Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210826801> ?p ?o ?g. }
- W4210826801 endingPage "2046" @default.
- W4210826801 startingPage "2034" @default.
- W4210826801 abstract "SUMMARY Moment tensors are key to seismic discrimination but often require accurate Green's functions for estimation. This limits the regions, frequency bands and wave types in moment tensor inversions. In this study, we propose a differential moment tensor inversion (diffMT) method that uses relative measurements to remove the path effects shared by clustered events, thereby improving the accuracy of source parameters. Using results from regular inversions as a priori distribution, we apply Bayesian Markov Chain Monte Carlo to invert the body- and surface wave amplitude ratios of an event pair for refined moment tensors of both events. Applications to three North Korea nuclear tests from 2013 to 2016 demonstrate that diffMT reduces the uncertainties substantially compared with the traditional waveform-based moment tensor inversion. Our results suggest high percentages of explosive components with similar double-couple components for the North Korea nuclear tests." @default.
- W4210826801 created "2022-02-09" @default.
- W4210826801 creator A5007353054 @default.
- W4210826801 creator A5037677315 @default.
- W4210826801 creator A5046208656 @default.
- W4210826801 date "2022-02-10" @default.
- W4210826801 modified "2023-10-18" @default.
- W4210826801 title "Bayesian differential moment tensor inversion: theory and application to the North Korea nuclear tests" @default.
- W4210826801 cites W1512208174 @default.
- W4210826801 cites W1526292992 @default.
- W4210826801 cites W1564664087 @default.
- W4210826801 cites W1590349367 @default.
- W4210826801 cites W1803019238 @default.
- W4210826801 cites W1982432554 @default.
- W4210826801 cites W1999555155 @default.
- W4210826801 cites W2006251572 @default.
- W4210826801 cites W2016869432 @default.
- W4210826801 cites W2018349419 @default.
- W4210826801 cites W2021232651 @default.
- W4210826801 cites W2021693842 @default.
- W4210826801 cites W2033811120 @default.
- W4210826801 cites W2048688473 @default.
- W4210826801 cites W2070665913 @default.
- W4210826801 cites W2085622540 @default.
- W4210826801 cites W2096204979 @default.
- W4210826801 cites W2096980268 @default.
- W4210826801 cites W2099857446 @default.
- W4210826801 cites W2108225522 @default.
- W4210826801 cites W2111085275 @default.
- W4210826801 cites W2122541472 @default.
- W4210826801 cites W2123925908 @default.
- W4210826801 cites W2125038021 @default.
- W4210826801 cites W2134863146 @default.
- W4210826801 cites W2135159355 @default.
- W4210826801 cites W2141109693 @default.
- W4210826801 cites W2141133733 @default.
- W4210826801 cites W2142542473 @default.
- W4210826801 cites W2146696196 @default.
- W4210826801 cites W2165745045 @default.
- W4210826801 cites W2275750751 @default.
- W4210826801 cites W2326969108 @default.
- W4210826801 cites W2333938719 @default.
- W4210826801 cites W2512335937 @default.
- W4210826801 cites W2512851087 @default.
- W4210826801 cites W2531408329 @default.
- W4210826801 cites W2572903687 @default.
- W4210826801 cites W2621700315 @default.
- W4210826801 cites W2737246190 @default.
- W4210826801 cites W2884938607 @default.
- W4210826801 cites W2890822861 @default.
- W4210826801 cites W2941198340 @default.
- W4210826801 cites W2968211911 @default.
- W4210826801 cites W2979679402 @default.
- W4210826801 cites W2980181005 @default.
- W4210826801 cites W2992748838 @default.
- W4210826801 cites W3052105306 @default.
- W4210826801 cites W3081600409 @default.
- W4210826801 doi "https://doi.org/10.1093/gji/ggac053" @default.
- W4210826801 hasPublicationYear "2022" @default.
- W4210826801 type Work @default.
- W4210826801 citedByCount "1" @default.
- W4210826801 countsByYear W42108268012023 @default.
- W4210826801 crossrefType "journal-article" @default.
- W4210826801 hasAuthorship W4210826801A5007353054 @default.
- W4210826801 hasAuthorship W4210826801A5037677315 @default.
- W4210826801 hasAuthorship W4210826801A5046208656 @default.
- W4210826801 hasBestOaLocation W42108268012 @default.
- W4210826801 hasConcept C105795698 @default.
- W4210826801 hasConcept C107673813 @default.
- W4210826801 hasConcept C111350023 @default.
- W4210826801 hasConcept C111472728 @default.
- W4210826801 hasConcept C120665830 @default.
- W4210826801 hasConcept C121332964 @default.
- W4210826801 hasConcept C121864883 @default.
- W4210826801 hasConcept C126691448 @default.
- W4210826801 hasConcept C127313418 @default.
- W4210826801 hasConcept C1276947 @default.
- W4210826801 hasConcept C13280743 @default.
- W4210826801 hasConcept C138885662 @default.
- W4210826801 hasConcept C155281189 @default.
- W4210826801 hasConcept C165205528 @default.
- W4210826801 hasConcept C165801399 @default.
- W4210826801 hasConcept C179254644 @default.
- W4210826801 hasConcept C180205008 @default.
- W4210826801 hasConcept C1893757 @default.
- W4210826801 hasConcept C197424946 @default.
- W4210826801 hasConcept C2524010 @default.
- W4210826801 hasConcept C2994172659 @default.
- W4210826801 hasConcept C33923547 @default.
- W4210826801 hasConcept C62520636 @default.
- W4210826801 hasConcept C74650414 @default.
- W4210826801 hasConcept C75553542 @default.
- W4210826801 hasConcept C77928131 @default.
- W4210826801 hasConceptScore W4210826801C105795698 @default.
- W4210826801 hasConceptScore W4210826801C107673813 @default.
- W4210826801 hasConceptScore W4210826801C111350023 @default.
- W4210826801 hasConceptScore W4210826801C111472728 @default.
- W4210826801 hasConceptScore W4210826801C120665830 @default.