SAIPy: A Python Package for single station Earthquake Monitoring using Deep Learning

Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical properties from extensive datasets, surpassing the capabilities of traditional approaches to an extent. In this study, we present SAIPy, an open source Python package specifically developed for fast data processing by implementing deep learning. SAIPy offers solutions for multiple seismological tasks, including earthquake detection, magnitude estimation, seismic phase picking, and polarity identification. We introduce upgraded versions of previously published models such as CREIMERT capable of identifying earthquakes with an accuracy above 99.8 percent and a root mean squared error of 0.38 unit in magnitude estimation. These upgraded models outperform state of the art approaches like the Vision Transformer network. SAIPy provides an API that simplifies the integration of these advanced models, including CREIMERT, DynaPickerv2, and PolarCAP, along with benchmark datasets. The package has the potential to be used for real time earthquake monitoring to enable timely actions to mitigate the impact of seismic events. Ongoing development efforts aim to enhance the performance of SAIPy and incorporate additional features that enhance exploration efforts, and it also would be interesting to approach the retraining of the whole package as a multi-task learning problem

Exploring a CNN model for earthquake magnitude estimation using HR-GNSS data

Highlights • We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. • The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. • The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33. Abstract High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements

PolarCAP – A deep learning approach for first motion polarity classification of earthquake waveforms

Highlights • We present PolarCAP, a deep learning model that can classify the polarity of a waveform with a 98% accuracy. • The first-motion polarity of seismograms is a useful parameter, but its manual determination can be laborious and imprecise. • We demonstrate that in several cases the model can assign trace polar-ity more accurately than a human analyst. Abstract The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earth-quake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on 22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, we check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities

Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan

Numerous microearthquakes, ML ≤ 3.8, corresponding to background seismicity and swarms were observed from September 3, 2017, to January 1, 2018, mainly in the Tesistán Valley, north of the Guadalajara Metropolitan Zone (GMZ). We located 188 tectonic microearthquakes and identified 11 clusters of similar events from a spatio-temporal analysis and waveform cross-correlations. Our results confirm the presence of continuous seismicity in the GMZ that long went unobserved. Most M L ≥ 2.5 events and some clustered events are located in the northeastern Tesistán, close to the NNE-SSW fault corresponding to the eastern edge of the Zapopan Graben, a structure evidenced by 2015–2016 seismicity. Seismicity recorded during 2020 by a recent local seismic network installed in Zapopan reaffirms that frequent microseismicity is related to active faults that cross the cities of Zapopan and Guadalajara. The microseismicity distribution suggests minor faults with the same orientation and sense of displacement as the main structures bounding the Zapopan Graben, which corresponds to structures known as synthetic faults. This arrangement is common within the Basin and Range tectonic province. The seismicity in the northeast boundary of Jalisco Block is closely related to faults formed by Cenozoic deformation events that might be reactivated due to modern crustal dynamics. Active faults and the possibility of synthetic structures are a hypothesis that necessitates long-term seismic monitoring in order to assess the seismic hazard in the GMZ, which is a crucial factor for urban planning.Secretaría de Energía Mexico-Consejo Nacional de Ciencia y TecnologíaConsejo Nacional de Ciencia y Tecnologia (CONACyT)Academic Research GroupCivil Defense of Zapopan, JaliscoDepto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEpu

The May 2016 Mw 5.6 Earthquake at Rivera Fault Zone

American Geophysical Union (AGU) Fall Meeting, 9-13 December 2019, San FranciscoOn May 7, an earthquake with MW = 5.6 took place in the contact area of the Rivera Plate, Cocos Plate and the Middle America Trench, subsequently occurred a seismic sequence with over 200 earthquakes until May 16. This seismic sequence was studied from the data collected during the TsuJal project. Between April and November 2016, seismic stations were recording onshore and offshore the passive seismicity in the area of Rivera Plate and Jalisco Block. The temporary seismic network (TSN) consisted of 25 Obsidian stations with a LE-3D MkIII sensor from the northern part of the state of Nayarit to the southern part of the state of Colima, including the Islas Marías. This network complemented the Jalisco Seismic Accelerometric Telemetric Network (RESAJ) reaching a total of 50 seismic stations deployed on land. In the offshore region, ten Ocean Bottom Seismometers (OBS) type LC2000SP with three short-term seismic and one pressure sensors were deployed by BO El Puma (UNAM) from the Islas Marías to the border between the states of Colima and Michoacán. The USGS located this seismic sequence north of the Rivera Fault Zone (RFZ) near the Mesoamerican Trench, while the locations obtained from the OBS network located it about 50 km south between the RFZ and the East Pacific Rise. Besides, the USGS reported a strike-slip fault focal mechanism, while our studies indicate a focal mechanism of thrust failure. In this work, an analysis of this sequence with 87 earthquakes occurred between May 7 and 11, 2016, recorded by three local seismic networks (RESAJ, TSN, and OBS) is present

The TsuJal Amphibious Seismic Network: a Passive-Source seismic Experiment in Western Mexico

American Geophysical Union (AGU) Fall Meeting, 13-17 December 2021, New OrleansThe geodynamic complexity in the interaction between Rivera, Cocos, and North American plates, is mainly evidenced by a high seismicity rate that unfavorably is not well located. In the framework of the TsuJal Project, a study of the passive seismic activity was carried out in this region, using data with a temporal seismic network with 25 Obsidian stations, deployed along the states of Nayarit, Jalisco and Colima, including the Islas Marías, in addition to the Jalisco Seismic Network (RESAJ), and 10 OBS type LCHEAPO 2000 with four channels, deployed and recovered by the BO El Puma in an array from the Islas Marías to off coast of the border of Colima and Michoacan states, in the period from 19th April to 7th November 2016. These networks allowed registering more than 2000 oceanic and continental earthquakes in the region, and will permit us to improve the quality of locations and design procedures for routine RESAJ offshore seismicity location. During the observation period, a seismic sequence occurred in the area between the Mesoamerican Trench and the Paleo-Rivera Transform Fault from May 7 to July 13 that included 22 earthquakes with a magnitude greater than 4.0, and one of magnitude 6.3 on June 7. The relocation of this seismic sequence offers a new vision on local tectonicsPeer reviewe

Memorias IX Congreso Geológico Venezolano (4)

Memorias IX Congreso Geológico Venezolano (4