Search

Locating seismic events is a central task for earthquake monitoring. Compared to arrival‐based location methods, waveform‐based location methods do not require picking phase arrivals and are more suitable for locating seismic events with noisy waveforms. Among waveform‐based location methods, one approach is to stack different attributes of P and S waveforms around arrival times corresponding to potential event locations and origin times, and the maximum stacking values are assumed to indicate the correct event location and origin time. In this study, to obtain a high‐resolution location image, we improve the waveform‐based location method by applying a hybrid multiplicative imaging condition to characteristic functions of seismic waveforms. In our new stacking method, stations are divided into groups; characteristic functions of seismic waveforms recorded at stations in the same group are summed, and then multiplied among groups. We find that this approach can largely eliminate the cumulative effects of noise in the summation process and thus improve the resolution of location images. We test the new method and compare it to three other stacking methods, using both synthetic and real datasets that are related to induced seismicity occurring in petroleum/gas production. The test results confirm that the new stacking method can provide higher‐resolution location images than those derived from currently used methods. A new waveform migration location method is developed by applying the hybrid multiplicative imaging condition.The new location method gives more accurate locations than conventional waveform location method.The new location method is successfully applied to induced seismicity in an oilfield in Oman and a shale gas field in southern Sichuan basin, China. A new waveform migration location method is developed by applying the hybrid multiplicative imaging condition. The new location method gives more accurate locations than conventional waveform location method. The new location method is successfully applied to induced seismicity in an oilfield in Oman and a shale gas field in southern Sichuan basin, China.

With the development of unconventional shale gas in the southern Sichuan Basin, seismicity in the region has increased significantly in recent years. Though the existing sparse regional seismic stations can capture most earthquakes with ML≥2.5, a great number of smaller earthquakes are often omitted due to limited detection capacity. With the advent of portable seismic nodes, many dense arrays for monitoring seismicity in the unconventional oil and gas fields have been deployed, and the magnitudes of those earthquakes are key to understand the local fault reactivation and seismic potentials. However, the current national standard for determining the local magnitudes was not specifically designed for monitoring stations in close proximity, utilizing a calibration function with a minimal resolution of 5 km in the epicentral distance. That is, the current national standard tends to overestimate the local magnitudes for stations within short epicentral distances, and can result in discrepancies for dense arrays. In this study, we propose a new local magnitude formula which corrects the overestimated magnitudes for shorter distances, yielding accurate event magnitudes for small earthquakes in the Changning−Zhaotong shale gas field in the southern Sichuan Basin, monitored by dense seismic arrays in close proximity. The formula is used to determine the local magnitudes of 7,500 events monitored by a two‐phased dense array with several hundred 5 Hz 3C nodes deployed from the end of February 2019 to early May 2019 in the Changning−Zhaotong shale gas field. The magnitude of completeness (MC) using the dense array is −0.1, compared to MC1.1 by the sparser Chinese Seismic Network (CSN). In addition, using a machine learning detection and picking procedure, we successfully identify and process some 14,000 earthquakes from the continuous waveforms, a ten‐fold increase over the catalog recorded by CSN for the same period, and the MCis further reduced to −0.3 from −0.1 compared to the catalog obtained via manual processing using the same dense array. The proposed local magnitude formula can be adopted for calculating accurate local magnitudes of future earthquakes using dense arrays in the shale gas fields of the Sichuan Basin. This will help to better characterize the local seismic risks and potentials. The local magnitudes of microseismic events monitored by a dense seismic array in close proximity are overestimated by the current national standard.The new formula yields accurate magnitudes for small earthquakes in the Changning–Zhaotong shale gas field monitored by dense seismic arrays in close proximity.The magnitude of completeness for small earthquakes monitored by a local dense seismic array can be as low as ML−0.3 via machine‐learning based processing. The local magnitudes of microseismic events monitored by a dense seismic array in close proximity are overestimated by the current national standard. The new formula yields accurate magnitudes for small earthquakes in the Changning–Zhaotong shale gas field monitored by dense seismic arrays in close proximity. The magnitude of completeness for small earthquakes monitored by a local dense seismic array can be as low as ML−0.3 via machine‐learning based processing.