Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method

Abstract

The accurate and automated determination of earthquake locations is still a challenging endeavor. However, such information is critical for monitoring seismic activity and assessing potential hazards in real time. Recently, a convolutional neural network was applied to detect earthquakes from single-station waveforms and approximately map events across several large surface areas. In this study, we locate 194 earthquakes induced during oil and gas operations in Oklahoma, USA, within an error range of approximately 4.9 km on average to the epicenter and 1.0 km to the depth in catalogs with data from 30 network stations by applying the fully convolutional network. The network is trained by 1,013 historic events, and the output is a 3D volume of the event location probability in the Earth. The trained system requires approximately one hundredth of a second to locate an event without the need for any velocity model or human interference.

Real-time determination of earthquake focal mechanism via deep learning

“…Rather than other deep learning applications [7][8][9][10][11][12][13][14][15][16] where numerous earthquakes are tested, the current FMNet is only evaluated on four earthquakes with magnitudes larger than 5.4. This results from the limitation of historical moderate-to-large earthquakes that occurred in the study area.…”

“…When a destructive earthquake occurs, real-time reporting of the earthquake parameters is of crucial importance for immediate destruction assessment and emergency evacuations. Recent efforts have been re ned towards applying AI technologies to estimate the source parameters because of its full automation, high e ciency, and human-like capability [4][5][6] , which has been remarkably demonstrated in numerous seismic processing tasks such as earthquake detection 7,8 , seismic phase picking [9][10][11] , magnitude estimation 12 , and other tasks [13][14][15][16][17] . Besides reporting the three basic parameters of an earthquake (i.e., origin time, location, and magnitude), it is also exceedingly important to derive the source focal mechanism in time to better understand various aspects of the earthquake.…”

Machine learning and fault rupture: a review

“…The model is trained with events from a double-difference relocated catalog. Zhang et al [123] rely on a fully convolutional network to estimate a 3D image of the earthquake location, from data recorded at multiple stations. While location errors are small for earthquakes greater than magnitude 2, smaller events were reported to be more challenging to locate using this approach and associated with larger errors.…”

“…A major effort in earthquake detection and location with ML algorithms has been focused on induced seismicity, either in geothermal fields [160], in mines [92], or in gas [103] and oil [91,100,101,113,123] extraction fields (in particular in Oklahoma). These studies aim at building automatic and more complete catalogs, and were described in Section 1.…”

Characterization of complex fluvio–deltaic deposits in Northeast China using multi-modal machine learning fusion

“…For example, Moreb et al 19 used real-world data from a hospital in Palestine to apply a new framework that combined software engineering and machine learning for predicting health informatics. Another notable example is the assessment of seismic hazards, where the challenging problem of pinpointing small earthquakes (M L < 3.0) was addressed with a convolutional network that distinguished between certain events in a target zone 22 . Extensive experiments have further demonstrated that multimodal learning using visual images and remote sensing data can perform more accurate classification of large-scale bathymetric maps 23 .…”