We present the first seismic model of the crust beneath Sakhalin based on P and S‐wave arrival time data from local earthquakes. Based on the results of numerous synthetic tests, we conclude that this model has fair horizontal and vertical resolution to 20–25 km depth. At shallow depths, seismic anomalies are clearly associated with known geological structures, such as the high‐velocity Paleozoic Susunai block and the low‐velocity Cenozoic fold belts along the West Sakhalin Mountains. In vertical sections, we observe westward underthrusting of the Susunai block to a distance of at least 70 km, which may represent the regional compression and considerable crustal shortening in this area. Based on the tomography results, we hypothesize about the origin of the mud volcanism in southern Sakhalin. We propose that because of the general westward underthrusting regime in Sakhalin, hydrocarbon‐rich shelf sediments may be entrained to considerable depths under the rigid Susunai block, which serves as a nonpermeable cover. The released gases find the weakest zones around the Susunai block and along the Tym‐Poronay Fault and escape to the surface to form the South Sakhalin and Lesnovsky mud volcano fields.
We describe a new algorithm for the inversion of one‐dimensional shear‐wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S‐wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 km2. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four‐dimensional near‐surface monitoring.
We have addressed the problem of estimating surface-wave phase velocities through the spectral processing of seismic data. This is the key step of the well-known near-surface seismic exploration method, called multichannel analysis of surface waves. To increase the accuracy and ensure the unambiguity of the selection of dispersion curves, we have developed a new version of the frequency-wavenumber ([Formula: see text]-[Formula: see text]) transform based on the S-transform. We obtain the frequency-time representation of seismic data. We analyze the obtained S-transform frequency-time representation in a slant-stacking manner but use a spatial Fourier transform instead of amplitude stacking. Finally, we build the [Formula: see text]-[Formula: see text] image by analyzing the spatial spectra for different steering values of the surface-wave group velocities. The time localization of the surface-wave packet at each frequency increases the signal-to-noise ratio because of an exclusion of noise in other time steps (which does not fall in the effective width of the corresponding wavelet). The new [Formula: see text]-[Formula: see text] transform, i.e., the slant [Formula: see text]-[Formula: see text] (SFK) transform, renders a better spectral analysis than the conventional [Formula: see text]-[Formula: see text] transform and yields more accurate phase-velocity estimation, which is critical for the surface-wave analysis. The advantages of the SFK transform have been confirmed by synthetic- and field-data processing.
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