010; CTD011; CTD012; CTD013; CTD014; CTD015; CTD016; CTD017; CTD018; CTD019; CTD020; CTD021; CTD022; CTD023; CTD024; CTD025; CTD026; CTD027; CTD028; CTD029; CTD030; CTD031; CTD032; CTD033; CTD034; CTD035; CTD036; CTD037; CTD038; CTD039; CTD040; CTD041; CTD042; CTD043; CTD044; CTD045; CTD046; CTD047; CTD048; CTD049; CTD050; CTD051; CTD052; CTD053; CTD054; CTD055; CTD056; CTD057; CTD058; CTD059; CTD060; CTD061; CTD062; CTD063; CTD-Acoustic Doppler Current Profiler; CTD-ADCP; CTD-RO; CTD-RO_UVP; CTD-twoyo; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Discovery (2013); Divinyl chlorophyll a; DPA; DY110; EG_I; EG_II; EG_III; EG_IV; Event label; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; Fucoxanthin; Global Long-term Observations of Phytoplankton Functional Types from Space; GLOPHYTS; Hand net; HG_I; HG_II; HG_III; HG_IV; HG_IX; HG_V; HG_VI; HG_VIII; HGIV; High Performance Liquid Chromatography (HPLC); HN; HPLC; ICE; Ice station; James Clark Ross; JR18001; Kb0; LATITUDE; Lazarev Sea; LONGITUDE; N3; N4; N5; North Greenland Sea; North Sea; Norwegian Sea; ORDINAL NUMBER; Peridinin; phytoplankton functional types; pigments; Polarstern; PORTWIMS; Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation; PS103; PS103_0_Underway-3; PS103_1-1; PS103_11-1; PS103_15-1; PS103_22-5; PS103_23-5; PS103_2-4; PS103_27-2; PS103_29-3; PS103_3-1; PS103_31-2; PS103_34-6; PS103_39-3; PS103_40-3; PS103_4-1; PS103_43-4; PS103_45-3; PS103_48-1; PS103_5-2; PS103_59-2; PS103_6-6; PS103_67-1; PS103_8-3; PS103_9-1; PS106_18-2; PS106_21-2; PS106_27-6; PS106_28-2; PS106_31-2; PS106_32-2; PS106_45-1; PS106_50-1; PS106_ZODIAK_170527; PS106_ZODIAK_170529; PS106_ZODIAK_170531; PS106_ZODIAK_170601; PS106_ZODIAK_170607; PS106_ZODIAK_170608; PS106_ZODIAK_170617; PS106_ZODIAK_170618; PS106_ZODIAK_170619; PS106_ZODIAK_170624; PS106_ZODIAK_170625; PS106_ZODIAK_170626; PS106_ZODIAK_170627; PS106_ZODIAK_170629; PS106_ZODIAK_170630; PS106_ZODIAK_170701; PS106_ZODIAK_170702; PS106_ZODIAK_170703; PS106_ZODIAK_170705; PS106_ZODIAK_170706; PS106_ZODIAK_170708; PS106_ZODIAK_170709; PS106_ZODIAK_170710; PS106_ZODIAK_170711; PS106_ZODIAK_170713; PS106_ZODIAK_170714; PS106_ZODIAK_170715; PS106/1; PS106/2; PS107; PS107_0_underway-9; PS107_10-4; PS107_12-3; PS107_14-1; PS107_16-3; PS107_18-3; PS107_19-1; PS107_20-8; PS107_21-1; PS107_22-6; PS107_24-1; PS107_28-1; PS107_29-1; PS107_33-6; PS107_34-5; PS107_36-1; PS107_37-1; PS107_40-2; PS107_40-3; PS107_40-4; PS107_40-5; PS107_40-6; PS107_48-1; PS107_6-8; PS107_7-1; PS107_8-1; PS113; PS113_0_underway-5; PS113_11-2; PS113_1-2; PS113_13-2; PS113_14-2; PS113_15-1; PS113_17-2; PS113_18-2; PS113_20-1; PS113_21-1; PS113_22-2; PS113_23-2; PS113_25-1; PS113_26-2; PS113_27-1; PS113_28-1; PS113_29-2; PS113_30-2; PS113_31-1; PS113_3-2; PS113_33-1; PS113_5-2; PS113_6-2; PS113_7-2; PS113_9-2; PS120; PS120_0_underway-10; PS120_11-3; PS120_15-3; PS120_19-3; PS120_20-1; PS120_21-3; PS120_24-3; PS120_3-1; PS120_5-3; PS120_8-3; PS121; PS121_0_Underway-65; PS121_1-2; PS121_12-2; PS121_15-1; PS121_16-5; PS121_24-2; PS121_25-2; PS121_27-2; PS121_28-4; PS121_29-1; PS121_32-2; PS121_33-2; PS121_34-1; PS121_35-3; PS121_36-1; PS121_38-1; PS121_39-1; PS121_40-3; PS121_43-7; PS121_44-3; PS121_