Automatic detection and location of microseismic events Tomas Fischer Automatic detections and location of microseismic events Approaches: (1) first detection, phase picking, then location, testing different combinations, (2) Simultaneous (source scanning algorithm, backward propagation ad(1) blind picking

Outline Why automatic How automatic Errors West Bohemia swarm 2000 Hydraulic stimulation in gas field in Texas

Why automatic processing? Huge datasets Improve productivity Improve data homogeneity Real time processing – alarms

Utilization of automatic processing Measurement of arrival times Measurement of amplitudes Phase-waveform extraction Hypocentre location Source parameters, focal mechanisms Seismic tomography Attenuation studies …

Approaches Classical - stepwise: (single station / network) 1. Phase detection & picking 2. Hypocentre location Simultaneous (seismic network) – source scanning / back-propagation (Kao & Shan, 2003; Drew 2005)

Classical approach – steps Phase detection – increased signal energy, single station Phase association – consistency betw. stations Phase picking – identify phase onset Location of hypocenters

Phase detection Transform 3C seismogram to a scalar > 0, characteristic function CF (Allen, 1978) Find maxima of CF S-wave energy detector E N Z l– maximum eigenvalue of signal covariance matrix in a running window

Distinguishing P and S-waves Hierarchic approach First find S-waves (higher amplitude, horiz. polarization) Then find P-waves (perpendicular polarization)

Distinguishing P and S-waves Equal approach evaluate horiz. & vert. polarization find consecutive intervals of perpendicular polarization (ampl. ratio or hor/vert gives hint to which one is P and S)

Phase association Simple kinematic (geometric) criteria e.g. t2 < t1+t12 A-priori information on source position - plane wave consistency Preliminary location - test the phase consistency by location residual 1 2 Source

Phase picking Find onset – abrupt amplitude increase STA/LTA (non-overlapping) Higher statistic moments Kurtosis Waveform cross- correlation Horiz. Polarization Kresleno pomoci AutoPickN1… STA/LTA Kurtosis

Automatic location No special needs (each location algorithm is automatic) Hydrocarbon reservoir stimulations – linear array of receivers – besides arrival times also backazimuth (polarization) needed => modify the location algorithm to include also the fit to the polarization data

Event location 2D array (Earth surface) – P-waves sufficient (S-waves beneficial) t3-t4 1 t1-t2 t2-t3 2 3 4

Event location 1D array (borehole) both P and S-waves needed 1 Map view depth Depth view 1 2 3 4 5 t1>t2>t3=t4<t5

Goodness Picking success Location success Amplitude ratio @ pick Location residual Location success Sharpness of foci image ? ! Location residual – results from Unknown structure Timing errors Picking errors (Gaussian & gross) => Residual is not a unique measure of picking success

Location residual calibration (remove gross errors) Training dataset – if manual processing available Loc. error: difference between manual and automatic locations 6 samples

Location residual calibration (remove gross errors) Dataset to be processed Limit for choice of good locations

Swarm 2000 in West Bohemia 4 SP stations 0-20 km epicentral distance synchronous triggered recording

Swarm 2000 Automatic processing Characteristic function S: maximum eigenvalue of the covariance matrix in horizontal plane (Magotra et al., 1987) P: sum of the Z-comp. and its derivative (Allen, 1978) Method S-waves, minimum interval>maximum expected tS-tP P-waves in a fixed time window prior to S Only complete P and S pairs processed => homogeneous dataset

Swarm 2000 in West Bohemia Resulting automatic picks

Swarm 2000 in West Bohemia >7000 detected events, 4500 well located Homogeneous catalog downto ML=0.4 Location error: ±100 m horiz. and ±200 m vert.

Automatic locations with RMS<8 smpl Automatic locations with RMS<8 smpl. compared with 405 manually located events

Automatic locations of the 2000 swarm 1 2 3 4 5 6+7 8 9 P1 a P2

Hydraulic stimulation in gas field

Hydraulic stimulation in gas field 8 3C geophones continuous recording

Hydraulic stimulation in gas field S-wave picker Get the maximum eigenvalue l(t) of the signal covariance matrix Find maxima of polarized energy arriving at consistent delays tj to vertical array (derived from expected slowness) Identify the S-wave onsets tS by STA/LTA detector in a short time window preceding the maxima of L(t) Measure S-wave backazimuth Array compatibility check by fitting hodochrone tS(z) by parabola, outliers repicked or removed

Hydraulic stimulation in gas field P-wave picker Search for signal s polarized in S-ray direction p. We use the characteristic function Find maxima of P-wave polarized energy Cp(t) arriving at consistent slowness (similar as in S-wave detection) Identify the P-wave onsets tP by STA/LTA detector in a short time window preceding the maxima of Cp(t) Measure the P-wave backazimuth Use Wadati’s relation to remove tP outliers

Hydraulic stimulation in gas field

Hydraulic stimulation in gas field

Hydraulic stimulation in gas field

Hydraulic stimulation in gas field

Hydraulic stimulation in gas field Comparison of manual and auto picks for 296 manually picked events Fig. 3. Distribution of time differences between automatically and manualy obtained arrival times of test dataset. P S

Comparison of manual and auto locations

Conclusions automatic processing useful in case of huge datasets & provides homogeneous results two approaches classic – mimics human interpreter modern – direct search for the hypocentre classic – network consistency beneficial two case studies show successful implementation of polarization based picker

Outlines use waveform cross-correlation for picking