Seismic wave propagation concepts applied to the interpretation of marine controlled-source electromagnetics

Rune Mittet

doi:10.1190/geo2014-0215.1

Seismic wave propagation concepts applied to the interpretation of marine controlled-source electromagnetics

Geophysics ◽

10.1190/geo2014-0215.1 ◽

2015 ◽

Vol 80 (2) ◽

pp. E63-E81 ◽

Cited By ~ 9

Author(s):

Rune Mittet

Keyword(s):

Seismic Data ◽

Maxwell Equations ◽

Seismic Wave Propagation ◽

Finite Difference Schemes ◽

Data Types ◽

Controlled Source ◽

Electromagnetic Data ◽

Inverse Transform ◽

Refraction Seismic ◽

Marine Csem

Concepts such as reflections, refractions, diffractions, and transmissions are very useful for the interpretation of seismic data. Moreover, these concepts play a key role in the design of processing algorithms for seismic data. Currently, however, the same concepts are not widely used for the analysis and interpretation of marine controlled-source electromagnetic (CSEM) data. Connections between seismic and marine CSEM data are established by analytically transforming the diffusive Maxwell equations to wave-domain Maxwell equations. Seismic data and wave-domain electromagnetic data are simulated with 3D finite-difference schemes. The two data types are similar; however, the wave-domain electromagnetic data must be transformed back to the diffusive domain to properly describe realistic field propagation in the earth. We analyzed the inverse transform from the wave domain to the diffusive domain. Concepts like reflections, refractions, diffractions and transmissions were found to be valid also for marine CSEM data but the properties of the inverse transform favored refracted and guided events over reflected and diffracted events. In this sense, marine CSEM data were found to be similar to refraction seismic data.

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Inversion of inline and broadside marine controlled-source electromagnetic data with constraints derived from seismic data

Geophysical Prospecting ◽

10.1111/1365-2478.12294 ◽

2015 ◽

Vol 63 (6) ◽

pp. 1371-1382 ◽

Cited By ~ 9

Author(s):

Torgeir Wiik ◽

Janniche Irén Nordskag ◽

Eirik Øverland Dischler ◽

Anh Kiet Nguyen

Keyword(s):

Seismic Data ◽

Controlled Source ◽

Electromagnetic Data

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Cooperative deep learning inversion of controlled-source electromagnetic data for salt delineation

Geophysics ◽

10.1190/geo2019-0532.1 ◽

2020 ◽

Vol 85 (4) ◽

pp. E121-E137 ◽

Cited By ~ 1

Author(s):

Seokmin Oh ◽

Kyubo Noh ◽

Soon Jee Seol ◽

Joongmoo Byun

Keyword(s):

Deep Learning ◽

Test Data ◽

Seismic Data ◽

Surrounding Medium ◽

Geophysical Data ◽

Single Type ◽

Inversion Method ◽

Data Types ◽

Controlled Source ◽

Cooperative Inversion

Various geophysical data types have advantages for exploring the subsurface, and more reliable exploration can be realized through integration of such data. However, the imaging of physical properties based on deep learning (DL) techniques, which has received considerable attention because of its enormous potential, has generally been performed using only a single type of data. We have developed a cooperative inversion method based on supervised DL for salt delineation. Controlled-source electromagnetic (CSEM) data, which can effectively distinguish a salt body with high electrical resistivity from the surrounding medium, are used as data for cooperative inversion, with high-resolution information derived from seismic data used as the constraint. This approach can entrain seismic information into a fully convolutional network designed to invert CSEM data to reconstruct the resistivity distribution. The inversion network is trained using large synthetic data sets, including the seismic information derived from seismic data as well as CSEM data and resistivity models. A cooperative strategy for reasonable entrainment of seismic information into the inversion network is established based on analysis of the network and kernels of the convolutional layers. The performance of the proposed method is demonstrated through experiments on test data generated for resistivity models for complex salt structures. The trained cooperative inversion network shows improved salt delineation results compared to the independent inversion network, irrespective of noise levels added to the test data, due to restriction of the resistivity model to fit seismic information. Moreover, training with noise-added data decreased the effects of noise on prediction results, similar to the case of adversarial training. We develop the possibility of combining geophysical data with a constraint using DL-based techniques.

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Lithospheric architecture of the Ligurian Basin from seismic travel time tomography

10.5194/egusphere-egu21-2822 ◽

2021 ◽

Author(s):

Anke Dannowski ◽

Heidrun Kopp ◽

Ingo Grevemeyer ◽

Grazia Caielli ◽

Roberto de Franco ◽

...

Keyword(s):

Seismic Data ◽

Velocity Model ◽

P Wave ◽

Central Basin ◽

Uppermost Mantle ◽

Mantle Boundary ◽

Oceanic Spreading ◽

Refraction Seismic ◽

Back Arc ◽

Ligurian Basin

The Ligurian Basin is located north-west of Corsica at the transition from the western Alpine orogen to the Apennine system. The Back-arc basin was generated by the southeast retreat of the Apennines-Calabrian subduction zone. The opening took place from late Oligocene to Miocene. While the extension led to extreme continental thinning little is known about the style of back-arc rifting. Today, seismicity indicates the closure of this back-arc basin. In the basin, earthquake clusters occur in the lower crust and uppermost mantle and are related to re-activated, inverted, normal faults created during rifting.To shed light on the present day crustal and lithospheric architecture of the Ligurian Basin, active seismic data have been recorded on short period ocean bottom seismometers in the framework of SPP2017 4D-MB, the German component of AlpArray. An amphibious refraction seismic profile was shot across the Ligurian Basin in an E-W direction from the Gulf of Lion to Corsica. The profile comprises 35 OBS and three land stations at Corsica to give a complete image of the continental thinning including the necking zone.The majority of the refraction seismic data show mantle phases with offsets up to 70 km. The arrivals of seismic phases were picked and used to generate a 2-D P-wave velocity model. The results show a crust-mantle boundary in the central basin at ~12 km depth below sea surface. The P-wave velocities in the crust reach 6.6 km/s at the base. The uppermost mantle shows velocities >7.8 km/s. The crust-mantle boundary becomes shallower from ~18 km to ~12 km depth within 30 km from Corsica towards the basin centre. The velocity model does not reveal an axial valley as expected for oceanic spreading. Further, it is difficult to interpret the seismic data whether the continental lithosphere was thinned until the mantle was exposed to the seafloor. However, an extremely thinned continental crust indicates a long lasting rifting process that possibly did not initiate oceanic spreading before the opening of the Ligurian Basin stopped. The distribution of earthquakes and their fault plane solutions, projected along our seismic velocity model, is in-line with the counter-clockwise opening of the Ligurian Basin.

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