earthdistance query performance

Hi,

I have a table with zip_code and latitude and longitude.

\d zip_code_based_lng_lat
Table "public.zip_code_based_lng_lat"
Column | Type | Modifiers
--------+------------------------+-----------
zip | character varying(100) |
state | character varying(100) |
city | character varying(100) |
type | character varying(100) |
lat | character varying(100) |
lng | character varying(100) |
Indexes:
"zip_code_based_lng_lat_zipidx" btree (zip)

I need to find the closest distance using the radius formula using a
zip_code provided by user.

I build the query like:

select *,
earth_distance(q2_c1, q1.c1) as d
from
(
select *, ll_to_earth(lat::float,lng::float) as c1 from
zip_code_based_lng_lat
) as q1,
(
select ll_to_earth(lat::float,lng::float) q2_c1 from zip_code_based_lng_lat
where zip='18938'
) as q2
order by d
limit 10

Limit (cost=216010.21..216010.24 rows=10 width=55) (actual
time=38296.185..38296.191 rows=10 loops=1)
-> Sort (cost=216010.21..216415.74 rows=162212 width=55) (actual
time=38296.182..38296.182 rows=10 loops=1)
Sort Key:
(sec_to_gc(cube_distance((ll_to_earth((public.zip_code_based_lng_lat.lat)::double
precision, (public.zip_code_based_lng_lat.lng)::double precision))::cube,
(ll
_to_earth((public.zip_code_based_lng_lat.lat)::double precision,
(public.zip_code_based_lng_lat.lng)::double precision))::cube)))
Sort Method: top-N heapsort Memory: 27kB
-> Nested Loop (cost=0.00..212504.87 rows=162212 width=55)
(actual time=3.244..38052.444 rows=81106 loops=1)
-> Seq Scan on zip_code_based_lng_lat (cost=0.00..817.90
rows=81106 width=38) (actual time=0.025..50.669 rows=81106 loops=1)
-> Materialize (cost=0.00..0.32 rows=2 width=17) (actual
time=0.000..0.001 rows=1 loops=81106)
-> Index Scan using zip_code_based_lng_lat_zipidx on
zip_code_based_lng_lat (cost=0.00..0.31 rows=2 width=17) (actual
time=0.080..0.084 rows=1 loops=1)
Index Cond: ((zip)::text = '18938'::text)
Total runtime: 38296.360 ms

The result is fine. But it is too slow.
I am using Postgresql 9.2 with following parameters:

shared_buffers = 6GB
work_mem = 500 MB
seq_page_cost = 0.01
random_page_cost = 0.01

Any idea to improve it.

Thanks.