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Inversion model combination for microseismic source positioning with Multi-Objective Grasshopper Optimization Algorithm
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DOI：10.36922/JSE025420089.png)
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Abstract
En 中文
The precise determination of microseismic source locations is one of the core components of theoretical research in microseismic monitoring technology. Multi-objective intelligent optimization is an effective approach for microseismic source positioning, but it faces challenges such as unclear rationality of model combinations, susceptibility to local optima, and significant variability in positioning results. To address these issues, four distinct mathematical models for microseismic source positioning were designed based on the arrival time difference model and the arrival time difference quotient model. These models were then combined in pairs to form six different microseismic source positioning model combinations, which were used as the optimization objective functions for the multi-objective computational algorithm. A set of microseismic source forward modeling experiments based on three-dimensional polyhedral array shapes, two sets of engineering microseismic data validation experiments,and one set of multi-objective computational method comparison experiments were designed. the multi-objective grasshopper optimization algorithm (MOGOA) was introduced to solve the six model combinations and employed in four sets of microseismic source positioning experiments. Multiple statistical metrics were applied to evaluate the performance of each model combination. The experimental results indicate that the microseismic inversion mathematical model combination (TDA, TDA-P1), combined with the MOGOA algorithm's multi-objective optimization positioning strategy, can achieve high microseismic source positioning accuracy under relatively reliable microseismic event data, and the model calculations are relatively robust. Under microseismic blasting data, the average positioning error over 100 rounds reached 27.6035 m, with standard deviation and interquartile range averages of only 3.2114 m and 5.5896 m, respectively, outperforming other inversion model combinations and similar multi-objective positioning methods. For microseismic event data with significant systematic errors, the microseismic inversion mathematical model combination (TDA-P1,TDQA-P1) demonstrates superior positioning performance, with an average positioning error of 151.1915 mover 100 iterations, significantly outperforming other model combinations. These model combination positioning performance studies hold practical application value in the field of microseismic monitoring.
Keywords:
Microseismic source positioning
Multi-objective optimization
Combination of inversion mathematical models
Time difference quotient of arrival
Time difference of arrival
Multi-Objective Grasshopper Optimization Algorithm
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J
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0.4
Papers:
44
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