Hutubi gas field, the largest gas storage field in China, has been operated on annual injection/extraction cycles since 9 June 2013. We study the seismicity near the gas field from 9 June 2013 to 22 October 2015, a time span that the gas field has experienced three injection periods and two extraction periods, and explore its physical mechanism based on the relationship between seismicity and field operation. We identify 273 events (ML > 1) in the region within 10 km of the gas field, with 97% of those occurring in the first two injection periods, 0.4% in the third injection period, and 1% in the two extraction periods. Seismicity in the first two injection periods occurs mostly as shallow clusters (focal depth < 2 km) at two locations: with one along the fault that marks the southern boundary of the gas field and the other about 2 km south to the southeastern tip of the gas field with the seismicity distributed along north‐south direction. The seismicity does not correlate with total gas injection volume, injection rate, or well pressure. It instead occurs 11–17 hr after simultaneous abrupt increases/decreases of gas injection rate and well pressure in the field operation in the first two injection periods when some accumulative injection has reached. Such relationship is consistent with a physical mechanism that the seismicity near Hutubi gas field is induced on pore‐pressured faults with a rate‐ and state‐dependent friction law through an abrupt change of stress in elastic and undrained poroelastic responses to simultaneous abrupt changes of injection rate and well pressure. Our study also points to the possibility that induced seismicity may be controllable in some practical field operations.

With the aim to the quantification of anomaly identification and extraction for observed or analyzed records， we present two statistical methods of earthquake corresponding relevancy spectrum (ECRS) and sliding mean relevancy (SMR). With ECRS method, we can obtain the abnormal confidence attribute of data in different value ranges. Based on the relevancy spectrum in different studied time-intervals, we convert the original time sequence into relevancy time sequence, and can obtain the SMR time series by using the multi-point cumulative sliding mean method. Then we can identify the seismic precursor anomaly. We test this method by taking the time sequence of η-value in the northern Tianshan region as original data. The result shows that when the studied time-interval is 18 months, the precursor anomaly can be identified better from sliding mean relevancy. The anomaly corresponding rate is 83 percent, the earthquake corresponding rate is 86 percent, and the anomaly is characteristic of the middle term. To try the research on multi-parameter comprehensive application, we take the Kalpin tectonic block in Xinjiang as our studied region, and analyze the spatial and temporal abnormal characters of multi-parameter sliding extreme-value relevancy (MSER) before mid-strong earthquakes in the Kalpin block. The result indicates that ECRS and SMR sequence in different time-intervals can not only be used to identify the precursor anomaly of single-item data, but also offer the data of quantitative single-item anomaly for comprehensive earthquake analysis and prediction.Key words: earthquake corresponding relevancy spectrum(ECRS); sliding mean relevancy (SMR); multi-parameter sliding extreme-value relevancy(MSER); comprehensive precursor anomaly CLC number: P315.72 Document code: A

Discriminating between various types of seismic events is of significant scientific and societal importance. We use a machine learning method employing support vector machine (SVM) to classify tectonic earthquakes (TEs), quarry blasts (QBs), and induced earthquakes (IEs) among 30,181 1.5 < M L <2.9 seismic events that occurred in the Tianshan orogenic belt in China from 2009 to 2017. SVM classifiers are derived based on discriminant features of a training data set consisting of 1,400 TEs selected from the aftershock sequences of 18 M L ≥ 5.0 earthquakes, 2,881 QBs from repeating events occurring in those areas with a percentage of event daytime occurrence greater than 0.9, and 987 IEs from events in the known oil/gas fields and water reservoirs. The discriminant features include spectral amplitudes of observed P and S wave signals in a frequency range of 1-15 Hz normalized by the P spectrum and averaged over the entire seismic network, and an optional feature of the percentage of event daytime occurrence. Statistics analyses indicate that the accuracies of the SVM classifiers are 99.81% for TEs, 99.93% for QBs, and 99.62% for IEs. Our classification indicates that 37.57% of the seismic events are QBs occurring in possible mine areas and appearing mostly as clusters with a percentage of event daytime occurrence greater than 0.9, 50.12% are TEs occurring in various thrust faults in the Tianshan orogenic belt, and 12.31% are IEs or shallow tectonic earthquakes occurring mostly as clusters near oil and gas fields and water reservoirs. We reevaluate b values in the region and obtain relatively uniform values for the classified TEs with most of them below 1.0, as opposed to a large range of values (0.5-2.7) when all the seismic events are used in the analysis. Key Points:• We establish support vector machine (SVM) classifiers to classify tectonic earthquakes, quarry blasts, and induced earthquakes in the Tianshan orogenic belt • Statistics analyses indicated that the accuracies of the SVM classifiers are more than 99% • We classify the events occurring from 2009 to 2017 in the Tianshan orogenic belt and reevaluate the b values of the tectonic earthquakes