GEOPHYSICAL STUDIES OF RING STRUCTURES FOR THE SEARCH FOR OIL AND GAS DEPOSITS

The purpose of this work is to show the possibilities of regional geomorphological and geological-geophysical methods for studying the tectonic and geomorphological features of the structure of central-type ring structures on the territory of the Middle Urals (Tatarstan, Udmurtia, Bashkiria, Kirov Region) and adjacent regions of the Komi-Permyat Autonomous Region, allowing to study the oil and gas prospects of these territories. According to the results of geological and geophysical interpretation of the data of magnetic exploration, gravity exploration and heat flow, the deep structure of the root inhomogeneities of the Voy-Vozhsky ring geomorphological structure of the central type, one of the analyzed in the territory of the Middle Urals, is shown. It is assumed that the intersection points of tectonic faults of the Earth's crust (geomorphological liniments) located in the side zones of circular geomorphological structures of the central type on the territory of the Middle Urals and adjacent regions can be promising objects of geological and geophysical research in search of oil deposits. Gas fields can be confined to the central zones ("pipes" of mantle degassing) of circular geomorphological structures of the central type.

Geological and geophysical analysis of morphological structures of the central type in the Eastern European platform territory and its connection with hydrocarbon fields

Correlative interrelation of hydrocarbon field arrangement and morphological structures of the central type is revealed. Possible natural mechanisms of morphological structures formation of the central type are considered. By results of geological and geophysical interpretation of magnetic prospecting, gravity prospecting, heat floor data the deep structure of these morphological structures is shown.

Seasonal and Decadal Variability of Atmosphere Pressure in Arctic, its Statistical and Temporal Analysis

The paper analyzes the statistical and temporal seasonal and decadal variability of the atmospheric pressure field in the Arctic region of Russia. Schemes for the frequency analysis of probability transitions for characteristics of stochastic-diffusion processes were used as the main research method. On the basis of the given series of 60 years long from 1948 to 2008, such parameters of diffusion processes as the mean (drift process) and variance (diffusion process) were calculated and their maps and time curves were constructed. The seasonal and long-term variability of calculated fields was studied as well as their dependencies on a discretization of the frequency intervals. These characteristics were analyzed and their geophysical interpretation was carried out. In particular, the known cycles of solar activity in 11 and 22 years were revealed. Numerical calculations were performed on the Lomonosov-2 supercomputer of the Lomonosov Moscow State University.

Complex rotated CCA: a method to correlate lagged geophysical variables

<p>The nature of the climate system is very complex: a network of mutual interactions between ocean and atmosphere lead to a multitude of overlapping geophysical processes. As a consequence, the same process has often a signature on different climate variables but with spatial and temporal shifts. Orthogonal decompositions, such as Canonical Correlation Analysis (CCA), of geophysical data fields allow to filter out common dominant patterns between two different variables by maximizing cross-correlation. In general, however, CCA suffers from (i) the orthogonality constraint, which tends to produce unphysical patterns, and (ii) the use of direct correlations, which leads to signals that are merely shifted in time being considered as distinct patterns.</p><p>In this work, we propose an extension of CCA, complex rotated CCA (crCCA), to address both limitations. First, we generate complex signals by using the Hilbert transforms. To reduce the spatial leakage inherent in Hilbert transforms, we extend the time series using the Theta model, thus creating an anti-leakage buffer space. We then perform the orthogonal decomposition in complex space, allowing us to detect out-of-phase signals. Subsequent Varimax rotation removes the orthogonal constraints to allow more geophysically meaningful modes.</p><p>We applied crCCA to a pair of variables expected to be coupled: Pacific sea surface temperature and continental precipitation. We show that crCCA successfully captures the temporally and spatially complex modes of (i) seasonal cycle, (ii) canonical ENSO, and (iii) ENSO Modoki, in a compact manner that allows an easy geophysical interpretation. The proposed method has the potential to be useful especially, but not limited to, studies on the prediction of continental precipitation by other climate variables. An implementation of the method is readily available as a Python package.</p>

Nonuniqueness and Uniqueness for Inverse Magnetization Problems

<p>Nonuniqueness is a well-known issue with inverse problems involving geophysical potential fields (typically gravitational or magnetic fields). If no additional assumptions are made on the underlying source, only certain harmonic contributions can be reconstructed uniquely from knowledge of the potential. Such harmonic contributions have no intuitive geophysical interpretation. However, in various applications some specific properties are of particular interest: e.g., the direction of the magnetization in paleomagnetic studies or the lithospheric susceptibility in geomagnetism. In this presentation, we give a brief overview on the characterization of nonuniqueness and on a priori assumptions on the underlying magnetization that might lead to uniqueness or at least partial uniqueness.</p>

Multiphysics anomaly map: A new data fusion workflow for geophysical interpretation

When collecting and processing geophysical data for exploration, the same geologic feature can generate a different response for each rock property being targeted. Typically, the units of these responses may differ by several orders of magnitude; therefore, the combination of geophysical data in integrated interpretation is not a straightforward process and cannot be performed by visual inspection only. The multiphysics anomaly map (MAM) that we have developed is a data fusion solution that consists of a spatial representation of the correlation between anomalies detected with different geophysical methods. In the MAM, we mathematically process geophysical data such as seismic attributes, gravity, magnetic, and resistivity before combining them in a single map. In each data set, anomalous regions of interest, which are problem-dependent, are selected by the interpreter. Selected anomalies are highlighted through the use of a logistic function, which is specially designed to clip large magnitudes and rescale the range of values, increasing the discrimination of anomalies. The resulting anomalies, named logistic anomalies, represent regions of large probabilities of target occurrence. This new solution highlights areas where individual interpretations of different geophysical methods correlate, increasing the confidence in the interpretation. We determine the effectiveness of our MAM with application to real data from onshore and offshore Brazil. In the onshore Recôncavo Basin, the MAM allows the interpreter to identify a channel where a drilled well found the largest sandstone thickness on the area. In a second example, from offshore Sergipe-Alagoas Basin, the MAM helps differentiate between a dry and an oil-bearing channel previously outlined in seismic data. Therefore, these outcomes indicate that the MAM is a valid interpretation tool that we believe can be applied to a wide range of geologic problems.