Joint inversion of airborne TEM data and surface geoelectrical data. The Egebjerg case

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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JOINT INVERSION OF MULTI-CONFIGURATION ELECTROMAGNETIC INDUCTION MEASUREMENTS TO PREDICT SOIL WETTING PATTERNS DURING SURFACE TRICKLE IRRIGATION

10.4133/sageep.27-080 ◽

2014 ◽

Author(s):

Khan Z. Jadoon ◽

Davood Moghadas ◽

Aurangzeb Jadoon ◽

Samir K. Al-Mashharawi ◽

Thomas M. Missimer

Keyword(s):

Electromagnetic Induction ◽

Joint Inversion ◽

Trickle Irrigation ◽

Soil Wetting

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Simultaneous Joint Inversion of Gravity and Self-Potential Data Measured Along Profile: Theory, Numerical Examples, and a Case Study From Mineral Exploration With Cross Validation From Electromagnetic Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2021.3071973 ◽

2021 ◽

pp. 1-20

Author(s):

Salah A. Mehanee

Keyword(s):

Cross Validation ◽

Joint Inversion ◽

Mineral Exploration ◽

Numerical Examples ◽

Electromagnetic Data ◽

Self Potential

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Lithospheric structure below seismic stations in Cuba from the joint inversion of Rayleigh surface waves dispersion and receiver functions

Geophysical Journal International ◽

10.1111/j.1365-246x.2012.05410.x ◽

2012 ◽

Vol 189 (2) ◽

pp. 1047-1059 ◽

Cited By ~ 6

Author(s):

O’Leary González ◽

Bladimir Moreno ◽

Fabio Romanelli ◽

Giuliano F. Panza

Keyword(s):

Surface Waves ◽

Joint Inversion ◽

Receiver Functions ◽

Lithospheric Structure ◽

Seismic Stations ◽

Rayleigh Surface Waves

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A Joint Inversion Estimate of Antarctic Ice Sheet Mass Balance Using Multi-Geodetic Data Sets

Remote Sensing ◽

10.3390/rs11060653 ◽

2019 ◽

Vol 11 (6) ◽

pp. 653 ◽

Cited By ~ 1

Author(s):

Chunchun Gao ◽

Yang Lu ◽

Zizhan Zhang ◽

Hongling Shi

Keyword(s):

Mass Balance ◽

Joint Inversion ◽

Ice Sheet ◽

Mass Change ◽

West Antarctica ◽

Amundsen Sea ◽

Antarctic Ice Sheet ◽

Forward Models ◽

Uplift Rates ◽

The Antarctic

Many recent mass balance estimates using the Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry (including two kinds of sensors of radar and laser) show that the ice mass of the Antarctic ice sheet (AIS) is in overall decline. However, there are still large differences among previously published estimates of the total mass change, even in the same observed periods. The considerable error sources mainly arise from the forward models (e.g., glacial isostatic adjustment [GIA] and firn compaction) that may be uncertain but indispensable to simulate some processes not directly measured or obtained by these observations. To minimize the use of these forward models, we estimate the mass change of ice sheet and present-day GIA using multi-geodetic observations, including GRACE and Ice, Cloud and land Elevation Satellite (ICESat), as well as Global Positioning System (GPS), by an improved method of joint inversion estimate (JIE), which enables us to solve simultaneously for the Antarctic GIA and ice mass trends. The GIA uplift rates generated from our JIE method show a good agreement with the elastic-corrected GPS uplift rates, and the total GIA-induced mass change estimate for the AIS is 54 ± 27 Gt/yr, which is in line with many recent GPS calibrated GIA estimates. Our GIA result displays the presence of significant uplift rates in the Amundsen Sea Embayment of West Antarctica, where strong uplift has been observed by GPS. Over the period February 2003 to October 2009, the entire AIS changed in mass by −84 ± 31 Gt/yr (West Antarctica: −69 ± 24, East Antarctica: 12 ± 16 and the Antarctic Peninsula: −27 ± 8), greater than the GRACE-only estimates obtained from three Mascon solutions (CSR: −50 ± 30, JPL: −71 ± 30, and GSFC: −51 ± 33 Gt/yr) for the same period. This may imply that single GRACE data tend to underestimate ice mass loss due to the signal leakage and attenuation errors of ice discharge are often worse than that of surface mass balance over the AIS.

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The waveform inversion of mainshock and aftershock data of the 2006 M6.3 Yogyakarta earthquake

Geoscience Letters ◽

10.1186/s40562-021-00176-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hijrah Saputra ◽

Wahyudi Wahyudi ◽

Iman Suardi ◽

Ade Anggraini ◽

Wiwit Suryanto

Keyword(s):

Joint Inversion ◽

Body Wave ◽

Moment Tensor ◽

Near Field ◽

Source Parameters ◽

Waveform Inversion ◽

Moment Tensor Inversion ◽

Slip Distribution ◽

Aftershock Distribution ◽

Velocity Models

AbstractThis study comprehensively investigates the source mechanisms associated with the mainshock and aftershocks of the Mw = 6.3 Yogyakarta earthquake which occurred on May 27, 2006. The process involved using moment tensor inversion to determine the fault plane parameters and joint inversion which were further applied to understand the spatial and temporal slip distributions during the earthquake. Moreover, coseismal slip distribution was overlaid with the relocated aftershock distribution to determine the stress field variations around the tectonic area. Meanwhile, the moment tensor inversion made use of near-field data and its Green’s function was calculated using the extended reflectivity method while the joint inversion used near-field and teleseismic body wave data which were computed using the Kikuchi and Kanamori methods. These data were filtered through a trial-and-error method using a bandpass filter with frequency pairs and velocity models from several previous studies. Furthermore, the Akaike Bayesian Information Criterion (ABIC) method was applied to obtain more stable inversion results and different fault types were discovered. Strike–slip and dip-normal were recorded for the mainshock and similar types were recorded for the 8th aftershock while the 9th and 16th June were strike slips. However, the fault slip distribution from the joint inversion showed two asperities. The maximum slip was 0.78 m with the first asperity observed at 10 km south/north of the mainshock hypocenter. The source parameters discovered include total seismic moment M0 = 0.4311E + 19 (Nm) or Mw = 6.4 with a depth of 12 km and a duration of 28 s. The slip distribution overlaid with the aftershock distribution showed the tendency of the aftershock to occur around the asperities zone while a normal oblique focus mechanism was found using the joint inversion.

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