Распознавание образов в задачах оценки сейсмической опасности

The paper is devoted to some aspects of application of pattern recognition algorithms in solving problems of strong earthquake-prone area determination that can be used for seismic hazard assessment. The main principles of the having a long-term history approach to recognition of strong earthquake-prone areas (EPA) in a region under consideration on the basis of its morphostructural zoning scheme applying the algorithms “CORA-3” and “HAMMING” are described. A review of the results obtained in this direction and work on the development of new algorithms based, in particular, on discrete mathematical analysis is given. The use of pattern recognition approaches to develop algorithms for medium-term earthquake prediction that can help to obtain an operative seismic hazard assessment is shown. The application of the Unified Scaling Law for Earthquakes for the earthquake hazard and risk assessment taking into account the EPA results is considered. A review of the EPA and earthquake hazard and risk assessment results for the Caucasus region is presented.Статья посвящена некоторым аспектам применения алгоритмов распознавания образов при решении задач определения мест возможного возникновения сильных землетрясений, что может быть использовано для оценки сейсмической опасности. Приведены основные принципы имеющего многолетнюю историю подхода к распознаванию мест сильных землетрясений (РМСЗ) рассматриваемого региона на базе схемы его морфоструктурного районирования с применением алгоритмов «Кора-3» и «Хемминг». Дан обзор полученных в этом направлении результатов и работ по разработке новых алгоритмов, основанных, в частности, на дискретном математическом анализе. Отмечено использование подходов распознавания образов для создания алгоритмов среднесрочного прогноза землетрясений, с помощью которых может быть получена оперативная оценка сейсмической опасности. Рассмотрено применение Общего закона подобия для землетрясений для оценки сейсмической опасности и рисков с учетом результатов РМСЗ. Приведен обзор результатов РМСЗ и оценки сейсмической опасности и рисков для региона Кавказа

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Knowledge-based seismogram processing by mental images

The impact of pictorial knowledge representation is demonstrated for two examples of time series analysis in seismology. The approaches perform a) automated recognition of known event signatures and b) high-resolution onset timing of later phases. Both methods work well under extreme conditions of noise and achieved human-like performance in recognizing known situations. Crucial for this success of pictorial knowledge representation was the design of suitably scaled images. They must be simple and robust enough to transform the complexity of “real life” data into a limited set of patterns. These patterns differ significantly from the initial data; they correspond more closely to the non-linear weighting of recognized impressions by an experienced scientist. Thus the author addresses the pictorial presentations as mental images. For both reported applications, part of their power comes by model-based image modifications. However, this enhancement is far from demanding a complete theory. Any fractional model already enhances the image adaptation, so mental images are best suited to deal with incomplete knowledge like any other artificial intelligence approach. Cognitive plausibility was found for both the non-linear image scalings and the model-based image modifications. In general, the author's method of pictorial knowledge representation conforms to the concept of mental images by Kosslyn. Any new task will demand the composition of new, dedicated image transformations where some generalized design criteria are derived from the author's applications

Shear-wave and spatial attributes in time-lapse 3-D/3-C seismic and potential-field datasets

In this study, I utilize multicomponent time-lapse seismic datasets for investigating subtle seismic properties of Weyburn reservoir undergoing enhanced oil recovery and geologic sequestration of CO2. The primary focus is on extracting shear-wave information from surface three-dimensional and three-component (3-D/3-C) reflection datasets. Four groups of interrelated objectives are addressed: 1) calibrated and true-amplitude processing of multicomponent time-lapse seismic data, 2) extraction of amplitude variations with angle (AVA) and offset (AVO) attributes for separating pressure and fluid-saturation effects within the reservoir, 3) development of receiver-function methods for investigating the shallow subsurface, and 4) 2-D spatial pattern analysis of attribute maps, intended for automated interpretation of the results and a new type of AVO analysis. To achieve the first of these objectives, I reprocess the field surface 3-C/3-D reflection datasets by using pre-stack waveform calibration followed by complete reflection processing using commercial ProMAX software. For the second, principal objective of this study, several AVA attributes of the reservoir are examined, including those related to P- and P/S- converted waves and P- and S-wave impedances. The amplitudes and AVA attributes derived from seismic data indicate temporal variations potentially caused by pore-pressure and CO2-saturation variations within the reservoir. By comparing with AVA forward models, the seismic data suggest correlations between the increasing pore pressure and decreasing AVA intercepts and increasing AVA gradients. Increasing CO2 saturations appear to correlate with simultaneously decreasing AVA intercepts and gradients. CO2-saturated zones are thus interpreted as Class III AVA anomalies. In order to take further advantage from 3-C recordings and investigate advanced methods for S-wave seismic data analysis, receiver functions are used to study the shallow near-surface structure. This is apparently the first application of this method to reflection seismic datasets on land and in a time-lapse 3-D dataset. I show that it is feasible and useful to measure the near-surface S-wave velocity structure by using multi-component seismic data. From Weyburn reflection data, the average mapped receiver-function time lags are about 35 ms, which corresponds to near-surface S-wave velocities of about 550 m/s. Time-lapse variations of the near-surface structure are measured, and S-wave statics models are derived. Such models can be useful for converted-wave seismic imaging. The last objective of this Dissertation is to develop tools for interpretation of gridded 2-D spatial images, such as mapping AVO attribute quantitatively and automatically. For this purpose, a new pattern-recognition approach called skeletonization is developed and applied to several regional aeromagnetic and gravity images from southern Saskatchewan and Manitoba. The approach is combined with 2-D empirical mode decomposition allowing pattern analysis at variable spatial scales. The results show that skeletonization helps identifying complex geologic structures and measuring their quantitative attributes that are not available from conventional interpretation. Applications of this approach to interpretation of AVO attributes are discussed