45-1; PS121_52-2; PS121_52-6; PS121_5-3; PS121_7-3; PS74; PS74/104-1; PS74/107-1; PS74/108-1; PS74/112-1; PS74/119-1; PS74/120-1; PS74/127-1; PS74/128-1; PS74/132-1; PS74/133-1; PS74/134-1; PS74/1-track; PS74/2-track; PS76; PS76/001-1; PS76/002-1; PS76/005-1; PS76/007-2; PS76/009-1; PS76/017-1; PS76/020-1; PS76/025-1; PS76/026-1; PS76/030-1; PS76/034-3; PS76/039-1; PS76/041-1; PS76/044-1; PS76/049-1; PS76/051-1; PS76/057-1; PS76/058-1; PS76/062-1; PS76/064-1; PS76/068-1; PS76/072-1; PS76/080-1; PS76/082-1; PS76/094-1; PS76/098-1; PS76/102-1; PS76/109-3; PS76/110-1; PS76/111-1; PS76/120-2; PS76/121-1; PS76/122-1; PS76/124-3; PS76/129-1; PS76/132-1; PS76/134-1; PS76/135-1; PS76/136-1; PS76/138-1; PS76/139-1; PS76/157-1; PS76/159-2; PS76/166-1; PS76/167-1; PS76/170-2; PS76/173-1; PS76/174-1; PS76/175-1; PS76/176-1; PS76/178-1; PS76/179-3; PS76/181-1; PS76/182-1; PS76/184-1; PS76/185-1; PS76/194-1; PS76/200-1; PS76/201-1; PS76/203-1; PS76/204-1; PS76/208-5; PS76/210-2; PS76/211-1; PS76/216-1; PS76/220-1; PS76/223-1; PS76/224-1; PS76/227-3; PS76/229-1; PS76/231-1; PS76/233-1; PS76/235-
    Type: Dataset
    Format: text/tab-separated-values, 37522 data points
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    DATA PANGAEA
  • 8
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    Unknown
    Spatial distribution of the b-value based on Gauss process inference with a geological prior (2023)
    In:  Abstracts
    Details
    Publication Date: 2024-07-02
    Description: As a population parameter, obtaining a reliable estimation of the b-value is inherently complex, especially when considering spatial variability. To tackle this issue, we adopt an approach that treats the spatial b-value distribution as a non-stationary Gauss process for the underlying earthquake-realizing Poisson process. For Gauss process inference, it is necessary to specify the covariance, which in this context describes the spatial correlation of the b-value, a priori. We formulate the anisotropic covariance as another Gauss process based on the local fault structure. The covariance anisotropy characterizes, in terms of the b-value, the correlation between earthquakes on a fault, which is higher than between an on-fault earthquake and an off-fault earthquake (or an event on another fault). This adaptive feature captures the geological structure more effectively than an isotropic covariance or similarly defined and commonly used running-window estimates of the b-value. In our research, we demonstrate the Bayesian inference of the Gauss process b-value estimation for several regions with dense earthquake catalogs and fault catalogs, such as southern California based on the SCEDC earthquake catalog and UCERF3 fault model. Our model provides a continuous b-value estimate (including its uncertainties) that reflects the local fault structure to a very high degree. We can associate the b-value with the local seismicity distribution and major faults with higher resolution than conventional (isotropic) estimation methods. Furthermore, in light of the Turkish earthquake sequence in 2023, we also assess the spatial variability of the b-value of aftershocks and their association with various faults in the region.
