Locating seismic events is a central task for earthquake monitoring.Compared to arrival-based location methods,waveformbased location methods do not require picking phase arrivals and are more suitable for locating se...Locating seismic events is a central task for earthquake monitoring.Compared to arrival-based location methods,waveformbased 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.展开更多
Low-frequency signals have been widely found in the conventional oil/gas field and volcanic region as well as during hydraulic fracturing of unconventional oil/gas reservoirs.Their generation mechanism has been ascrib...Low-frequency signals have been widely found in the conventional oil/gas field and volcanic region as well as during hydraulic fracturing of unconventional oil/gas reservoirs.Their generation mechanism has been ascribed to the flow of gas/fluid in the fractures,which can induce the Krauklis wave around fractures and can further excite low-frequency seismic body wave signals at diffraction points.Thus,it is theoretically feasible to determine the gas/fluid enrichment areas and migration pathways by locating the low-frequency signals.Here we have utilized a surface dense seismic array deployed above the Sijiazhuang coal mine in Shanxi province to detect and locate such low-frequency signals that are dominant in the frequency range of 1.5–4.0 Hz.Waveform migrationbased location method is employed to locate these signals that have low signal to noise ratios.We further compare the distribution of low-frequency signals and coalbed methane concentrations that are estimated based on ambient noise tomography result with the same seismic array.The spatial consistency between low-frequency signals and coalbed methane enrichment areas suggests that detecting and locating low-frequency signals with a surface seismic array is an efficient way to identify gas enrichment areas and potential gas migration pathways.展开更多
基金supported by National Key R&D Program of China under grant 2018YFC1504102the National Natural Science Foundation of China under grant 41961134001。
文摘Locating seismic events is a central task for earthquake monitoring.Compared to arrival-based location methods,waveformbased 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.
基金supported by National Natural Science Foundation of China under grant 41961134991。
文摘Low-frequency signals have been widely found in the conventional oil/gas field and volcanic region as well as during hydraulic fracturing of unconventional oil/gas reservoirs.Their generation mechanism has been ascribed to the flow of gas/fluid in the fractures,which can induce the Krauklis wave around fractures and can further excite low-frequency seismic body wave signals at diffraction points.Thus,it is theoretically feasible to determine the gas/fluid enrichment areas and migration pathways by locating the low-frequency signals.Here we have utilized a surface dense seismic array deployed above the Sijiazhuang coal mine in Shanxi province to detect and locate such low-frequency signals that are dominant in the frequency range of 1.5–4.0 Hz.Waveform migrationbased location method is employed to locate these signals that have low signal to noise ratios.We further compare the distribution of low-frequency signals and coalbed methane concentrations that are estimated based on ambient noise tomography result with the same seismic array.The spatial consistency between low-frequency signals and coalbed methane enrichment areas suggests that detecting and locating low-frequency signals with a surface seismic array is an efficient way to identify gas enrichment areas and potential gas migration pathways.