Work

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

Year: 2018

Type: article

Abstract: < 1.5 from the Geysers geothermal field (CA) yields groupings that have no reservoir-scale spatial patterns but clear temporal patterns. Events with similar spectral properties repeat on annual cycles within each cluster and track changes in the water injection rates into the Geysers reservoir, indicating that changes in acoustic properties and faulting processes accompany changes in thermomechanical state. The methods open new means to identify and characterize subtle changes in seismic source properties, with applications to tectonic and geothermal seismicity.

Source: Science Advances

DOI: 10.1126/sciadv.aao2929

Authors: Benjamin K. Holtzman, Arthur Paté, John Paisley, Felix Waldhauser, Douglas Repetto

Institutions: Lamont-Doherty Earth Observatory, Columbia University

Language: English

Topic: earthquake and tectonic studies

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,500

Funder: Columbia University