This report compiles hydrologic observations in southern California and elsewhere associated with the 1992 ML = 7.3 Landers, California earthquake sequence.In southern California, the largest ground-water-level changes were a rise of 3 meters at Lucerne Valley and a drop of 5 m at Pinon Flat.Most of the steplike water-level changes recorded in the hours following the Landers and Big Bear earthquakes agreed in direction with the sign of the calculated coseismic volume strain field.In the Pinon Flat area, however, two wells measured on June 28, after both these earthquakes, displayed water-level rises of 9 cm above the reading made two days before.A spring discharge increase in Millard Canyon was reported to have preceded the earthquake by several days.Outside of southern California, water-level changes were also observed, but are not consistent in sign or size with the static strain field of the earthquake sequence.At Parkfield, California, water-level changes took place in three wells at the time of the earthquake, and recovered over periods as long as 30 days.At Long Valley, California, observed water-level changes generally returned to normal after minutes to hours, consistent with their having been caused by the passage of surface waves.Water levels in one well at Long Valley and in a well near Grants Pass, Oregon, remained low for at least two days following the earthquake.Water-level oscillations took place in two wells in eastern Nevada.Phenomena accompanying the Landers earthquake that were of practical significance include the Tapo Canyon oil seep, which polluted part of the Santa Clara River; gas bubbles in San Bernardino city water supply wells, which clogged filters; and a coseismic discharge increase in Millard Creek, which added to the water supply.1. Includes response to Big Bear earthquake.
Abstract We analyze co-seismic changes of water level in nine wells near Parkfield, California, produced by an MD 4.7 earthquake on 20 December 1994 in order to test the hypothesis that co-seismic water-level changes are proportional to co-seismic volumetric strain. For each well, a quantitative relationship between water level and volumetric strain can be inferred from water-level fluctuations due to earth tides and barometric pressure. The observed co-seismic water-level changes, which ranged from −16 to +34 cm, can therefore be compared with volumetric strain recorded by borehole strainmeters or calculated using a dislocation model of the earthquake. We were able to find a dislocation model of the earthquake rupture that predicts volumetric expansion at five of the six wells where water level fell co-seismically, as well as volumetric contraction at one of the two sites where water level rose. Strain predicted by the dislocation model is in good quantitative agreement with the strain inferred from water-level changes observed at four of the well sites, as well as strain recorded by three borehole strainmeters. Water-level changes at two more well sites correspond to strain somewhat greater than predicted by the model but agree in sign with model-calculated strains. At three of the well sites, however, water-level changes took place that cannot be explained as responses to co-seismic volumetric strain for any plausible dislocation model of the earthquake rupture. At two of these sites, one in and one near the San Andreas fault, large water-level drops are probably influenced by co-seismic fault creep. The third site has a history of large water-level rises in response to earthquakes at distances up to several hundred kilometers. This data set shows that co-seismic water-level changes in many wells are proportional to volumetric strain but that other wells exist in which different mechanisms dominate co-seismic response.
This report compiles hydrologic observations in southern California associated with the 1994 Mw = 6.7 Northridge, California earthquake. In southern California, the largest ground water level change was a drop of 52 cm at Crystalaire. Most of the steplike water-level changes recorded following the Northridge earthquake agreed in direction with the sign of the calculated coseismic volume strain field. A spring discharge increase at Southern Pacific Springs was reported to have preceded the earthquake by several days. Outside of southern California, water-level changes were also observed, but are not consistent in sign or size with the static strain field of the earthquake sequence.
A variety of instruments (including borehole strainmeters, water wells, creepmeters, laser ranging and differential magnetometers) are used to monitor crustal deformation in areas that are prone to geologic hazards such as earthquakes and volcanic eruptions. In monitoring the deformation, one typically examines the data for either a change in rate, or a simple offset in the record. However, one needs to place a statistical confidence level that the detected signal differs from the background “noise”. Calculation of the statistical confidence level may be done using the formalism of the matched filter , whose output is the signal‐to‐noise ratio, ρ. Two ingredients are needed to form a matched filter: 1) The power density spectrum of the instrument and 2) the functional form of the signal that we desire to detect. Using the available crustal deformation data from the Parkfield, California network, the background noise for individual instruments as a function of frequency, f, is estimated using the traditional method of the power density spectra. Except for two‐color laser distance‐ranging data, the power spectra for most of the instruments have a frequency dependence of f −n where 2≤n≤3. The confidence level with which a hypothesized signal is present is determined directly from the signal‐to‐noise ratio, with the numerator being a function of the signal and the denominator being a function of the power spectrum. Using a creepmeter as an example, a 0.04‐mm change occurring over 1 hour, a 0.06‐mm occurring over 10 hours, or 0.20‐mm over 100 hours are all signals for which ρ=2 and therefore have only a 5% confidence that these signals could be background noise.
The distribution of deformation within the Basin and Range province was determined from 1992, 1996, and 1998 surveys of a dense, 800-kilometer-aperture, Global Positioning System network. Internal deformation generally follows the pattern of Holocene fault distribution and is concentrated near the western extremity of the province, with lesser amounts focused near the eastern boundary. Little net deformation occurs across the central 500 kilometers of the network in western Utah and eastern Nevada. Concentration of deformation adjacent to the rigid Sierra Nevada block indicates that external plate-driving forces play an important role in driving deformation, modulating the extensional stress field generated by internal buoyancy forces that are due to lateral density gradients and topography near the province boundaries.
The removal from water level data fluctuations due to barometric forcing at subtidal frequencies is studied using transfer functions. In a poorly confined well‐aquifer system these transfer functions are, in general, dependent upon air diffusivity through the unsaturated zone and diffusivity from the water table into the unsaturated zone. The stability in time of estimated transfer functions from selected water well sites near Parkfield, California, is examined, and an average transfer function is computed for each site that demonstrates reasonable stability over time. Two of these transfer functions that show marked frequency dependence in the subtidal frequency band are used to filter water level data in this frequency range. A comparison is made with residuals after subtracting the best fitting multiple of band‐passed barometric pressure from the water level data, and transfer function filtering is shown to be superior. Comparing the estimated transfer functions with previously derived theoretical ones shows that in one case the well‐aquifer system can be reasonably well modeled by assuming that the effects of air and water diffusivity are nonnegligible, while the remaining frequency‐dependent transfer function can be better modeled by assuming minimal air diffusivity through the unsaturated zone.
