Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a 10 second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.

Abstract: An apparatus, a method, and a non-transitory computer readable medium for event detection of passive seismic data are disclosed. The apparatus includes processing circuitry extracts features from the passive seismic data based on a backbone subnetwork of a residual deep neural network. The processing circuitry generates bounding box proposals for a region of interest (ROI) in the passive seismic data based on the extracted features being input to a region proposal network of the residual deep neural network. The processing circuitry classifies the bounding box proposals into two groups. Each bounding box proposal in a first group indicates that a corresponding seismic signal presents in the ROI. Each bounding box proposal in a second group indicates that no seismic signal presents in the ROI. The processing circuitry determines at least one seismic signal in the ROI from the first group of bounding box proposals.