Java Island is part of the island arc influenced by subducting Indo-Australian beneath Eurasian tectonic plates, therefore there is high seismic activity and an active volcanic chain trending East-West. One of the volcanoes in Java Island is Mt. Pandan, northern part of Madiun, East Java region, which is known as one of the dormant volcano in the region. According to the list of volcanoes in Indonesia Mt. Pandan is not classified as an active volcano. The previous studies mentioned that Mt. Pandan is a modern volcano which is located in the Kendeng zone. On June 25, 2015, there was felt earthquake (M 4.2) causing several houses damaged around Mt. Pandan as reported by Agency for Meteorology, Climatology, Geophysics (BMKG), Indonesia and then in February 2016, more than twenty small earthquakes (M < 4) occurred again in the area. In order to understand the structure beneath Mt. Pandan, we have conducted gravity measurement and seismicity analysis through hypocenter relocation. Our results show prominent low gravity and density anomalies by forward modeling derived from residual anomaly around Mt. Pandan area. The clusters of small earthquakes appear at depths of less than 30 km beneath Mt. Pandan. The selected focal mechanism of the event in the area is left-lateral faulting in the north and oblique dominant thrust in the south of Mt. Pandan. Some indications related to submagmatic activities such as hot springs and warm ground is found. Our interpretation is this phenomenon may be related to tectonic and magmatic activities. On the other hand, it confirms also that Mt. Pandan is probably a modern volcanic center.
Abstract.As an advanced application of soft computation in the oil and gas industry, genetic algorithms (GA) can contribute to geophysical inversion problems in order to achieve better results and efficiency in the computational process. Time-lapse gravity responses to pore-fluid density changes can be modeled to provide the density distribution in the subsurface. This paper discusses the progress of work in inverse modeling of time-lapse gravity data using value encoding with alphabet formulation. The alphabet formulation was designed to provide the solution for positive and negative density change with respect to a reference value (0 gr/cc). The inversion was computed using a genetic algorithm as the optimization method. Working with genetic algorithms, time-intensive computational processes are a challenge, so the algorithm was designed in steps through the evaluation of a GA operator performance test. The performances of several combinations of GA operators (selection, crossover, mutation, and replacement) were tested with a synthetic model of a single-layer reservoir. Sharp boundaries of density changes in the reservoir layer were derived from interpretation of the averaged calculation of several model samples. Analysis showed that the combination of stochastic universal sample-multipoint crossover-quenched simulated annealing per generation-no duplicity achieved the most promising results.
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