Abstract: An induced earthquake evaluation method for hydraulic fracturing activated faults includes obtaining an induced earthquake evaluation unit, cumulative injection equivalent energy, induced earthquake probability of fault type, induced earthquake probability of fault distance, hydraulic fracturing fluid diffusion capacity in fractured formation, new effective fault stress after fracturing, formation fluid pressure change rate and cumulative released equivalent energy. The probability of induced earthquake by hydraulic fracturing can be obtained. The induced earthquake possibility of each evaluation unit or single well under different stages of hydraulic fracturing, different construction injection methods and production conditions can be quantitatively evaluated. The method provides a quantitative result that can provide data support for optimizing hydraulic fracturing and reducing induced earthquake hazards.

Abstract: An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.

Abstract: An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.

Abstract: An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can he modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.

Abstract: An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.