    Type: info:eu-repo/semantics/conferenceObject
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    PAPER (OA)
  • 9
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    Unknown
    Strong-motion on DAS: Insights from the 2022 MW 6.9 Chishang earthquake (Southern Taiwan) (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2024-07-02
    Description: Distributed acoustic sensing (DAS) sees increased utilization in the seismological community in recent years and various applications are investigated for the usage of DAS in different branches of seismology. Strong-motion seismology uses records of earthquakes of engineering concern (MW〉4.5) with hypocentral distances within few hundreds of kilometers. This demands dense networks over a wide area and installation of typical strong-motion instruments (accelerometers) can be achieved quickly and at a reasonable budget, compared to other network types. For DAS, installation and operation are more involved, and deployment is very still limited. Consequently, DAS recordings of nearby large events are still very unlikely and rare compared to accelerometers. On September 18, 2022, a shallow earthquake sequence with a M〈sub〉W〈/sub〉 6.9 mainshock struck near Chishang (Taiwan) and was recorded by DAS in Hualien city, appr. 100 km north. Shaking of the mainshock and several aftershocks were noticeable in Hualien, though not damaging with PGA recorded at 0.28 m/s^2 nearby the DAS site. The DAS campaign was originally conceptualized as a test suite with different fiber installations: including buried, within a gutter (as in commercial fiber installation) and loose within a basement. The test site is in an urban area affected by surface rupturing during the 2018 Hualien earthquake. The presented recordings provide not only an unprecedented insight how strong-motion appears on DAS but also how effective different installation techniques are for this kind of event. The waveforms are also compared to records of a collocated broadband seismometer and an accelerometer 1 km away.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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  • 10
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    Unknown
    Investigating dynamic range of Distributed Acoustic Sensing and introducing a neural-network-based detection for saturation effects (2023)
    In:  XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    Details
    Publication Date: 2024-07-02
    Description: Distributed Acoustic Sensing (DAS) is used to record high-spatial resolution strain-rate data. For ground motion observation, the DAS data can be converted from strain rate to acceleration or velocity by array-based measurements with coherent plane waves. DAS provides an opportunity to map high-resolution shaking patterns near faults. We installed collocated geophones and optical fiber in Hualien City (a very seismically active area in Taiwan) from the end of January to the end of February in 2022. Earthquakes with magnitudes (Mw) between 3.2 and 5.4 have been recorded. These records illustrate the typical magnitude-distance dependence of ground-motion but also show saturation for higher magnitudes and/or at shorter distances (e.g for an earthquake of Mw 5.2 earthquake recorded at 100 km). For frequency-based analyses, clipped signals on DAS result in challenges not present in classical instruments (seismometers). The upper limit in dynamic range of seismometers results in easily identifiable trapezoidal signals. The dynamic range of DAS interrogators is limited by gauge length, sampling frequency, and wrapped phase in the interferometric phase demodulation. We observe that clipped DAS signals not only affect time series but also contaminate their spectra on all frequencies, due to the random nature of clipping in DAS—contrasting to the flat plateaus in clipped time series on seismometers. Therefore, the identification of the start and end points of clipped DAS records poses a major challenge, which we aim to resolve with a neural network. This approach enhances the efficiency for quality control of massive DAS datasets.
    Language: English
    Type: info:eu-repo/semantics/conferenceObject
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