Wozniakowska_Eaton_2020_GRL_Supporting_Files

The datasets include interpolated input values and script can be used to reproduce the study described in the Manuscript published in Geophysical Research Letters entitled:"Machine learning-based analysis of geological susceptibility to induced seismicity in the Montney Formation, Canada."<br>Included files:<br>1. Interpolated grid files: <br>- pressure gradients [pressure_gradients.csv]<br>- Montney thickness [montney_thickess.csv]<br>- Montney Formation tops [montney_tops.csv]<br>- Debolt Formation tops [debolt_tops.csv]<br>- Phanerozoic thickness [phanerozoic_thickness.csv]<br>- local SHmax variance [local_shmax_variance.csv]<br><br>2. Raw data files:<br>- SHmax azimuths from the Western Canada (wsm_montney_abc_quality.csv)- phanerozoic shapefiles (isolines every 1000m) [digitized_phanerozoic_isolines_1000m.zip]- Cordilleran thrust and fold belt shapefile [shape_files_dist_belt.zip]- faults shapefiles (digitized from Furlong et al., 2020) [faults.zip]<br><br>3. Raw AER, NRCan and CASC earthquake catalogues. [EQ_catalogues_to_compile.zip]<br><br>4. Compiled input dataset (WELLS_INPUT.csv)<br>5. Python script with logistic regression algorithm (logistic_regression.py)<br>