Peculiarities of slick formation on the sea surface

This paper deals with surface slicks, their nature, and causes of formation in context of the development of remote detection methods and investigation of dynamic processes in the ocean and at the air–ocean interface. A simplified formalism is introduced for slick–sea surface contrast formation which takes into account the upward sea radiation and qualitatively explains in situ measurement results. The results of a detection of slicks on the coastal water surface are also described, which, together with the simplified formalism suggested, provide optimal experimental conditions for slick–sea surface contrast measurements; i.e., the contrast of the P component of reflected radiation is measured at viewing angles close to the horizon.     

Dependences of statistical characteristics of <Emphasis Type="Italic">NmE</Emphasis> on the month of the year at middle and low latitudes under daytime geomagnetically quiet conditions at low solar activity

Month-to-month changes in the statistical characteristics of the ionospheric E layer peak electron density NmE at medium and low geomagnetic latitudes under daytime geomagnetically quiet conditions are investigated. Critical frequencies of the ionospheric E layer measured by the middle latitude ionosonde Boulder and low latitude ionosondes Huancayo and Jicamarca at low solar activity from 1957 to 2015 have been used in the conducted statistical analysis. The mathematical expectation of NmE, standard deviation of NmE from the expectation of NmE, and NmE variation coefficient have been calculated for each month of the year. The months of the formation of extrema of these statistical parameters of NmE were found.     

Fifty-year variations in water ionic composition in small tributaries of the Southern Baikal

Concentrations of major ions in surface waters of the rivers of Khara-Murin and Snezhnaya are compared based on data of many-year observations carried out in the 1950s and 2000s. The concentrations of HCO ₃ ^? , Cl^?, Ca²⁺, Mg²⁺, Na⁺ + K⁺ are shown to be stable. A considerable increase in SO ₄ ^?2 concentration was revealed.     

Variations in statistical parameters of the <Emphasis Type="Italic">NmF</Emphasis>2 winter anomaly with latitude and solar activity

The maximal R ratios of the winter-to-summer NmF2 values of each ionosonde are calculated for a specified UT under daytime quiet geomagnetic conditions and at approximately equal levels of solar activity, based on foF2 measurement data of 98 ionosondes at mid- and low geomagnetic latitudes of the Northern and Southern hemispheres for 1957–2009. The P(R > 1) conditional probability of NmF2 winter anomaly observations, as well as the most probable RMP and average <R> of R values are calculated for low, moderate, and high solar activity on the base of foF2 measurements during the periods December 22 ± 30 days and June 21 ± 30 days. Variations in P(R > 1), RMP, and 〈R〉 with latitude and solar activity are analyzed.     

On the evaluation of displacement and deformation in tidal waves according to digital records of STS-2 and KSESh-R seismometers

A procedure for eliciting displacements in tidal waves from seismograms of broadband velocimeters like STS-2 and KSESH-R and the evaluation conversion of these displacements into vertical or volumetric deformation is proposed.     

A probabilistic model of seismicity: Kamchatka earthquakes

The catalog of Kamchatka earthquakes is represented as a probability space of three objects {Ω, $ \tilde F $ \tilde F P}. Each earthquake is treated as an outcome ω _i in the space of elementary events Ω whose cardinality for the period under consideration is given by the number of events. In turn, ω _i is characterized by a system of random variables, viz., energy class k_i, latitude φ _i , longitude λ _i , and depth h _i . The time of an outcome has been eliminated from this system in this study. The random variables make up subsets in the set $ \tilde F $ \tilde F and are defined by multivariate distributions, either by the distribution function $ \tilde F $ \tilde F (φ, λ, h, k) or by the probability density f(φ, λ, h, k) based on the earthquake catalog in hand. The probabilities P are treated in the frequency interpretation. Taking the example of a recurrence relation (RR) written down in the form of a power law for probability density f(k), where the initial value of the distribution function f(k ₀) is the basic data [Bogdanov, 2006] rather than the seismic activity A ₀, we proceed to show that for different intervals of coordinates and time the distribution f _elim(k) of an earthquake catalog with the aftershocks eliminated is identical to the distribution f _full(k), which corresponds to the full catalog. It follows from our calculations that f ₀(k) takes on nearly identical numeral values for different initial values of energy class k ₀ (8 ≤ k ₀ ≤ 12) f(k ₀). The difference decreases with an increasing number of events. We put forward the hypothesis that the values of f(k ₀) tend to cluster around the value 2/3 as the number of events increases. The Kolmogorov test is used to test the hypothesis that statistical recurrence laws are consistent with the analytical form of the probabilistic RR based on a distribution function with the initial value f(k ₀) = 2/3. We discuss statistical distributions of earthquake hypocenters over depth and the epicenters over various areas for several periods     

Estimating the source parameters for the M<Subscript><Emphasis Type="Italic">W</Emphasis></Subscript> 7.6 April 20, 2006 Olyutorskii earthquake from long period <Emphasis Type="Italic">P</Emphasis>-wave records made by the worldwide network

The source parameters of the M _W = 7.6 Olyutorskii earthquake were estimated using the moments of the slip rate function with degrees 1 and 2. The moments were estimated from broadband P-wave records at 52 stations of the worldwide network. The first step was to find a function S(t) for each station; this function is an apparent source time function, i.e., the P-wave slip as radiated by the source toward a station under consideration. The method of empirical Green’s functions was used to estimate S(t). The next step was to calculate the moments of S(t) of degrees 1 and 2 over time and to set up relevant equations to be solved by least squares for the unknown source moments. The horizontal linear source was used as a nonparametric model for calculating the source moments. Haskell’s parametric model was used for further interpretation of the source moments. The resulting estimates are as follows: the source centroid was 13–25 km southwest of the epicenter, the source was 105–120 km long, the source strike was 222°–228°, the rupture velocity was 2.7–3.0 km/s, and the total radiation duration was 24–27 s. These estimates indicate a bilateral rupture dominated by a southwestward sense of rupture propagation. The source characteristics are consistent with the aftershock area geometry and with the focal mechanism, as well as with surface breakage as observed by geologists in the field.     

Short intense bursts in magmatic activity in the south of Siberian Platform (Angara-Taseeva depression): the paleomagnetic evidence

Based on the paleomagnetic study of intrusive and explosive Permian-Triassic traps in the Angara River basin, Siberian Platform, it is established that the formation of the traps was marked by three short and highly intense bursts in magmatic activity, which resulted in the intrusion of three large dolerite sills (Tolstomysovsky, Padunsky and Tulunsky) and the deposition of the tuffs of the Kapaevsky Formation. These magmatic bursts occurred against the long-lived less intense background magmatism, which caused the formation of small intrusive bodies and tuff sequences. The geochronological data and correlation of the Angara traps to the effusive trap sequences in the north of the Siberian Platform (Norilsk and Maymecha-Kotuy regions) indicate that intrusion of the Tolstomysovsky sill and eruption of its comagmatic tuffs of the Kapaevsky Formation occurred in the Early Triassic. The obtained paleomagnetic data contradict the existing idea that the Padunsky and Tulunsky sills are coeval. Moreover, these data show that the magmatic bodies of different ages were mistakenly referred to the same sill.