Reliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC-UCV (Universitat Politecnica de Catalunya - Universidad Central de Venezuela), over single-station three-channel signal windows for P-wave earthquake detection and source region estimation in north-central Venezuela. The analysis is performed on a new dataset of handpicked arrivals of P waves from local events, named CARABOBO, built and made public for reproducibility and benchmarking purposes. The CARABOBO dataset consists of three-channel continuous data recorded by the broadband stations of the Venezuelan Foundation for Seismological Research in the region of 9.5°–11.5°N and 67.0°–69.0°W during the time period from April 2018 to April 2019. During this period, 949 earthquakes were recorded in that area, corresponding to earthquakes with magnitudes in the range from Mw 1.1 to 5.2. To estimate the epicentral source region of a detected event, the proposed network employs geographical distribution of the CARABOBO dataset into K clusters as a basis. This geographical partitioning is automatically performed by the k-means algorithm, and the optimality of the K-values for our dataset has been assessed using the elbow (K=5) and silhouette (K=3) methods. For target seismicity, the proposed network achieves 95.27% detection accuracy and 93.36% source region estimation accuracy, when using K=5 geographic clusters. The location accuracy slightly increases to 95.68% in the case of K=3 geographic partitions. The detection capability of this network has also been tested on the OKLAHOMA dataset, which compiles more than 2000 local earthquakes that occurred in this U.S. state. Without any modification, the proposed network yields excellent detection results when trained and evaluated on that dataset (98.21% accuracy; ConvNetQuake, fine-tuned for this dataset, achieves a 97.32% accuracy), corresponding to a totally different geographical region.

As seismic networks continue to spread and monitoring sensors become more efficient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identification, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recent advances are related to the application of Artificial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.

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Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Boconó and La Victoria-San Sebastián faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.