A Machine Learning-enhanced Robust P-Phase Picker for Real-time Seismic Monitoring
Identifying the arrival times of seismic P-phases plays a significant role in real-time seismic monitoring, which provides critical guidance for emergency response activities. While considerable research has been conducted on this topic, efficiently capturing the arrival times of seismic P-phases hidden within intensively distributed and noisy seismic waves, such as those generated by the aftershocks of destructive earthquakes, remains a real challenge since most common existing methods in seismology rely on laborious expert supervision. To this end, in this paper, we present a machine learning-enhanced framework based on ensemble learning strategy, EL-Picker, for the automatic identification of seismic P-phase arrivals on continuous and massive waveforms. More specifically, EL-Picker consists of three modules, namely, Trigger, Classifier, and Refiner, and an ensemble learning strategy is exploited to integrate several machine learning classifiers. An evaluation of the aftershocks following the MS 8.0 Wenchuan earthquake demonstrates that EL-Picker can not only achieve the best identification performance but also identify 120% more seismic P-phase arrivals as complementary data. Meanwhile, experimental results also reveal both the applicability of different machine learning models for waveforms collected from different seismic stations and the regularities of seismic P-phase arrivals that might be neglected during manual inspection. These findings clearly validate the effectiveness, efficiency, flexibility and stability of EL-Picker.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningEnsemble LearningSimilar Papers 제목 키워드 기반
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning
In this paper we present a novel application of detecting fruit picker activities based on time series data generated from wearable sensors. During harvesting, fruit pickers pick fruit into wearable bags and empty these …
Activity RecognitionHuman Activity RecognitionTime SeriesEarthquake Phase Association with Graph Neural Networks
Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective association must determine the number of discrete events, their location and origin times, and it must differe…
Graph Neural NetworkParameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator
Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pick…
parameter-efficient fine-tuningTransfer LearningData-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting
Manual fruit harvesting is common in agriculture, but the amount of time that pickers spend on nonproductive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for…
Activity RecognitionBreakout-picker: Reducing false positives in deep learning-based borehole breakout characterization from acoustic image logs
Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry azimuths, low acoustic amplitudes, and increased borehole radius. Accu…