The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Simplified moment tensor analysis and unified decomposition of acoustic emission source: Application to in situ hydrofracturing test
Abstract
An application of a moment tensor analysis to acoustic emission (AE) is studied to elucidate crack types and orientations of AE sources. In the analysis, simplified treatment is desirable, because hundreds of AE records are obtained from just one experiment and thus sophisticated treatment is realistically cumbersome. Consequently, a moment tensor inversion based on P wave amplitude is employed to determine six independent tensor components. Selecting only P wave portion from the full‐space Green's function of homogeneous and isotropic material, a computer code named SiGMA (simplified Green's functions for the moment tensor analysis) is developed for the AE inversion analysis. To classify crack type and to determine crack orientation from moment tensor components, a unified decomposition of eigenvalues into a double‐couple (DC) part, a compensated linear vector dipole (CLVD) part, and an isotropic part is proposed. The aim of the decomposition is to determine the proportion of shear contribution (DC) and tensile contribution (CLVD + isotropic) on AE sources and to classify cracks into a crack type of the dominant motion. Crack orientations determined from eigenvectors are presented as crack‐opening vectors for tensile cracks and fault motion vectors for shear cracks, instead of stereonets. The SiGMA inversion and the unified decomposition are applied to synthetic data and AE waveforms detected during an in situ hydrofracturing test. To check the accuracy of the procedure, numerical experiments are performed on the synthetic waveforms, including cases with 10% random noise added. Results show reasonable agreement with assumed crack configurations. Although the maximum error is approximately 10% with respect to the ratios, the differences on crack orientations are less than 7°. AE waveforms detected by eight accelerometers deployed during the hydrofracturing test are analyzed. Crack types and orientations determined are in reasonable agreement with a predicted failure plane from borehole TV observation. The results suggest that tensile cracks are generated first at weak seams and then shear cracks follow on the opened joints.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quantitative seismology : theory and methods
Aki, K., & Richards, P. G. (1980) · Cited by 4,410
Dziewonski, A. M., & Woodhouse, J. H. (1983) · Journal of Geophysical Research: Solid Earth · doi:10.1029/jb088ib04p03247 · Cited by 729
Use of long-period surface waves for rapid determination of earthquake-source parameters
Kanamori, H., & Given, J. W. (1981) · Physics of the Earth and Planetary Interiors · doi:10.1016/0031-9201(81)90083-2 · Cited by 393
The determination of source properties by the linear inversion of seismograms
Stump, B. W., & Johnson, L. R. (1977) · Bulletin of the Seismological Society of America · doi:10.1785/bssa0670061489 · Cited by 225
Dynamics of an expanding fluid‐filled crack
Chouet, B. A., & Julian, B. R. (1985) · Journal of Geophysical Research Atmospheres · doi:10.1029/jb090ib13p11187 · Cited by 97
Cited by
Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
Siracusano, G., Garescì, F., Finocchio, G., Tomasello, R., Lamonaca, F., Scuro, C., Carpentieri, M., Chiappini, M., & La Corte, A. (2021) · Applied Sciences · doi:10.3390/app112412059 · Cited by 39
Metadata
- Authors
- Ohtsu, M.
- Venue
- Journal of Geophysical Research: Solid Earth
- Publisher
- American Geophysical Union (AGU)
- Published
- 1991-04-10
- Volume
- 96
- Issue
- B4
- Pages
- 6211-6221
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W2141340174
- Cited by
- 331
- References
- 5
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Simplified moment tensor analysis and unified decomposition of acoustic emission source: Application to in situ hydrofracturing test. https://app.cochat.ai/papers/10.1029/90jb02689
- The paper
- Ohtsu, M. (1991). Simplified moment tensor analysis and unified decomposition of acoustic emission source: Application to in situ hydrofracturing test. Journal of Geophysical Research: Solid Earth. https://doi.org/10.1029/90jb02689
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.